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0-6628-P1
THE ECONOMICS OF TRANSPORTATION SYSTEMS: A REFERENCE FOR PRACTITIONERS
Dr. Kara Kockelman T. Donna Chen Dr. Katie Larsen Brice Nichols
TxDOT Project 0-6628: Economic Considerations in Transportation System Development & Operations
JANUARY 2013 Performing Organization: Center for Transportation Research The University of Texas at Austin 1616 Guadalupe, Suite 4.202 Austin, Texas 78701
Sponsoring Organization: Texas Department of Transportation Research and Technology Implementation Office P.O. Box 5080 Austin, Texas 78763-5080
Performed in cooperation with the Texas Department of Transportation and the Federal Highway Administration.
The Economics of Transportation Systems: A Reference for Practitioners January 2013 University of Texas at Austin Dr. Kara Kockelman, T. Donna Chen,Dr. Katie Larsen, and Brice Nichols
Sponsored by the Texas Department of Transportation
The authors appreciate all the contributions to this research of multiple individuals. These include Duncan Stewart, Project Director, who provided much guidance, feedback, and support; the Project Monitoring Committee members, particularly Matt MacGregor and Ron Hagquist, for their regular feedback; Dr. Fernanda Leite, Sami Kolahdoozan, Dr. Leigh Boske, YiYi Wang, and Jay Chmilewski for their various contributions to the Reference’s draft content; Maureen Kelly for editorial development and design; and Annette Perrone and Stefanie Pascacio for their editorial assistance.
Table of Contents Introduction Economics as a Tool for Transportation Decision-making .........................................................2 Reference Overview ....................................................................................................................3 Chapter 1. Costs and Benefits of Transportation 1.1 Introduction ........................................................................................................................ 1-1 1.2 Internal Costs and Benefits ................................................................................................ 1-3 Accounting Costs ................................................................................................................. 1-3 Capital vs. Operating Costs.................................................................................................. 1-3 Marginal vs. Average Costs ................................................................................................. 1-6 Total Costs: Fixed vs. Variable, and Short Run vs. Long Run .......................................... 1-11 Opportunity Costs .............................................................................................................. 1-12 1.3 External Costs and Benefits ............................................................................................. 1-17 1.4 Summary .......................................................................................................................... 1-21 1.5 An In-Depth Look ............................................................................................................ 1-23 Estimating Value of Travel Time (VOTT) ........................................................................ 1-23 Estimating Value of Reliability (VOR) ............................................................................. 1-23 Cost Functions and Returns to Scale in Production ........................................................... 1-24 1.6 References ........................................................................................................................ 1-25 Chapter 2. Pricing of Transportation Services 2.1 Introduction ........................................................................................................................ 2-1 2.2 What is an Optimal Price? ................................................................................................. 2-1 Maximizing Profit ................................................................................................................ 2-3 Maximizing Social Benefits ................................................................................................. 2-4 Yield Management ............................................................................................................... 2-5 2.3 Roadway Pricing ................................................................................................................ 2-7 Short-Run Marginal Cost Pricing ........................................................................................ 2-7 Long-Run Marginal Pricing ................................................................................................. 2-9 Static vs. Dynamic Pricing ................................................................................................... 2-9 Second-Best Pricing ........................................................................................................... 2-10 Implementing Roadway Pricing ........................................................................................ 2-13 Freight Movements ............................................................................................................ 2-18 2.4 Road Pricing’s Impacts on Equity ................................................................................... 2-20
Measures to Improve Horizontal Equity ............................................................................ 2-21 Measures to Improve Vertical Equity ................................................................................ 2-21 2.5 Summary .......................................................................................................................... 2-23 2.6 References ........................................................................................................................ 2-24 Chapter 3. Regulation and Competition 3.1 Introduction ........................................................................................................................ 3-1 3.2 Regulations ........................................................................................................................ 3-1 Environmental ...................................................................................................................... 3-2 Safety ................................................................................................................................... 3-5 Workers’ Wages................................................................................................................... 3-8 3.3 Deregulation....................................................................................................................... 3-8 Railroad and Motor Carrier Deregulation ............................................................................ 3-8 Airlines Deregulation ......................................................................................................... 3-10 3.4 Competition ..................................................................................................................... 3-12 Competition in Public Transit Systems.............................................................................. 3-12 Competition between Bidders ............................................................................................ 3-13 3.5 Summary .......................................................................................................................... 3-13 3.6 An In-Depth Look ............................................................................................................ 3-15 Regulatory Evolution ......................................................................................................... 3-15 3.7 References ........................................................................................................................ 3-17 Chapter 4. Movement, Transportation, and Location 4.1 Introduction ........................................................................................................................ 4-1 4.2 Accessibility and Mobility ................................................................................................. 4-1 4.3 Transportation and Location Choice.................................................................................. 4-1 Theories of Business Location ............................................................................................. 4-2 Theories of Residential Location ......................................................................................... 4-5 Policy Impacts ...................................................................................................................... 4-7 4.4 Transportation and Land Values ........................................................................................ 4-7 Theoretical Expectations ...................................................................................................... 4-7 Rail Transit........................................................................................................................... 4-8 Highway Investment ............................................................................................................ 4-9 4.5 Transportation and Wages ............................................................................................... 4-10 4.6 Transportation and Economic Development ................................................................... 4-11 Economic Impact of Relief Routes .................................................................................... 4-13
Economic Impact of Access Management ......................................................................... 4-13 4.7 Summary .......................................................................................................................... 4-15 4.8 References ........................................................................................................................ 4-16 Chapter 5. Investment and Financing 5.1 Introduction ........................................................................................................................ 5-1 5.2 U.S. Railroad, Road, and Bridge Investment Needs .......................................................... 5-1 Importance of Transportation Infrastructure ........................................................................ 5-1 U.S. Railroad Infrastructure ................................................................................................. 5-2 U.S. Roads and Bridges ....................................................................................................... 5-3 5.3 Financing ........................................................................................................................... 5-5 Revenue Sources .................................................................................................................. 5-5 Expenditure Sources ............................................................................................................ 5-6 Traditional Project Delivery Methods ................................................................................. 5-8 Innovative Financing ........................................................................................................... 5-8 5.4 Summary .......................................................................................................................... 5-22 5.5 An In-Depth Look ............................................................................................................ 5-23 FY2010 Funding Breakdown ............................................................................................. 5-23 TIF Legislation and Valuation ........................................................................................... 5-24 TRENDS ............................................................................................................................ 5-25 5.6 References ........................................................................................................................ 5-27 Chapter 6. Project Evaluation 6.1 Introduction ........................................................................................................................ 6-1 6.2 Engineering Economic Analysis ........................................................................................ 6-1 Discount Rate and Time Value of Money ........................................................................... 6-1 Net Present Value (NPV) ..................................................................................................... 6-2 Internal Rate of Return (IRR) .............................................................................................. 6-8 Incremental Rate of Return (ΔROR) ................................................................................... 6-9 Payback Period................................................................................................................... 6-10 Breakeven Analysis ........................................................................................................... 6-11 Cost-Benefit Analysis (CBA) ............................................................................................ 6-13 Life Cycle Cost Analysis (LCCA) ..................................................................................... 6-17 Constrained Optimization .................................................................................................. 6-18 6.3 Multicriteria Analysis ...................................................................................................... 6-25 Simple Additive Weighting (SAW) ................................................................................... 6-27
Data Envelopment Analysis (DEA) ................................................................................... 6-29 6.4 Sensitivity Analysis ......................................................................................................... 6-29 Single Factor Sensitivity Analysis: A TxDOT Application .............................................. 6-30 Multiple Factor Sensitivity Analysis: A TxDOT Application ........................................... 6-32 Monte Carlo Methods ........................................................................................................ 6-33 6.5 Summary .......................................................................................................................... 6-33 6.6 References ........................................................................................................................ 6-34 Chapter 7. Economic Impact Analysis of Transportation Investments and Policies 7.1 Introduction ........................................................................................................................ 7-1 7.2 Why Economic Impact Analyses Are Conducted ............................................................. 7-1 Prediction and Evaluation .................................................................................................... 7-1 Motivations .......................................................................................................................... 7-2 7.3 Economic Indicators .......................................................................................................... 7-3 Measuring Economic Indicators .......................................................................................... 7-4 Impact Measures .................................................................................................................. 7-5 7.4 Generative and Redistributive Impacts .............................................................................. 7-5 7.5 Paths of Economic Analysis .............................................................................................. 7-9 7.6 Input-Output (IO) Modeling ............................................................................................ 7-14 The Foundation of the IO Model ....................................................................................... 7-16 Using the IO Model ........................................................................................................... 7-19 Multiplier Analysis ............................................................................................................ 7-19 Limitations of IO Modeling ............................................................................................... 7-24 7.7 Computable General Equilibrium (CGE) Models ........................................................... 7-25 The CGE Model Structure ................................................................................................. 7-26 Strengths and Limitations of CGE Models ........................................................................ 7-31 7.8 Summary .......................................................................................................................... 7-32 Recommended Reading ..................................................................................................... 7-33 7.9 An In-Depth Look ............................................................................................................ 7-34 Texas EIA Requirements ................................................................................................... 7-34 Double-Counting................................................................................................................ 7-35 Creating the IO Model ....................................................................................................... 7-37 CGE Model Foundation ..................................................................................................... 7-47 7.10 References ...................................................................................................................... 7-52
Chapter 8. Econometrics for Data Analysis 8.1 Definitions and Steps for Econometric Analysis ............................................................... 8-1 Some Caveats ....................................................................................................................... 8-1 Some Terminology............................................................................................................... 8-2 Understanding the Data: Use of Summary Statistics ........................................................... 8-5 8.2 Data Sets for Regression Models ....................................................................................... 8-8 Cross-Sectional, Time-Series, and Panel Data Sets ............................................................. 8-9 8.3 Specifying the Model ....................................................................................................... 8-11 Parameter Estimation ......................................................................................................... 8-12 Choice of Estimation Methods: Error Terms’ Distributions.............................................. 8-16 Choice of Estimation Methods: Error Term Correlation ................................................... 8-17 Common Modeling Mistakes ............................................................................................. 8-18 8.4 Nonlinear Parameter Estimation Methods ....................................................................... 8-20 Generalized Least Squares (GLS) Estimation ................................................................... 8-21 Instrumental Variables (IV) Estimation ............................................................................. 8-21 Systems of Equations Models (SEMs) .............................................................................. 8-22 8.5 Panel Data Models and Parameter Estimation Methods .................................................. 8-22 Pooled OLS ........................................................................................................................ 8-23 Fixed Effects (FE) Modeling ............................................................................................. 8-23 First Difference (FD) Modeling ......................................................................................... 8-24 Random Effects (RE) Modeling ........................................................................................ 8-24 8.6 Discrete Choice Models and Estimation Methods ........................................................... 8-25 8.7 Time-Series Modeling ..................................................................................................... 8-29 Single Response Variable Settings .................................................................................... 8-30 Multivariate Response Variable Settings ........................................................................... 8-31 8.8 Computer Programs for Econometric Applications......................................................... 8-31 8.9 Statistical Significance and Prediction ............................................................................ 8-32 8.10 Summary ........................................................................................................................ 8-34 8.11 An In-Depth Look .......................................................................................................... 8-35 Systems of Simultaneous Equations .................................................................................. 8-35 Recursive and Non-Recursive SES .................................................................................... 8-36 Seemingly Unrelated Regression (SUR)............................................................................ 8-37 Single-Equation Estimation Methods ................................................................................ 8-37 Systems of Equation Models (SEM).................................................................................. 8-38
8.12 References ...................................................................................................................... 8-44 Chapter 9. Data Sets References ................................................................................................................................ 9-2 Examples of Existing Data Sets ............................................................................................... 9-3 Chapter 10. Case Studies 10.1 Case Study 1: Cost-Benefit Evaluation of Network Improvements .............................. 10-1 10.2 Case Study 2: The Economic Impacts of Bypasses ....................................................... 10-3 10.3 Case Study 3: The Economic Impacts of Congestion Pricing ....................................... 10-6 10.4 Case Study 4: Right-of-Way Acquisition Costs ............................................................ 10-7 10.5 Summary ...................................................................................................................... 10-10 10.7 References .................................................................................................................... 10-11
Introduction and Overview “I am an engineer, so I never use economics—do I?” Transportation planners and engineers often feel unfamiliar with economic principles, and some assume that economics does not apply to their job duties. In practice, most transportation professionals can regularly employ economic concepts and techniques for decision-making—and many do, albeit unconsciously. Due to a variety of time and data constraints, many transportation practitioners’ decision-making processes are not formally documented and emerge via “engineering judgment.” However casual in nature, the wisdom behind such judgment comes from past experiences and is rooted in economic considerations and consequences. In fact, many rules of thumb for transportation investment and policy arose from economic backgrounds. Consider this example: due to pavement aging and regular use, many farm-to-market (FM) roads are in need of rehabilitation or reconstruction. Should TxDOT districts install more expensive but longer lasting concrete pavements or rely on less expensive asphalt overlays? The rule of thumb is to go with asphalt, for a variety of reasons, but a definitive answer is not simple. If strict near-term budget constraints did not exist, the decision presumably would be based on a lifecycle cost analysis, used to reveal the solution that yields the lowest annual equivalent cost or maximum net present benefit over a long-term horizon, reflecting risk and uncertainty in flow volumes, materials prices, vehicle sizes, and other economic indicators. In the face of tight budgets, immediate tradeoffs loom. Asphalt pavements may be favored simply to ensure a consistent level of pavement quality across the district under limited funding conditions, while emphasizing equity in funds disbursem*nt—thus covering more funding requests in a given year. However, if certain FM roads carry significantly more truck traffic, and some are in areas with high levels of black clay (which causes premature distress on asphalt pavement and so requires higher maintenance costs), should these roads be candidates for concrete pavements? What if such a consideration requires some lighter-traffic roads to be maintained less frequently? What is the cost passed onto the users of the lighter-traffic roads who may experience slower travel times and increased vehicle repair and maintenance costs? This common topic is rife with economic considerations. Fortunately, a wide variety of tools is available to help transportation professionals address these common but fundamentally complex questions with more confidence than a rule of thumb offers. All of the following questions also apply. Have you ever had to ponder one or more of these? • How much should contractors be charged for project schedule delays? • How should DOTs prioritize capacity-expansion, maintenance, and operations projects? • With limited funding, should DOTs focus on implementing multiple smaller projects, or allocate a significant amount to relatively few larger projects? • Should a new highway include or exclude frontage roads? What are the monetary and other costs associated with constructing these frontage roads relative to the benefits they provide?
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• Should right-of-way (ROW) acquisition for a new-build project include room for a future passenger rail corridor (or other future connecting facilities)? What is the likelihood of rail implementation (or construction of future connections) compared to the uncertainty of future ROW acquisition cost? • Should a speed limit be raised (to save travel time) or lowered (to guard against severe crashes and increase energy efficiency)? What speed changes (and times savings) can we expect from drivers, and how do all costs and benefits compare? • If adding a relief route attracts new development (e.g., a big-box retailer) to the bypass frontage, but the competition closes several smaller shops in the city’s historic downtown, what is the overall economic impact to the city? And to the region? • What is better for DOT budgets, the environment, and travelers: gas taxes, vehicle-milestravelled (VMT) fees, or tolls by time of day and location? These are just a few of the questions where successful solutions are improved by an economic understanding. This Reference is designed to introduce transportation practitioners to the underlying economic realities of their profession. Ultimately, good engineering judgment, which is vital to defensible and optimal decision-making, relies in large part on good economic judgment.
Economics as a Tool for Transportation Decision-making From travel time savings to job creation (both direct and indirect), income growth to property value changes, motor vehicle crashes to air quality and noise impacts, and microeconomic choices to macroeconomic shifts, transportation policies and investments carry great weight. Where formally assembled data is available, economic analysis tools allow decision-makers to comprehensively evaluate projects. For large projects with significant costs and many others closely scrutinized by the public, practitioners feel more confident about decisions with “numbers to back them up.” Even when data are lacking and/or decision impacts are minor, a basic understanding of various economic principles will aid transportation professionals in anticipating the direction and general magnitude of project (and policy) effects. Such understanding helps identify key project impacts and leads to more educated and robust decisionmaking. An understanding of current and future data needs also helps engineers and planners identify—and remedy—important data limitations for enhancements in future decision-making. For example, unsafe curves on two-lane highways with high fatality rates can be prioritized on the basis of these crashes’ very high economic and social costs, as discussed in Chapter 1, Costs and Benefits of Transportation. And the geo-coding of network design databases (such as TxDOT’s RHiNo and GeoHiNi files) can be prioritized to better map to police-report crash information systems, ensuring more accurate crash counts by segment for statistical regression applications (as discussed in Chapter 8, Econometrics for Data Analysis.) An additional motivation for DOT staff to become familiar with economic analysis tools is the trend of evolving federal mandates that require economic impact analysis and comprehensive quantification of transportation costs and benefits. For example, the USDOT Transportation Investment Generating Economic Recovery (TIGER) Discretionary Grant Program required that applicants monetize project benefits in the categories of livability, economic competitiveness, Introduction
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safety, state of good repair, and sustainability (as discussed in Chapter 5, Investment and Financing). Such mandates will motivate agency staff to understand and apply economic techniques in order to pursue funding through various federal channels. Even if such analyses are not performed in-house, an understanding of basic economic analysis principles helps staff members critically review outsourced analyses and more appropriately guide consultants’ activities. This Reference seeks to enable such understanding while enhancing a wide variety of DOT staff activities. Following is a sneak preview of key concepts covered.
Reference Overview For those who want a strong sense of the Reference without reading it from beginning to end, this section summarizes the chapters while demonstrating how topics relate across chapters. This overview also lists sample transportation considerations that each chapter addresses, pointing readers to specific chapters for more details on topics of greatest interest to them.
Chapter 1: Costs and Benefits of Transportation Without question, transportation plays a vital role in human interactions. Before delving into analytical techniques, transportation engineers and planners should be able to comprehensively tabulate the various benefits and costs associated with transportation decisions, as these costs and benefits are fundamental to project and policy valuations, both economic and otherwise. The Reference’s first chapter lays this foundation. Chapter 1 covers the estimation of capital costs and operating costs, which may be fixed or variable. In the short run, capital costs such as construction and design are considered fixed, while operating costs such as maintenance and traffic management are considered variable. However, in the long run, all transportation facilities will eventually need replacement or major rehabilitation, and capital costs can also be considered variable costs, as Chapter 1 explains. The chapter also differentiates between marginal and average costs, which are key concepts for optimal supply, demand, and cost allocation decisions. For example, a new four-lane highway is unlikely to cost twice as much as a two-lane highway (in the same location) because the marginal cost of adding lanes is likely to be less than that of the first and second lanes, thanks to economies of scale. Chapter 1 also introduces opportunity costs and indirect internal costs, which can be difficult to observe and/or quantify, but can be monetized based on choice behaviors. For example, no physical monetary exchange occurs when a driver is stuck in traffic on a congested highway, yet that delay results in missed economic (and other) opportunities. Willingness-to-pay (WTP) measures allow planners to estimate the value of travel time (VOTT), which may be the monetary value a salesperson places on spending an extra hour pitching a product or a parent places on spending at his/her child’s soccer game (as opposed to sitting in traffic). Moreover, travelers are concerned about travel time reliability. A truck driver on the way to an important or time-constrained delivery can better anticipate and prepare for a consistent 30-minute travel time than one that averages 25 minutes, but regularly varies between 15 and 50 minutes. Another critical, yet sometimes overlooked, benefit-cost component emerges from the concept of externalities, or external costs to society. Transportation system users impact the safety and well-being of others via these developments: traffic crashes; the effect of mobile emissions on air I-3
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quality; traffic noise; and effects on wetlands, groundwater quality, endangered species, and other wildlife habitats. Chapter 1 discusses how these external impacts can be—and often are— valued, as compared to the internal costs and benefits of transportation projects and policies. Some example questions that Chapter 1 addresses are as follows: • What factors into the marginal and average ownership and operating costs ofvehicles? • Why do airline, trucking, and shipping industries rely on “hub-and-spoke” networks? What is their economic advantage? • How are the VOTT and value of reliability (VOR) estimated, and how do these compare? • What are the economic and other benefits of a 50% crash reduction when a dangerous intersection is realigned for better visibility? • How do the benefits of a noise wall compare to its construction costs?
Chapter 2: Pricing of Transportation Services While Chapter 1 emphasizes the significant benefits and costs involved in providing and using transportation systems, Chapter 2 examines how these costs can best be allocated. In other words, who should pay for transportation services and how? Transportation pricing refers to fees (and incentives) incurred by travelers, including transit fares, cargo fees, fuel taxes, tolls, parking fees, vehicle registration fees, and insurance payments. A key Chapter 2 concept is the notion of consumer surplus, or the difference between the maximum price a traveler is willing to pay for a good or service and the price he/she actually pays. While providing transportation free of charge is infeasible, tolling or taxing in pursuit of maximum profit contradicts the goal of putting public interests first. In theory, social welfare is maximized when marginal cost pricing is used, which is when users pay the equivalent of their added cost to the system (in terms of delays for those who follow, crash costs they may be responsible for but don’t pay for, emissions that others will be breathing, and so forth). For example, a flat-rate tolled road cannot moderate congestion as well as a dynamically/variably priced road. Variable tolls are generally designed to fall (to zero, potentially) when traffic is light (because external delay costs are negligible then) and rise when traffic is heavy (because added delays are quite high under near-capacity conditions). In theory, a dynamically priced road can be priced so that level of service (LOS) F never occurs. But with only incomplete information available to drivers, unpredictable events (such as traffic crashes), and a lack of substitutable alternative routes and modes (and imperfect pricing of such alternatives), even facilities with the best applications of congestion pricing can occasionally experience congestion. In addition to fuel taxes and vehicle registration fees, Chapter 2 describes other pricing strategies to achieve fuller cost recovery and better reflect user costs. These include congestion pricing, highway cost allocation methods, and VMT fees. The chapter also examines equity issues that can arise from transportation pricing (and other) policies, such as impacts to specific socioeconomic groups and/or people with special mobility needs. Example questions that Chapter 2 addresses are as follows:
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• How much should commercial trucks pay per mile of freeway driving, versus light-duty vehicles? • How does a fuel tax differ from a mileage-based user fee? What considerations determine an optimal VMT fee? • How can tolling be deployed to prevent congestion in the presence of non-tolled alternative routes? • What freight pricing mechanisms can regulate truck travel demand on busy urban corridors? • How do the equity impacts of gas taxes compare to those of VMT fees? • What are common administrative costs to implement automated tolling?
Chapter 3: Regulation and Competition Due to their tremendous importance and complexity, transportation markets are subject to various forms of regulation. As transportation supply and travel demand have evolved over time, policies and regulations have developed accordingly. Economic, safety, environmental, and social regulations are set by multiple government entities to enhance procedures and behaviors, and these regulations impact market outcomes—including competition across modes and within modes (e.g., airlines and railroads). Environmental and safety regulations can affect every transportation market participant, from car manufacturers (who must abide by fuel economy standards and vehicle safety requirements) to transportation agencies (who must pursue many kinds of environmental impact studies in order to receive federal transportation funding). Wage regulations establish elevated minimumpay rates for different types of labor and tend to raise the cost of federally funded construction projects. Competition between operators depends upon many factors, including the nature of demand (e.g., local vs. inter-regional) and technology (e.g., high speed vs. low speed, shared vs. exclusive ROW, electric vs. conventional vehicles, and online reservations vs. first come-first served settings). Risk and uncertainty also vary by mode and setting. As a result, regulatory policies vary across sectors (e.g., air vs. rail), operator types (e.g., private vs. public carriers), vehicle types (e.g., passenger cars vs. motorcycles), times of day (e.g., nighttime vs. daytime speed limits), and so forth. In some cases, deregulation, or the removal of government mandates, has allowed more efficient operation of transport markets. Chapter 3 provides examples of deregulation in the railroad and airline industries that have resulted in growth of freight and long-distance passenger travel. On the other hand, increased regulation through government ownership of previously private transit systems has preserved a system whose service arguably suffers from too little competition. Example questions that Chapter 3 tackles are as follows: • What are some cost-control strategies to increase competition among bidding contractors for transportation construction projects?
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• What are the carbon emissions and energy implications of Corporate Average Fuel Economy (CAFE) standards? • What safety benefits do air bag, car seat, and seat belt laws provide? • What impacts did trucking’s deregulation have on U.S. highways?
Chapter 4: Transportation, Movement, and Location Chapter 4 describes interactions between transport and location choices, land values, wages, and economic development. The “chicken and egg” relationship between system provision and land use decisions is discussed in the context of distinguishing accessibility from mobility. Transportation engineers have long quantified the travel-time savings benefits of network improvements. However, travel is a derived demand, a by-product of the need to work, shop, visit with others, and so forth. Other than the occasional joy ride or Formula One race, travel itself is not the desired activity. Getting from point A to point B only matters if point B is a quality destination that offers the traveler the satisfaction of a more direct demand (for labor, food, human interaction, etc.). While the quest for mobility looks solely at travel times, speeds, and distances, accessibility also considers the quality of the destinations, which tends to increase with higher land use intensities. While activity site locations tend to drive travel patterns in the short term, in the longer term transportation infrastructure pricing and provision shape urban form. In addition to impacting land use decisions, transportation policies and investments impact land values, the prices of goods and services, and wages. Chapter 4 describes how transportation influences site accessibility and thus business and household location choices. Because access is valued by businesses and households, transportation investments (and speed limits and tolling policies) impact land values via their bid-rent curves. Chapter 4 presents estimates of property-value impacts from many case studies of rail transit and highway investments. Such impacts are generally context-specific, with economically thriving communities already experiencing population and employment growth tending to benefit the most from transportation investments. Studies of urban wage gradients find that employees with higher commute costs require higher wages, creating wage differences between urbancenter workers and those in suburban employment centers, with their typically lower commute costs. Chapter 4 examines key features of the following example questions: • Why do businesses tend to cluster by industry (e.g., Silicon Valley and Wall Street)? • How do transportation investments impact nearby land values? • How do raised medians and other access management strategies impact local sales, by business type?
Chapter 5: Transportation Investment and Financing Transportation agencies face budget constraints and must set funding priorities. Chapter 5 describes U.S. road, bridge, and railway infrastructure conditions and investment needs. Transportation financing strategies are presented in the context of traditional revenue sources and other, more innovative financing methods. Traditional sources include state and federal funding,
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via motor fuel taxes, bond proceeds, tolls, state motor fuel taxes, vehicle registration fees, along with local funding via property and sales taxes. Funding shortfalls have motivated the pursuit of other financing methods. Chapter 5 summarizes key features of federal Section 129 loans, TIFIA loans, and TIGER/MAP-21 grants, along with opportunities such as state infrastructure banks (SIBs), private activity bonds (PABs), tax increment financing (TIF), and public-private partnerships (PPPs). Each method has advantages and disadvantages, in specific contexts, and example projects financed under each method are provided throughout the chapter. Chapter 5 content addresses a range of questions, including the following: • Which Texas transportation projects have utilized TIFIA financing? • What is the federal grant application process, and what kind of projects do the programs target? • From a DOT’s perspective, what are the advantages and disadvantages of design-build projects? How do features compare between a new-build project versus a concession or lease on an existing facility?
Chapter 6: Project Evaluation Chapter 6 describes two different approaches to project evaluation and selection: traditional engineering economy-based techniques and multicriteria analysis (MCA) methods. This chapter defines and then demonstrates how to use discount rates, internal rates of return (IRRs), payback periods, breakeven analysis, and other techniques. The chapter outlines typical steps in a comprehensive cost-benefit analysis (CBA) to reflect agency costs, user benefits and costs, and externalities, as introduced in Chapter 1. Chapter 6 also discusses constrained optimization, an important tool for maximizing total benefits under budget and other constraints (or, for example, minimizing costs subject to supply and demand constraints). Such techniques can be quickly applied using MS Excel functions. Unlike traditional optimization techniques, where all outcomes are characterized in a single metric (such as dollars), MCA can reflect a host of non-quantifiable considerations, such as environmental justice and public support, thus allowing project rankings to be reasonably calculated across multiple criteria in various dimensions. Chapter 6 presents a Kansas DOT MCA application utilizing weighted numeric scores for project selection to highlight the flexibility (and potential interpretation issues) of such tools. The simplest form of MCA, simple additive weighting (SAW), is discussed in detail and illustrated with an example. Data envelopment analysis (DEA), a widely used decision-making model to compare relative performance of units within systems, is also covered. Chapter 6’s final section introduces sensitivity analysis, which helps decision-makers identify the degree to which analysis outputs (such as net present values, project rankings, and traffic flow predictions) are affected by changes in inputs. For example, link performance function parameters and population and job growth rates are critical assumptions in travel demand models that can greatly impact future years’ traffic predictions (and project benefit-cost ratios).
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Sensitivity analysis can quantify the range of likely decision outcomes, helping decision-makers guard against uncertainty and risk. Example questions that Chapter 6 helps address are as follows: • What are the steps for a proper CBA? • What is the difference between real interest rates and nominal interest rates? • What are the costs and benefits associated with a bridge replacement project, and how can future benefits be compared to present costs? • How can the user benefits and construction and rehabilitation costs of flexible and rigid pavement alternatives be compared using life-cycle cost analysis (LCCA)? • What is the optimal way to select a set of top projects under agency budgets and other constraints?
Chapter 7: Economic Impact Analysis of Transportation Investments In addition to anticipating direct costs and direct benefits of policies and projects, transportation professionals are interested in a wide range of less direct economic impacts. Chapter 7 opens with a discussion of why economic impact analyses (EIAs) are conducted, describing regulatory requirements such as environmental impact statements and public information and planning needs. While a CBA can reveal project alternatives that maximize net benefits, an EIA attempts to anticipate wage, employment, sales, and related impacts. Economic indicators such as spending by households and businesses, employment levels, wages, business sales, tax revenues, exports and imports, and capital investment expenditures can be the focus of an EIA. Transportation investment has the potential to affect a variety of economic indicators (via time savings and price changes), and these changes can be categorized as generative or redistributive impacts. As Chapter 7 described, generative impacts produce a net economic gain, while redistributive impacts essentially shift economic activity from one area to another, netting zero economic gain. Chapter 7 also introduces input-output and general equilibrium models of the economy. These models’ foundations are presented alongside model strengths and limitations, helping identify a variety of critical issues that relate to all economic analysis methods. An in-depth section offers a deeper look at the common issue of double-counting economic impacts, identifying a dozen types of EIA-based double-counting errors. Chapter 7 helps address questions like these: • How do EIA and CBA differ? • What is the difference between economic value and economic impact? • How can engineers anticipate the multiplicative effects of transportation spending? • What is double-counting and how can it be avoided?
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Chapter 8: Econometrics for Data Analysis This chapter is a departure from project-focused evaluation and impact analysis. It tackles the fundamentals of transportation data analysis, characterizing mathematical relationships across wide samples of data points. Econometric analysis involves advanced statistical models to help practitioners analyze a variety of transportation data and discern the interactions and relationships between various variables (in order to pursue more optimal policies and investments, while predicting future trends). The results of these flexible statistical models are used to predict ROW acquisition costs, mode and route choices in the presence of tolls, the impact of gas prices on VMT, the effects of increased speed limits on crash counts and injury severities, the impact of household characteristics and income on vehicle ownership choices, and much more. Chapter 8 details the steps taken for econometric analysis, including data selection, model specification, and parameter estimation (including use of MS Excel’s Regression command). Crucial to the understanding of econometrics is the realization of how different types of data affect choice of model and estimation methods. The chapter introduces a wide variety of continuous (both linear and non-linear) and discrete choice model specifications to illustrate the models and methods that are most appropriate for different data types and research questions. Example specifications include ordinary versus feasible generalized least squares, use of instrumental variables and seemingly unrelated regression systems, and multinomial logit versus ordered probit models. The ultimate goal of econometric regression analysis is to determine the explanatory variables that impact the response variable—and to what extent. Chapter 8’s final sections describe how to determine statistical versus practical significance.
Chapter 9: Data Sets and Chapter 10: Case Studies The Reference’s last two chapters identify data resources and describe real-world transportation applications of various economic methods and tools discussed in the main chapters. The Data Sets chapter lists example transportation applications for a wide variety of publicly available data sets, and mentions some data collection trends (e.g., use of GPS and Bluetooth-based cellphone data). Many economics concepts apply simultaneously in specific transportation project and policy contexts. The Reference’s Case Studies chapter details several applications that demonstrate this interconnectedness, while tying such concepts to real-world settings. The featured case studies include a benefit-cost evaluation of a New Jersey DOT highway extension project, the local sales impacts of bypasses for small to mid-sized Texas communities, an economic impact assessment of various congestion pricing scenarios in the Dallas-Fort Worth Metroplex, and an estimation of ROW acquisition costs. All together, these case studies, analysis methods, and transportation economic fundamentals reveal a world of concepts and tools that should strengthen the practice of transportation engineering, planning, and policymaking. Ultimately, travel is an economic activity, and DOT decisions impact our quality of life in a number of significant and measurable ways. To ignore such opportunities is imprudent. Resource constraints and public interest should not allow a lack
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Introduction
of economic understanding to continue. The motivation is clear, and the opportunities to incorporate economic ideas in the practice of transportation planning and implementation are endless. Here’s to better decision-making and public communication!
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Chapter 1. Costs and Benefits of Transportation 1.1 Introduction ........................................................................................................................ 1-1 1.2 Internal Costs and Benefits ................................................................................................ 1-3 Accounting Costs ................................................................................................................. 1-3 Capital vs. Operating Costs.................................................................................................. 1-3 Estimating Capital Costs ................................................................................................... 1-4 Estimating Operating Costs .............................................................................................. 1-5 Marginal vs. Average Costs ................................................................................................. 1-6 Average Costs and Economies of Scale ........................................................................... 1-7 Economies of Scale vs. Returns to Scale ...................................................................... 1-8 Diminishing Returns vs. Decreasing Returns to Scale .................................................. 1-8 Total Costs: Fixed vs. Variable, and Short Run vs. Long Run .......................................... 1-11 Opportunity Costs .............................................................................................................. 1-12 Shadow Prices ................................................................................................................. 1-12 Implicit Costs .................................................................................................................. 1-13 Estimating the Value of Travel Time .......................................................................... 1-14 Reliability of Travel Times ......................................................................................... 1-14 Opportunity Cost Example: Cost of Delay ..................................................................... 1-15 1.3 External Costs and Benefits ............................................................................................. 1-17 1.4 Summary .......................................................................................................................... 1-21 1.5 An In-Depth Look ............................................................................................................ 1-23 Estimating Value of Travel Time (VOTT) ........................................................................ 1-23 Estimating Value of Reliability (VOR) ............................................................................. 1-23 Cost Functions and Returns to Scale in Production ........................................................... 1-24 1.6 References ........................................................................................................................ 1-25
Key Terms
1.1 Introduction Transportation constitutes nearly 20% of household expenditures, 30% of U.S. greenhouse gas (GHG) emissions, and 70% of domestic petroleum consumption. In a world of limited resources, volatile materials prices, energy security issues, and multiple environmental concerns, it is imperative to understand and accurately model how transportation investments and policies impact stakeholders’ and society’s bottom lines. Economic practice and theory require familiarity with a variety of costs and benefits. So do transportation engineering and planning practice. This chapter presents the most common cost and benefit concepts encountered in transportation economics. A transportation project’s or policy’s economic value can be estimated by anticipating its potential costs and benefits. Table 1.1 lists the typical costs and benefits considered in this chapter’s transportation context, and Figure 1.1 illustrates them. Economists regularly refer to variable and fixed costs, and Figure 1.1 illustrates this complementary relationship, with dashed lines around capital and operating costs indicating that these can be either variable or fixed in different contexts. Further, the figure illustrates that all costs can be categorized as internal or external costs, and this chapter describes their inter-relationships.
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TC: total cost AC: average cost MC: marginal cost VOTT: value of travel time VOR: value of reliability
For applications of costs and benefits concepts, see Chapter 6 for cost-benefit analysis (CBA) and Chapter 7 for economic impact analysis (EIA).
Costs and Benefits
Potential Costs (and Benefits) of Transportation Projects and Policies
Description
Examples
Capital
One-time design and construction costs
A new facility’s capital costs include planning, preliminary engineering, project design, environmental impact analysis, right-of-way (ROW) acquisition, construction, equipment purchases, etc.
Operating
Recurring operations, maintenance, and rehabilitation costs
Typical highway operating costs include traffic management, crash- or weatherrelated repair and cleanup, equipment (vehicles, traffic signals, signs), utilities, resurfacing (but not reconstruction), etc.
Vehicle
Vehicle ownership and maintenance costs such as fuel, tire replacement, insurance, etc.
Pavement resurfacing improves road conditions and reduces vehicle wear and maintenance costs.
Travel Time
Lost time and productivity
Implementation of signal timing coordination on an arterial street enables faster travel times and reduces delay.
Travel Time Reliability
Variance of schedule uncertainty
Safety
Number, severity, and cost of crashes
Emissions
Noise
Ecological Impacts
Costs and Benefits
Health and other costs of vehicleproduced pollution due to changes in travel speeds, distances, times of day, fuels, and vehicle types Discomfort and property value loss due to increased traffic noise Travel’s impacts on wildlife habitat, water flow, and water quality
Dynamically priced high-occupancy/toll (HOT) lane keeps travel speeds close to free flow speed and reduces variability in travel time. Addition of rumble strips reduces the number of crashes related to driver fatigue.
Table 1.1: Typical Costs and Benefits Used in CBA, EIA, and Other Project Evaluation Methods
Fleet conversion from diesel to compressed natural gas vehicles reduces emissions.
Construction of a sound wall between a freeway facility and an adjacent neighborhood reduces traffic noise. Planned roadway alignment runs through an endangered species habitat, impeding animal movements through the area.
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Internal Costs
External Costs (Fixed and Variable)
Accounting Costs
Opportunity Costs
+
Capital Costs (Fixed and Mobile)
Figure 1.1: Cost Concept Relationships
Operating Costs
Variable + Fixed = Total Costs
Variable
+
Marginal Costs (MC) = Change in Total Costs (TC) per Additional Unit Output
Fixed
Average Cost (AC) = Total Cost (TC) divided by Total Output
1.2 Internal Costs and Benefits Broadly speaking, internal costs are those borne by system operators and/or users while external costs are those borne by non-users. Internal costs include construction, maintenance, operation, and road user costs (e.g., fuel and registration fees). External costs, such as noise and air pollution costs borne by members of the community other than the transportation system users, are discussed later in this chapter. In computing internal costs, an appreciation for the difference between real accounting costs and opportunity costs is necessary. The former are real, experienced, monetary costs, while the latter are potential, missed benefits, as described below.
Accounting Costs Accounting costs refer to transactions when real monetary changes occur. These costs are represented in traditional engineering project cost estimates—including but not limited to construction, maintenance, and operation costs. These costs can occur once, as in the case of initial project costs (like ROW acquisition and design), or be regular and continuous, as in the case of operations, maintenance, and rehabilitation. Distinctive accounting-cost concepts are presented in the following sections.
Capital vs. Operating Costs In transportation, capital costs typically refer to fixed capital costs for facilities and mobile capital costs for vehicles. Operating costs are incurred for goods and services Chapter 1
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Costs and Benefits
used to maintain and operate a facility, vehicle, or service. The benefit counterpart to operating costs is operating revenues (as well as savings in operating costs). Accounting techniques effectively merge capital and operating costs by including depreciation of capital goods within operating expenses. Categories of operating costs are essentially the same across all modes, whereas capital costs differ, as noted in Table 1.2. Mode
Capital Costs
Operating Costs
Automobiles
Vehicle, roadway, traffic signals
Railroads
Rail cars, tracks, stations, rail yards, signal systems
Airlines
Airports, traffic control systems
Ships
Ports, ships
Fuel, labor, maintenance, supplies
Table 1.2: Comparison of Capital and Operating Costs for Different Transportation Modes
Estimating Capital Costs Transportation planners and engineers must assess a number of capital costs when estimating project expenditures during the planning phase. In addition to standard construction costs, funding must be allocated for project design, environmental process activities, and ROW acquisition. The FHWA also recommends that a 5–10% contingency be included in projects to account for unforeseen changes, though a contingency of up to 15% may be used for projects particularly susceptible to “scope creep.” Low-risk projects must have well-defined scopes and schedules and properly identified risks and uncertainties. For projects entailing substantial risk and/or uncertainty, the FHWA recommends that ranges be used for early estimates. For example, a planning stage estimate could state project cost as $15 million, with a 95% confidence interval or probable range of $14 million to $18 million. Two basic procedure types are used to estimate project construction costs: detailed bid item estimates and broader conceptual estimates. Bid item estimates are conducted by estimating quantities and prices of specific items that contractors will bid on. An engineer may estimate, for example, that a project will require 300 linear feet of 36 in. diameter corrugated metal pipe (CMP) as one bid item. The engineer must then determine the price per foot of 36 in. CMP. He or she may do this by reviewing historical projects that used 36 in. CMP, emphasizing recent installations in nearby and similar locations with similar quantities of 36 in. CMP and similar project sizes. The FHWA cautions against using historical bid prices unless the projects are for similar work and similarly sized. As an alternative or supplement to estimating based on historical bid prices, the engineer may estimate the price that a potential contractor will purchase 36 in. CMP for and anticipate the labor and equipment costs required to install it. Regardless of whether historical bid prices or materials and labor are used to generate estimates, engineering judgment should be used for all assumptions. However, detailed bid item estimates are often time consuming and the individual quantities may be hard to obtain before substantial design work has been completed. Costs and Benefits
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Conceptual cost estimates are typically appropriate for sketch planning because they require a lower level of detail. A conceptual estimate is similar to a bid item estimate in that quantities of specific items are estimated and assigned a cost per unit. The difference lies in the magnitude and number of the quantities being estimated. A large project would look at the number and characteristics of new lane miles, on and off ramps, signalized intersections, bridges, and other major items. The costs of smaller items are figured into the larger ones. For example, a conceptual estimate would estimate the cost of a “50-foot by 3-mile resurfacing using 6 inches of asphalt treated base and 4 inches of asphalt” instead of estimating individual quantities and prices for tons of asphalt, tons of asphalt-treated base course, square feet of pavement removal, concrete manhole collars, and other items that may be measured in a bid item estimate. For example, generic cost-per-mile models developed by the Florida Department of Transportation estimated the 2012 cost of new construction of a twolane divided urban interstate with median, barrier walls, and full inside and outside shoulders to be $8.9 million per mile, and the cost of a rural arterial widening from two lanes to four lanes with shoulders to be $2.0 million per mile. The FHWA recommends that certain items such as traffic control, environment mitigation, and utility relocation be estimated separately from the base project and itemized within the final estimate. Design and ROW costs must also be anticipated. Design is often calculated as a percentage of project costs. Agencies typically assign a default design cost percentage, then adjust it up or down based on the project’s complexity. Also, design cost percentages for very small projects are generally higher because various administrative overhead costs typically apply, regardless of project size. ROW costs can be estimated during the planning process by first estimating the amount and location of new ROW required and then ascertaining whether any structures will be taken (e.g., parking lots and billboards). General property values (per square foot or acre) can then be applied for an early estimate of acquisition costs. Regression models using past ROW acquisition cost data (e.g., from TxDOT’s ROW Information System database) can also be developed, and their parameter estimates used, as provided under TxDOT projects 0-6630 and 0-4079, and as discussed in the Case Studies section of this Reference.
Resource The FHWA recommends that planners consult their ROW division staff when generating cost estimates. These individuals have the greatest expertise and will generate the most accurate ROW cost estimates.
Estimating Operating Costs Changes in operating costs may be determined by using standard values for expected operation and maintenance costs. Agencies often estimate standard maintenance costs for each foot-mile or lane-mile of pavement. Items such as road signs, guardrail, luminaire heads, and signal indications may have design lives shorter than the project life, so their replacement should be factored into maintenance costs. For projects with substantial new electrical features, daily electricity demands for illumination, signals, and intelligent transportation system applications must also be included.
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Costs and Benefits
Concept Example: Operating Costs Not all projects result in increased operations and maintenance (O&M) costs. For example, a roundabout replacing a traffic signal should result in lower overall O&M costs. Intersections with roundabouts require less electricity than signalized intersections (although intersection illumination is still required). Furthermore, signal cabinets, signal indications, vehicle detectors, and pedestrian pushbuttons do not have to be provided and then replaced (as components wear out). Nevertheless, most transportation investments add more pavement, guardrail, crash cushions, electroliers, signs, bridges, signals, and/or other features that can increase maintenance costs instead of lowering them. In general and in the short run, capital costs tend to be considered fixed costs, while operating costs tend to be considered variable costs. As discussed in a later section of this chapter, all costs are variable in the long run, though the time horizon for which that is true varies by project type. All facilities eventually need to be replaced or updated, and the costs for doing so can be considered variable costs over the long term, or fixed capital costs at/near the time of their application.
Marginal vs. Average Costs As noted in Table 1.3, marginal cost (MC) refers to the change in total cost when the quantity produced changes by one unit (or an infinitesimal unit), whereas average cost refers to total cost divided by the total number of units produced (such as seatmiles delivered by an bus operator, square feet of pavement laid by a contractor, and miles travelled by a commercial truck fleet). To get a sense of this difference, consider how the total cost of a new four-lane highway is unlikely to be twice as much as that of a two-lane highway in the same location. Essentially, marginal costs tend to fall with project size, so the marginal cost of the third and fourth lanes is likely to be less than that of the first and even second lanes, thanks to economies of scale in bringing equipment and workers out to a job site, ordering materials, managing the inspections, and so forth. Nevertheless, the average cost per lane of the new highway is the total cost divided by four. Table 1.3 also defines two types of marginal costs (point versus incremental) and compares them to the definition of average costs. Point marginal cost calculations can come from taking derivatives (if a continuous mathematical function exists for total costs), while incremental MC calculations reflect simpler (but somewhat less elegant) mathematics.
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Costs
How To Calculate (TC= Total Cost; Q= Total Output)
Point Marginal Costs (MC)
MC = dTC/dQ (derivative of TC with respect to Q) Example: TC=$200+4Q → dTC/dQ = $4/unit
ARC MC = Arc Marginal Cost (incremental)
Average Cost (AC)
(
−
Interpretation The instantaneous slope of the TC function, relative to output (Q). This represents the infinitesimal change in total costs for an infinitesimal change in total output (often a function of output level).
)− −
Example: ($600-$400)/(100 units - 50 units) = $4/unit AC = TC/Q Example: $600/100 units = $6/unit average cost
Table 1.3: Marginal and Average Cost Comparison
Normalized change in costs for a specific, discrete change in total output.
Also called unit cost, AC is total cost divided by total output.
Transportation Application: Marginal Vehicle Ownership and Operating Costs For automobile travel, marginal vehicle operation and ownership costs (such as tire wear and vehicle depreciation) are private costs borne by the users. The American Automobile Association estimated the cost of gas, maintenance, insurance, licensing, financing, and registration to be in the range of $0.47 to $0.72 per mile for a sedan, with a base cost of $0.179 per mile for gas and maintenance in 2010 dollars. Other estimates place operating costs (not including ownership and insurance) at $0.21 per mile (Polzin et al. 2008), or $0.173 per passenger-car mile, $0.217 per pickup truck, van, or SUV mile, and $0.49 per commercial-truck mile (Barnes & Langworthy 2003). However, depreciation is an inevitable cost of asset ownership, at an estimated $0.062 per added mile of passenger car use (Barnes & Langworthy 2003). In other words, greater use means faster depreciation of this asset.
Average Costs and Economies of Scale Average costs will vary as production expands and illustrate economies of scale and scope, as well as constraints on production. They help explain why increasing ship sizes have limits and why major airlines tend to rely on hub and spoke systems. Economy of scale (EOS) is an important concept in any production process, including the provision of transportation. EOS indicates what happens to average costs when scaling up a transportation project or process. A transportation service, facility, or organization can experience either (rising) economies of scale, constant economies of scale, or diseconomies of scale, depending on how average costs change as output (such as seat-miles or VMT) increases, as described below and shown in Table 1.4. A related term, returns to scale (RTS), is also common in economics and often used interchangeably with EOS. However, RTS refers to production or output scaling, rather than cost scaling, as described below. Chapter 1
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Chapter 6 discusses depreciation of vehicle capital ownership costs and use of interest rates for standard discounting techniques.
Costs and Benefits
Economies of Scale vs. Returns to Scale Economies of scale indicate average cost savings (per unit) as output/production increases, whereas returns to scale indicate the factor change in production in response to a factor increase in all inputs. When input prices are constant and do not change with the firm or agency’s purchase decisions, EOS and RTS are essentially equivalent (i.e., increasing RTS will occur with EOS and vice versa). Table 1.4 presents the three cases when EOS and RTS align in direction of change (increasing, constant, and decreasing), when input prices are fixed. Returns to scale are determined by analyzing a production function, which relates the maximum possible quantity of output with a given quantity of inputs. Economies of scale are determined by analyzing the average cost curve. When a transportation organization’s purchasing power lowers the price of inputs or its heavy demands increase input prices, EOS and RTS may no longer move in the same direction. For example, an organization that experiences an increasing returns to scale in production (a doubling of inputs [like workforce, fuel, and equipment] more than doubles outputs) could potentially have diseconomies of scale (e.g., input prices increase so much that average costs rise following doubled production). Case Increasing Economies of Scale or Increasing Returns to Scale
No Economies of Scale or Constant Returns to Scale
Diseconomies of Scale or Decreasing Returns to Scale
Economies of Scale Interpretation
Returns to Scale Interpretation
MC(Q) < AC(Q) The average cost (per unit) decreases as output increases.
Increasing all inputs by same proportion results in a more-thanproportional increase in the level of output.
Example: Average input costs per seat on Amtrak decrease as seat-miles increase.
Example: Number of Amtrak seatmiles increases by 60% when all inputs increase by 40%.
MC(Q) = AC(Q) The average cost (per unit) stays the same as output increases.
Increasing all inputs by same proportion results in the same proportional increase in the level of output.
Example: Average input costs per seat stay the same as seatmiles increase.
Example: Number of seat-miles increases by 50% when all inputs increase by 50%.
MC(Q) > AC(Q) The average cost (per unit) increases as output increases.
Increasing all inputs by same proportion results in a less-thanproportional increase in the level of output.
Example: Average input costs per seat increase as seat-miles increase.
Example: Number of seat-miles increases by 10% when inputs increase by 20%.
Table 1.4: Economies of Scale and Return to Scale Cases when Input Prices Are Constant
Diminishing Returns vs. Decreasing Returns to Scale Diminishing returns sounds similar to decreasing returns, but the former only applies to increases in one input or factor of production at a time (rather than a factor increase in all inputs). For example, increasing the number of employees at a toll Costs and Benefits
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booth will eventually result in diminishing returns because only so many employees can be in a toll booth. The marginal or added number of vehicles served (the output) per added worker will fall and eventually hit zero (and possibly turn negative) because the numbers of toll booths, lanes, and queued vehicles (three other key inputs) did not increase. If all inputs—number of employees, tollbooths, waiting vehicles, and lanes—are increased by the same factor, one would generally expect a proportional rise in output, and thus constant RTS. While constant RTS is most common in practice, and often assumed by economists, some production processes show rising and falling RTS, depending on details of how inputs interact. Transportation Application: Airline Hub-and-Spoke Operations The airline, trucking, and shipping industries regularly rely on hub-and-spoke operations to exploit the economies of using larger vehicles (and fuller vehicles, via more appropriately sized-to-load vehicles or higher load factors) to transport more passengers or goods at a lower average cost (and with more frequent trip scheduling, which travelers and shippers greatly appreciate). Trans-shipment points (like the Dallas-Fort Worth airport and Chicago rail yard) allow carriers to consolidate goods or passengers into larger vehicles (or longer trains) for routes with the demand to support the consolidation. Hub and spoke networks are designed with larger vehicles for high-demand routes (e.g., San Francisco to New York City) and smaller vehicles for lower-demand routes (e.g., Sacramento to San Francisco) to economize on average costs. Large and small vehicles exchange passengers or goods at the hubs. U.S. airlines were clearly moving towards hub and spoke structures by the mid-1980s because of economies of vehicle size. Figure 1.2 shows visually the change from direct service to hub and spoke for cities around Atlanta, Georgia (with Table 1.5 providing corresponding airport codes); and Table 1.6 lists Texas’s current hub airports. Because hub and spoke designs tend to increase total travel mile (by reducing the number of direct trips, from one’s origin non-stop to one’s destination), many airlines have more than one U.S. hub, as shown in Table 1.6’s listing of out-of-state hubs. Hub and spoke operations do carry the following added costs: • Increased operating costs due to mileage increases, • Additional terminal costs for passengers, • Additional take-off, landing, and operation costs, and • Potential delays for passengers and goods if hub operations cease or slow down temporarily (e.g., due to weather). Airports themselves do not offer clear economies of scale, so the advantage of hub and spoke lies in vehicle size and scheduling frequency for passengers, not airport size. For larger market pairs, of course, non-stop service endures, thanks to high demand (such as San Francisco to Chicago and New York City to Washington, D.C.).
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Figure 1.2: The Switch to Huband-Spoke Airline Operations around Atlanta, Georgia (see Table 1.5 for Airport Code Locations) (Source: Kanafani & Ghobrial 1985)
Airport Code BNA TYS CHA HSV BHM AVL MGM MOB JAX
Airport Location Nashville, TN Knoxville, TN Chattanooga, TN Huntsville, AL Birmingham, AL Henderson, NC Montgomery, AL Mobile, AL Jacksonville, FL
Texas Hub Airports
Dominant Carrier at Airport
Dallas-Fort Worth International Airport (DFW)
American Airlines
Dallas Love Field
Southwest Airlines*
Airport Code PNS CSG AGS GSP CLT CAE CHS SAV TLH
Airport Location Pensacola, FL Columbus, GA Augusta, GA Greensville/Spartaburg, SC Charlotte, NC Columbia, SC Charleston, SC Savannah, GA Tallahassee, FL
Table 1.5: Airport Codes and Corresponding Locations for Figure 1.2
Out-of-State Hubs for Dominant Carriers • • • • •
Chicago’s O’Hare International Airport (ORD) Miami International Airport (MIA) John F. Kennedy International Airport (JFK) Los Angeles International Airport (LAX) Baltimore/Washington International Thurgood Marshall Airport (BWI) • Chicago Midway Airport (MDW) • Las Vegas’s McCarran International Airport (LAS) • Phoenix Sky Harbor International Airport (PHX)
Table 1.6: Hub Airports in Texas
George Bush Continental • Newark Liberty International Airport (EWR) Intercontinental Airlines • Cleveland Hopkins International Airport (CLE) Airport (IAH) Houston Hobby Southwest • Same as listed above, for Southwest Airlines Airport (HOU) Airlines* *While Southwest Airlines’ business model is to operate as a direct-to-city carrier (rather than as a hub-and-spoke carrier), they have a large number of flights with connecting opportunities at these asterisked airports.
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Total Costs: Fixed vs. Variable, and Short Run vs. Long Run As suggested earlier in this chapter, fixed costs are costs that do not change with the level of output (such as VMT along a new highway). In other words, fixed costs are independent of the output: they must be paid even if no output is being produced. Capital infrastructure such as railroad track and highway lanes is typically considered a fixed cost, although some components can be variable costs, due to variations in the number of tracks needed, runways used, gates at an airport, and/or lanes along a highway. Variable costs are costs that do change (at least somewhat) with the level of output, including fuel used, system operators paid, and vehicle maintenance fees. Variable costs are avoidable; lowered production means lower variable costs. The total cost of producing goods and services is the sum of all fixed and variable costs, as shown in Figure 1.3. Fixed and variable costs are often differentiated by one’s timeframe of reference. For instance, a cost can be fixed in the short run but variable in the long run. Essentially, there are no fixed costs in the long run.
Total cost
Figure 1.3: Variable and Fixed Costs
Variable cost
Costs
Fixed cost
Output Quantity
Table 1.7 summarizes several key variable and fixed costs associated with highway transportation (Small & Verhoef 2007), as borne directly by highway users and highway providers (consumers and producers). This table has other columns and rows, as added in Section 1.3 of this chapter, relating to external costs, borne by others.
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Variable Costs (1) Operating and maintenance (2) Vehicle capital (3) Travel time (4) Schedule delay and unreliability (5) Crashes (6) Government services Fixed Costs (8) Roadway (9) Parking Average Total Costs (not including externalities)
Average Private Cost ($/mile) $0.141/mile 0.170 0.303 0.093 0.117 0.005
Table 1.7: Variable and Fixed Short-Run Costs of Automobile Travel ($ per Vehicle-Mile) (Small & Verhoef 2007)
0.016 0.007 $0.852/mile
*All costs measured in US$ per vehicle-mile in 2005 prices.
Concept Example: Fleet Vehicle Operating Costs Researchers have modeled TxDOT fleet operating costs as both fixed and variable, with fixed costs including vehicle depreciation, financing, and insurance, and variable costs including vehicle repairs, maintenance, and fuel.
Opportunity Costs Opportunity cost is the lost value/benefit of an investment or activity’s “next-best” (foregone) alternative. These benefits may be monetary or non-monetary in nature (such as lost time or other valued impacts from the missed opportunity). One way to think about opportunity costs is in the context of shadow prices, which give the “cost” (or reduced objective function value) of a constraint on one’s choices. Another type of opportunity cost is implicit cost, expressed in transportation terms as the value of travel time (described later in this section).
Shadow Prices In a broad sense, shadow prices are used to measure changes in a situation’s outlook resulting from “any marginal change in the availability of commodities or factors of production” (Squire 1975, p. 26). For our purposes, shadow prices determine how an objective value (some measure of benefit or cost) is impacted by altering some constraining factor by one unit. (More technically, the shadow price is equal to the Lagrange multiplier on a given constraint at the point of optimal solution.)
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Chapter 6 provides more details of shadow pricing.
Chapter 1
Concept Example: Shadow Price In transportation, the objective may be user benefits that are to be maximized, but are constrained by factors like the number of busses available in a transit fleet. The shadow price of an additional bus in the fleet is the change in total user benefits that follows an increase in the fleet. Similarly, shadow prices can be applied to a minimization problem: manipulating a constraint on total costs, such as reducing a maintenance project’s roadway resurfacing time by one day, will result in total cost reductions. Even if reducing rehabilitation time on a road by one full day is not realistically possible, the shadow price provides insight into the value of this additional time.
Implicit Costs Essentially, implicit cost is the value of an owned asset, whether physical (such as a machine) or temporal (such as a person’s time). Such assets could be used in other, productive pursuits (such as a truck serving another freight trip or a person enjoying more time with family). A DOT’s accounting expenditures do not capture the opportunity costs of using goods, labor, or time that the DOT does not pay for directly, such as when it uses a machine or property that it purchased in the past. The DOT’s implicit cost is the cost of the machine at present market prices. Another example is when a person is delayed on a congested roadway; the traveler’s implicit cost is his/her value of time. TxDOT’s office buildings and the land they occupy can be considered implicit costs to the department, because the current use of office or land space is not available for renting or selling to others. Transportation Application: Travel Time and Reliability The value of travel time (VOTT) is a type of implicit opportunity cost. Everything else constant (such a total number of trips to and from various activity sites), a decrease in travel time (an increase in travel time savings) reduces a project’s opportunity cost of the project; and an increase increases the project’s opportunity cost. Figure 1.4 breaks out the components of VOTT. Wage Rate
Value of Enjoyment of Work
Value of Travel Time
Figure 1.4: Components of VOTT
Value of Enjoyment of Travel
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VOTT is the amount of money a traveler is willing to pay for time savings. In more technical terms, VOTT is the marginal rate of substitution of money for travel time (while keeping a traveler’s overall happiness or utility constants). The variation in VOTT and value of reliability (VOR, discussed below) across a population of travelers can significantly affect project evaluations because some travelers are willing to pay more than others to save travel time (thanks to having more income or tighter time constraints, for example). Estimating the Value of Travel Time Estimates of VOTT for individual travelers vary widely, depending on where and how the data are collected and the methods of analyzing the data. One method of collecting data regarding VOTT is to use traveler survey data to estimates how much a traveler is willing to pay for time savings. For example, revealed-preference surveys ask what travelers actually do and pay, while stated-preference surveys ask what they would most likely do and pay. Thanks to various data regression techniques, analysts can hone in on distributions of VOTT across survey respondents. Because VOTT estimates emerge from willingness-to-pay (WTP) considerations, higher-income individuals regularly exhibit higher VOTTs than lower-income individuals. To recognize differences among travelers, more than one estimate of VOTT can be used when assigning trips to the network for determining routes and then a project’s implicit costs and benefits. Using a variety of traveler classes is especially important when anticipating the impacts of tolling policies. For instance, higher-income travelers may be more able and willing to pay a toll at congested times of day. If everyone had the same VOTT, most variable tolling policies (to reflect congestion externalities) would generally fail to improve social welfare. Thanks to variation or “heterogeneity” in the traveling population, thoughtfully designed variable toll policies can improve overall traveler welfare (Verhoef & Small 1999).
Resource An extensive body of research focuses on valuing the value and implicit cost of travel time. See Zamparini and Reggiani’s 2007 literature survey.
See the In-Depth Look section at this chapter’s end for other VOTT methods and estimates.
In their guidance for economic analysis of federal transportation projects and policies, the USDOT recommends hourly values of travel time (in 2000 U.S. $) of $10.60– $21.20 for local surface modes of travel, $14.80–$21.20 for intercity travel, and $18.80 for truck driver travel, both local and long distance (USDOT 2003). Of course, a traveler’s VOTT varies by other details of the trip as well. If one is late to an important meeting or the airport, VOTTs can run very high, even for low-income travelers. Reliability of Travel Times In addition to persons valuing the (average) time they spend traveling, they also value reliability or the lack of uncertainty in travel times. Transportation projects that increase reliability offer an important benefit; those that decrease reliability create a cost. Most theories explaining aversion to unreliable travel are based on costs of unexpected arrival times at work, which are greater for being late than for being early. Estimates of the VOR, as with VOTT, vary from $10.20 to $32 per hour of late or Costs and Benefits
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early arrival (Brownstone & Small 2005). VOR may be estimated with WTP values determined from revealed- or stated-preference surveys. In transportation settings, implicit costs can be the majority of travel’s economic costs. Thus, assessing delay’s implicit costs when planning transportation projects and policies is an essential calculation.
See the In-Depth Look section at this chapter’s end for more VOR methods and estimates.
Opportunity Cost Example: Cost of Delay Delays can increase a project’s cost considerably, during both planning and construction. These additional costs may not always be obvious, but quantifying delay costs is useful in prioritizing resources for allocation. Perhaps the most apparent cost of transportation project delays is the cost of redirecting traffic around construction zones. The Kentucky Transportation Center identified three distinct categories of costs associated with construction: vehicle operating costs, user delay costs, and crash costs. • Vehicle operating costs are simply the physical costs involved with redirecting traffic, including the cost of additional fuel consumption from vehicles changing speeds, idling, or taking longer detours. While these costs may be the smallest portion of project delay costs, they can be computed more exactly. • User delay costs, the cumulative value of the additional time road users must spend to detour from a construction area, are more difficult to quantify. Research suggests values of approximately $16 per automobile-hour and $28 per truckhour (2010 dollars). In 2011, TxDOT used values of travel time of $20.35 per passenger car hour and $29.71 per truck hour for calculating road user costs. While some debate exists about these exact values, the total cost will remain high even if smaller values are assumed (Rister & Graves 2002). • Crash costs involve the average cost of a crash and the increased likelihood of a crash occurring due to construction. This cost, though significant, varies widely from one project to another. While all three of these costs will be present in any construction project, delays can greatly increase the total cost by extending the time-period over which they act. Another way in which project delay costs accrue is in the opportunity cost of waiting. When a project is put off for future construction, the problems the construction intended to alleviate remain, and their costs accrue.
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Texas Example Costs of Delay The major at-grade railroad junction in Fort Worth, near Tower 55, results in sizable delay costs. For several years, plans have been made to separate the massive east/west and north/south freight flows so that both may continue unimpeded. Presently, however, the tower still represents a bottleneck in the state’s rail network. “Due to the volume of trains, each train must come to a complete stop prior to crossing. The average wait time is 15 minutes, with 90-minute delays during peak period. Long freight trains with lengthy wait times at Tower 55 are responsible for several negative impacts to the region,” notes the North Texas Council of Governments (NCTCOG 2011). Postponing the start of this project not only means additional costs for freight companies from the delays, but also greater environmental costs (air and noise pollution) caused by keeping trains (and then waiting cars and trucks) at a stand-still in the DFW Metroplex. For these reasons, contractors are sometimes granted early-finish bonuses (for completing a facility prior to its originally scheduled completion date). For example, the contract for an emergency bridge replacement near Toyah, Texas included a $10,000 per day early-finish bonus for the contractor (Smith 2011). Extreme rainfall in the rural region had caused the IH-20 bridge to fail, prompting a detour 20 miles longer than the original route, and demanding immediate DOT and contractor actions. Another serious cost of delaying a project is the uncertainty it invites. If the price of raw materials rises significantly, overall project costs can become unmanageable. For example, the cost of constructing the Oakland Bay Bridge in California more than doubled (from $2.6 billion to around $6 billion), largely due to an increase in the price of steel (Diesenhouse 2005). The lead-time to buy materials also changed from 3 to 8 months, causing further delays. The effect of the sudden shortage in steel caused many projects across the country to either temporarily halt or stop altogether. Obviously, this cost can be computed accurately only in retrospect, but planners must remain mindful of its possibility. Traditionally, the costs of delaying a project are not considered when choosing between different alternatives. However, these costs can be useful in determining the priority ranking, especially because putting something off for the future can actually be far more expensive than doing it now.
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1.3 External Costs and Benefits The provision and pursuit of transportation regularly entails external costs and sometimes external benefits, as borne by those not making the travel or transportinfrastructure-provision decision. These so-called “externalities” include the annoyance of highway noise, harm to adjacent properties and bystanders during crashes, and the visual and health impacts of air and water pollution. Reductions in these external costs (due to provision of sound walls, low-noise pavements, cleaner vehicles, and safer roadways) can be considered external benefits. External costs and benefits are reflected in comprehensive cost-benefit analyses (such as TxDOT’s Project Evaluation Toolkit or PET) to estimate the overall economic value and social welfare impacts of transportation projects and policies. Table 1.8 presents an extended version of Table 1.7 to show not just variable vs. fixed costs, but privately borne vs. total social costs (internal and external) during a typical commute trip during a peak time of day on a congested U.S. network. This table also shows marginal versus average costs for adding one more mile to one’s trip, versus dividing total commute cost by total commute VMT. It is interesting to see how high travel time costs are in comparison to other costs, and how high the marginal costs of time and (un)reliability are. Vehicle capital, O&M, and crash costs are also quite high. At the end of the day, one passenger-vehicle-mile costs travelers and society at large on the order of $1, with external costs (social minus private costs) accounting for about 30% of the total. Type of Cost
Private (Internal) Average
Social (Internal + External) Average Marginal
Variable Costs Costs borne mainly by highway users (1) Operating and maintenance $0.141/mile 0.141 (2) Vehicle capital 0.170 0.170 (3) Travel time 0.303 0.303 (4) Schedule delay and unreliability 0.093 0.093 Costs borne substantially by non-users (5) Crashes 0.117 0.140 (6) Government services 0.005 0.019 (7) Environmental externalities 0 0.016 Fixed Costs (8) Roadway 0.016 0.056 (9) Parking 0.007 0.281 $0.852/mile $1.219/mile Total Costs
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0.141 0.170 0.388 0.172
Table 1.8: Variable and Fixed Short-Run Costs of Automobile Travel (Small & Verhoef 2007)
0.178 0.019 0.016
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Transportation Application: Safety Impacts Motor vehicle use affects not only the road, air, and water systems, but also the safety of those in the vehicles and nearby. Transportation projects or polices can either increase or decrease the number of crashes occurring, and their severity, resulting in costs or benefits to travelers and society at large. Vehicle owners buy insurance to cover many crash costs, but auto insurance is typically a flat fee, rather than per-mile driven, and many costs are not recovered from insurers (such as delays in lanes blocked by crashed vehicles, deployment of emergency personnel, and the pain and suffering of crash victims). Crash cost allocation is complex due to the nature of shared responsibility in a crash. According to the USDOT, just “about one-quarter of the cost of crashes is paid directly by those involved, while society in general pays the rest,” suggesting that external costs are substantial. In 2000, driver-funded insurance companies paid about half the cost of all U.S. highway crashes (BTS 2003). Figure 1.5 breaks down payments for motor vehicle crashes by source.
Figure 1.5: Estimated Sources of Payment for Motor Vehicle Crashes (2000 US$) (Source: BTS 2003)
The National Highway Traffic Safety Administration’s Economic Impacts of Motor Vehicle Crashes 2000 (Blincoe et al. 2002) provides rigorously estimated crash costs. Average total market costs of lost productivity, medical services, travel delay and property damage per crash range from $2,762 per no-injury crash to $977,208 per fatal crash. These values include market costs, such as lost productivity, medical services, travel delay, and property damage, but they do not include non-market factors, such as the value of life, pain and suffering, or values based on WTP in order to avoid collisions. Other crash valuations may consider WTP measures, which are based on the price that Costs and Benefits
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a person is willing to pay for a marginal increase in safety. A person may be willing to pay millions of dollars to save his or her own life, but the implied value of life based on reducing the probability of serious injury or death via air-bag purchase and installation tends to be lower—and highly variable. For example, one study by the European Conference Ministers of Transport (2000) found that a person’s past experience of a crash increased his/her average stated WTP by a hundred-fold (i.e., by 10,000%). Furthermore, WTP was found to vary with respondent age and household income, with those near 40 years of age placing the highest value on human life. Table 1.9 presents values based on WTP from National Safety Council estimates. Crash Severity Death Incapacitating Injury Non-Incapacitating Injury Possible Injury Property Damage Only
WTP (Per Injured Person) $4,200,000 $214,200 $54,700 $26,000 $2,400 (no injury)
Table 1.9: WTPBased Motor Vehicle Crash Cost Estimates (Source: NSC 2010)
Note that Table 1.9’s crash costs are per injured person, rather than per crash. Oftentimes, more than one person is injured during a crash, so analysts intending to use crash costs based on WTP measures should account for the total number of persons injured. The California Benefit/Cost model assumes an average of 1.15 fatalities per fatal injury crash and an average of 1.49 injuries per injury crash (Caltrans 2010). Transportation Application: Air Quality Impacts Mobile source emissions, such as carbon dioxide (CO2), oxides of nitrogen (NOx), reactive organic gases or volatile organic compounds (ROG and VOC), fine particulate matter (PM2.5 and PM10), carbon monoxide (CO), and various other hazardous air pollutants, constitute a major fraction of human-caused emissions and are responsible for air quality concerns in hundreds of U.S. cities and counties. Emissions costs can be broadly associated with the costs of damages caused by the pollutants to human, plant, and animal health, as well as damage to buildings and ecosystems. Another influential factor is whether to focus on the cost to control or reduce such emissions, rather than compensate for harm done. Each pollutant carries different costs, and human health costs are dominated by particulate matter. The oxides of nitrogen and sulfur, NOx and SO2, form acid rain, which has been associated with ecosystem damage as well as degradation of structure exteriors and the built environment. Ozone exposure in humans is associated with breathing difficulty, asthma, airway, and lung inflammation and lung damage. Ozone deposits on plants reduce the efficiency of photosynthesis and have contributed to 90% of air-pollution-associated U.S. crop losses (Murphy & Delucchi 1998). Carbon dioxide (CO2), a greenhouse gas, contributes to climate change. Chapter 1
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A second variable affecting emission costs is their location, or more specifically, the potential for exposure of humans, plants, animals, and structures. Meteorological conditions as well as other factors (such as activity patterns) significantly influence the impact that the geographic location of emissions will have on human exposure and health outcomes. Two additional, somewhat subjective variables impact emissions costs: 1. The value placed on human life and health. No universally accepted value has been determined, but many studies now include the economic (and opportunity) costs of lost productivity. 2. The range of impacts to natural resources and the built environment. Models like the EPA’s MOBILE 6.2 can quantify the costs of air pollution impacts by assessing the change in quantity and mix of emissions when a transportation project effects changes to vehicle-miles, vehicle-hours, and vehicle-trips. Emissions rates depend on transportation facility type (freeway, arterial, local road, or ramp), vehicle speed, year of analysis, vehicle type, and age. Once the emission rates are estimated, a dollar value can be applied per unit of emissions to assess air pollution costs and benefits (McCubbin & Delucchi 1996 and Mailbach et al. 2008). Emissions costs have been estimated (in 2010 $) as $2,900 to $5,800 per ton of hydrocarbon, $70 to $140 per ton of CO, $620 to $5,600 per ton of NOx, $620 to $6,400 per ton of SO2, and $9,300 to $830,000 per ton of PM2.5. As alluded to earlier, these costs depend on population density (and thus level of human exposure) and meteorology, as well as the local population’s wealth and income (and thus WTP to protect one’s lungs, views, and property). Transportation Application: Noise Impacts Traffic noise is a nuisance. Beyond that, sustained exposure to traffic noise can cause hearing impairment and sleep disturbance, increasing stress levels in those living and working nearby. In addition to human health impacts, studies indicate that traffic noise reduces the price of nearby homes (Gamble et al. 1974, Lewis et al. 1997, Bowes & Ihlanfeldt 2001). Numerous factors influence levels of traffic noise, including vehicle type, engine type, traffic speed, pavement type and condition, and presence of noise barriers. For examples, passenger cars are generally quieter than buses and motorcycles. Within passenger cars, electric and hybrid vehicles are generally quieter than gasoline and diesel vehicles. Due to the wide range of inputs affecting noise, it is difficult to quantify noise costs for projects in which the change in traffic noise is small. However, for larger projects in which traffic noise changes are significant, hedonic price models can estimate the effects of traffic noise on property values. One study recommends a depreciation of 0.5% in property value per decibel increase in traffic noise above 50 dB. A 2009 online review of a variety of other research suggests the following variations by vehicle type:
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Chapter 8 discusses hedonic price models, used to estimate traffic noise levels.
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• Automobile noise costs 1.3¢ per mile on urban roads and 0.7¢ per mile on rural roads, on average. • Electric car noise costs 0.4¢ per mile on urban roads and 0.4¢ per mile on rural roads. • Motorcycle noise costs 13.2¢ per mile on urban roads and 6.6¢ per mile on rural roads. Transportation Application: Ecological Impacts Wildlife habitat and water quality can be impacted (and mitigated) by transportation improvements in many ways. Habitat disruptions (particularly those of endangered species) are difficult to monetize and are typically mitigated on a case-by-case basis according to level of impact. Vehicles deposit rubber, oil, and other polluting particles on pavements. Rain washes these pollutants over impervious roadway surfaces into nearby areas, causing groundwater and/or wetlands contamination. Additional of new impervious surfaces makes the ground less permeable and increases runoff. Hydrologic impact models can predict increased storm water management costs. As with noise impacts, hedonic price models can also be used to monetize water quality impacts.
1.4 Summary This chapter discusses and quantifies key transportation cost and benefit concepts. While some costs are relatively easy to observe and anticipate, including fixed capital costs (such as initial construction expenses), many are difficult to observe or quantify, but are very significant for economic evaluations. Unseen costs include opportunity costs and indirect costs, which lead to concepts like shadow prices and the significance of travel time estimation, reliability, and traveler welfare. Many concepts in this chapter have overlapping definitions and applications. For instance, capital and operating costs tend to be fixed versus variable, and can change depending on the analyst’s timeframe of reference. Capital costs tend to represent fixed initial costs that may take place at specific points in time (especially at a project’s start), while operating costs include ongoing expenses from repeated maintenance, fuel and labor use. The idea of fixed versus variable costs and benefits relates to short-run versus longrun horizons (with all costs and benefits being variable in the long run). Other key concepts for project costing and economic impact assessments include marginal versus average costs, economies of scale, and returns to scale. An appreciation of external costs and benefits is critical to understanding the environmental, delay-related and safety impacts of transportation activities. To keep things in perspective, consider that Americans spend roughly $1 trillion per year in travel time, and lose on the order of $300 billion per year to highway crashes, along Chapter 1
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with roughly $100 billion per year to congestion (from time and fuel losses) and $50 billion to vehicle emissions. Such metrics help transportation planners, engineers and policymakers gain a sense of how important transportation is to economic systems, quality of life, and the long-term health and safety of current and future generations. Optimal decision-making in the transportation arena requires a comprehensive and long-term perspective on a variety of project and policy alternatives.
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1.5 An In-Depth Look Estimating Value of Travel Time (VOTT) A common method for VOTT estimation involves collecting travel time and travel cost data for various travelers’ mode options and their chosen alternative(s). A logit choice model is calibrated from such data, and the ratio of its utility function’s parameters on travel time and cost gives the VOTT estimate (VOTT = [marginal utility of time]/[marginal utility of money] = [$/minute] or [$/hour]). A wide range of VOTTs has been calculated by various researchers:
See Chapter 8 for details on utility functions and logit choice models.
• Levinson and Smalkoski (2003) estimated U.S. heavy-duty truck VOTTs to average $49.42 per hour. • Brownstone and Small (2005) estimated those of morning commuters in the Los Angeles area along routes SR-95 and I-15 to generally lie between $20 and $40 per hour, using revealed preference surveys. These values are nearly two times greater than the hypothetical values emerging from stated preference surveys. • The Oregon DOT (2006) estimated VOTTs of $16.31 per hour for the average auto, $20.35 per hour for light trucks, and $29.50 per hour for heavy trucks. • Schrank and Lomax (2009) used $15.47 per hour for autos and $102 per hour for commercial vehicles in their regional congestion cost calculations. • Litman (2009) recommends that paid travel be valued at 150% of the wage rate, commuting and congested travel be valued at 50% of the wage rate for drivers’ and 35% of passengers’ wage rates, uncongested travel be valued at 25% of wage rate, and pleasurable travel be valued at $0/hour. • Zamparini and Reggiani (2007) conducted a meta-analysis of VOTT studies for Europe and North America and estimated VOTT to average 82% of the wage rate, with an average of 68% for North American travelers. They estimated travel time valuations at 55% of the wage rate for commuting, 146% for business travel, and 60% for other activities.
Estimating Value of Reliability (VOR) Unreliability is typically measured as the standard deviation in travel times (from day to day, and minute to minute) on a set route at a given time of day. Accurately estimating the VOR and transportation projects and policies’ reliability impacts is a fairly new research topic in transportation. Reliability measurement requires detailed data because many observations over several days of speed and traffic settings are needed to assess travel time variability on a roadway or route. Various VOR estimates have been developed, including the following: • Using data for travel on the variably tolled SR-91 in Southern California, Brownstone and Small (2005) estimated travelers’ VOR to be $12 to $32 per hour of standard deviation in arrival time, or roughly 95% to 145% of the corresponding VOTT on those links (the VOTT was between $20 and $40 per Chapter 1
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hour). They also estimated a much higher VOR for women than men—roughly twice as high. They hypothesized that because women have more child-care responsibilities, their schedule flexibility is limited, making them more likely to choose the variably tolled SR-91 road. • Litman’s (2009) literature review suggests a VOR range of $10.20 to $15.60 per hour of standard deviation in arrival time.
Cost Functions and Returns to Scale in Production Determining returns to scale involves an analysis of a transportation sector’s production functions. Production functions are simply equations for predicting the quantity of output as a function of all inputs’ quantities, xi (where i indexes input type, such as fuel, employee hours, and vehicles). Cost functions are similar to production functions in that they predict the cost of production as a function of the output (Q) and the prices of all inputs, where pi or wi is the (unit) price or wage of input xi. Production functions can take on a variety of forms, as Table 1.10 illustrates. Production Functions Constant Elasticity of Substitution (CES) Leontief (production function only)
Cobb-Douglas
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General Form (where xi=quantity of input i; αn= indicates how input i affects the output, y) =
+
= (
1 1,
=
+ …+
2 2,…,
)
…
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Parameters to Estimate “p” is the elasticity of substitution parameter. The actual elasticity of substitution equals 1/(1-p). “αi” is the amount of industry output i needed for production of one unit of y “A” indicates the general scale of production. “αi” are elasticities of production with respect to each input.
Table 1.10: Examples of Production Function Forms
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1.6 References Barnes, G. & Langworthy, P. (2003) Per Mile Costs of Operating Automobiles and Trucks. Humphrey Institute of Public Affairs, University of Minnesota. Available online at http://www.hhh.umn.edu/centers/slp/pdf/reports_papers/per_mile_costs.pdf. Bates, J., Polak, J., Jones, P., & Cook, A. (2001) The Valuation of Reliability for Personal Travel, Transportation Research Part E, Vol. 37, 191–229. Blincoe, L., Seay, A., Zaloshnja, E., Miller, T., Romano, E., Luchter, S., & Spicer, R. (2002) The Economic Impact of Motor Vehicle Crashes 2000. National Highway Traffic Safety Administration. Bowes, D. R. & Ihlanfeldt, K. R. (2001) Identifying the Impacts of Rail Transit Stations on Residential Property Values. Journal of Urban Economics 50, 1–25. Boyer, K. D. (1998) Principles of Transportation Economics. New York: Addison Wesley Longman. Brownstone, D. & Small, K. A. (2005) Valuing Time and Reliability: Assessing the Evidence from Road Pricing Demonstrations. Department of Economics, University of California, Irvine. California Department of Transportation (Caltrans) (2010) Life Cycle Benefit Cost Analysis Model (Cal B-C). Available online at http://www.dot.ca.gov/hq/tpp/offices/ote/benefit.html. Cohn, E. (1992) Returns to Scale and Economies of Scale Revisited. Journal of Economic Education 23(2),123–24. Diesenhouse, S. (2005) Commercial Real Estate; Rising Steel Prices Force Changes in Construction Plans. New York Times. available online at http://query.nytimes.com/gst/fullpage.html?res=9905EEDF133EF930A25757C0A9639C 8B63&pagewanted=1. European Road Safety Observatory (2006) Cost-Benefit Analysis. European Commision, Directorate-General Transport and Energy. Available online at http://www.dacotaproject.eu/Links/erso/knowledge/Fixed/08_measures/cost_benefit_analysis.pdf. Florida Department of Transportation (2012) Generic Cost per Mile Models. Available online at ftp://ftp.dot.state.fl.us/LTS/CO/Estimates/CPM/summary.pdf Gamble, H., Sauerlender, O., & Langley, J. (1974) Adverse and Beneficial Effects of Highways on Residential Property Values. Transportation Research Record 508, 37–48. Gelles, G. M. & Mitchell, D. W. (1996) Returns to Scale and Economies of Scale: Further Observations. Journal of Economic Education 27(4), 259–261. Gomez-Ibanez, J., Tye, W. B., & Winston, C. (1999) Essays in Transportation Economics and Policy: a Handbok in Honor of John R. Meyer. Washington, D.C. The Brookings Institution. Heiner, J. and Kockelman, K. (2005). Costs of Right-of-Way Acquisition: Methods and Models for Estimation. Journal of Transportation Engineering, 131(3), 193–204. Chapter 1
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Intergovernmental Panel on Climate Change (IPCC) (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II, and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland. IPCC. Available online at http://www.ipcc.ch/publications_and_data/ar4/syr/en/main.html. Janke, B., Gulliver, J. S., & Wilson, B.N. (2011) Development of Techniques to Quantify Effective Impervious Cover. Center for Transportation Studies, University of Minnesota. Available online at: www.cts.umn.edu/Publications/ResearchReports/reportdetail.html?id=2058. Kanafani, A. & Ghobrial, A. A. (1985) Airline Hubbing—Some Implications for Airport Economics, Transportation Research Part A 19(1), 15–27. Levinson, D. & Smalkoski, B. (2003) Value of Time for Commercial Vehicle Operators in Minnesota. TRB International Symposium on Road Pricing, University of Minnesota. Available online at http://nexus.umn.edu/Papers/TruckValueOfTime.pdf. Lewis, C. A., Buffington, J. L., Vadali, S. R., & Goodwin, R. E. (1997) Land Value and Land Use Effects of Elevated, Depressed, and At-Grade Level Freeways in Texas. TxDOT Research Report 1327–2. Litman, T. (2009) Transportation Cost and Benefit Analysis (2nd ed.). Victoria Transport Policy Institute. Available online at http://www.vtpi.org/tca/. Maibach, M., Schreter, C., Sutter, D., van Essen, H. P., Boon, B. H., Smokers, R., Schroten, A., Doll, C., Pawlowska, B., & Bak, M. (2008) Handbook on Estimation of External Costs in the Transport Sector, Delft, the Netherlands. Available online at http://ec.europa.eu/transport/sustainable/doc/2008_costs_handbook.pdf. McCubbin, D. & Delucchi, M. (1996) The Social Cost of Health Effects of Motor-Vehicle Air Pollution. Institute for Transportation Studies, University of California, Davis. Available online at http://www.its.ucdavis.edu/publications/1996/UCD-ITS-RR-96-03(11).pdf. Murphy, J. & Delucchi, M. (1998) A Review of the Literature on the Social Cost of Motor Vehicle Use in the United States. Journal of Transportation and Statistics, Vol. 1, No. 1, Bureau of Transportation Statistics, January 1998, 15–42. National Safety Council (NSC) (2010) Estimating the Costs of Unintentional Injuries. Statistics Department, National Safety Council, Itasca, Illinois, and Children’s Safety Network, Economics and Insurance Resource Center, Institute for Research and Evaluation. Available online at http://www.nsc.org/news_resources/injury_and_death_statistics/Pages/EstimatingtheCost sofUnintentionalInjuries.aspx. North Central Texas Council of Governments (NCTCOG) (2011) Streamlined Project Delivery—Tower 55 Projects. Available online at http://www.nctcog.org/trans/spd/freightrail/tower55/index.asp. Oregon Department of Transportation (2006) The Value of Travel Time: Estimates of the Hourly Value of Time for Vehicles in Oregon 2005. Available online at http://www.oregon.gov/ODOT/TD/TP/docs/publications/ValueTravelTime2005.pdf.
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Pearce, D.W. & Markandya, A. (1989) Environmental Policy Benefits: Monetary Valuation. Organisation for Economic Co-operation and Development. Pike, R. W. (2001) Optimization for Engineering Systems. Louisiana State University. Available online at http://mpri.lsu.edu/bookindex.html. Polzin, S., Xuehao C., & Raman, V.S. (2008) Exploration of a Shift in Household Transportation Spending from Vehicles to Public Transportation, Center for Urban Transportation Research. Available online at http://www.nctr.usf.edu/pdf/77722.pdf. Risbey, T., de Silva, H., & Tong, A. (2007) Road Crash Cost Estimation: A Proposal Incorporating a Decade of Conceptual and Empirical Developments. Bureau of Transport and Regional Economics, Department of Transport and Regional Services, Australian Government. Available online at http://www.bitre.gov.au/publications/96/Files/ATRF07_Road_Crash_Cost.pdf. Rister, B. & Graves, C. (2002) The Cost of Construction Delays and Traffic Controls for LifeCycle Cost Analysis of Pavements. Kentucky Transportation Center. available online at http://www.ktc.uky.edu/Reports/KTC_02_07_SPR197_99_SPR218_00_1F.pdf. Samuelson, P. A. & Nordhaus, W. D. (2004) Economics (18th ed). New York, New York: The McGraw-Hill Campanies, Inc. Schrank, D. and Lomax, T. (2009) 2009 Urban Mobility Report. Texas Transportation Institute. Available online at http://tti.tamu.edu/documents/mobility_report_2009_wappx.pdf. Small, K. & Verhoef, E. (2007) The Economics of Urban Transportation. New York, Routledge. Small, K. & Yan, J. (2001) The Value of “Value Pricing” Roads: Second-Best Pricing and Product Differentiation. Journal of Urban Economics, 310–336. Small, K. A. (1992) Urban Transportation Economics. Harwood Academic Publishers. Smith, S. (2011). Personal communication, November 21, 2011. Squire, L. & Van Der Tak, H. G. (1975) Economic Analysis of Projects. International Bank for Reconstruction and Development, Washington, D.C.: Johns Hopkins University Press. Texas Department of Transportation (2011). Value of Time and Road User Costs. Memo to District Engineers, May 11, 2011. Truett, L. J. & Truett, D. B. (1990) Regions of the Production Function, Returns, and Economies of Scale: Further Considerations. Journal of Economic Education 21(4), 4113–19. US Department of Transportation (USDOT) (2003) Revised Departmental Guidance: Valuation of Travel Time in Economic Analysis. Office of the Secretary of Transportation, U.S. Department of Transportation. Available online at http://ostpxweb.dot.gov/policy/Data/VOTrevision1_2-11-03.pdf. US Department of Transportation, Bureau of Transportation Statistics (BTS) (2003) Transportation Statistics Annual Report. Available at: http://www.bts.gov/publications/transportation_statistics_annual_report/2003/html/chapte r_02/economic_impacts_of_motor_vehicle_crashes.html.
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US Department of Transportation, Federal Highway Administration (FHWA) (2007) Major Project Program Cost Estimating. Available online at: http://www.fhwa.dot.gov/ipd/pdfs/project_delivery/major_project_cost_guidance.pdf. US Environmental Protection Agency (EPA) (2009) Ozone and Your Health. Available online at http://www.epa.gov/airnow/ozone-c.pdf. US Environmental Protection Agency (EPA) (2011) 2011 U.S. Greenhouse Gas Inventory Report. United States Environmental Protection Agency. Available online at http://www.epa.gov/climatechange/emissions/downloads11/US-GHG-Inventory-2011Complete_Report.pdf. US Environmental Protection Agency (EPA) (2012) What is Acid Rain? Available online at http://www.epa.gov/acidrain/what/index.html. Verhoef, E. & Small, K. (1999) Product Differentiation on Roads: Second-Best Congestion Pricing with Heterogeneity under Public and Private Ownership. Department of Spatial Economics, Free University Amerstam, and Department of Economics, University of California at Irvine. Available online at http://www.tinbergen.nl/discussionpapers/99066.pdf. Wang, M. Q., Santini, D.J., & Warinner, S.A. (1995) Monetary Values of Air Pollutants in Various U.S. Regions. Transportation Research Record 1475, 33–41. Welter, D., Ates, M., Loftus-Otway, L., Matthews, R., & Harrison, R. (2009) Estimating Texas Motor Vehicle Operating Costs. Center for Transportation Research, University of Texas at Austin. Available online at http://www.utexas.edu/research/ctr/pdf_reports/0_5974_1.pdf. Zamparini, L. & Reggiani, A. (2007) Meta Analysis of the Value of Travel Time Savings: A Transatlantic Perspective in Passenger Transport. Springer Science & Business Media, LLC. Available online at http://www.springerlink.com/content/h663q51u448078x1/fulltext.pdf.
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Chapter 1
Chapter 2. Pricing of Transportation Services 2.1 Introduction ........................................................................................................................ 2-1 2.2 What is an Optimal Price? ................................................................................................. 2-1 Maximizing Profit ................................................................................................................ 2-3 Maximizing Social Benefits ................................................................................................. 2-4 Yield Management ............................................................................................................... 2-5 Price Discrimination ......................................................................................................... 2-5 Dynamic Yield Management ............................................................................................ 2-6 2.3 Roadway Pricing ................................................................................................................ 2-7 Short-Run Marginal Cost Pricing ........................................................................................ 2-7 User Vehicle Operating Costs (MCUser) ............................................................................ 2-7 Travel Time Costs ............................................................................................................. 2-7 Schedule Delay Costs ....................................................................................................... 2-8 Provider Service Costs ...................................................................................................... 2-8 Safety and Environmental Costs ....................................................................................... 2-9 Long-Run Marginal Pricing ................................................................................................. 2-9 Static vs. Dynamic Pricing ................................................................................................... 2-9 Second-Best Pricing ........................................................................................................... 2-10 Case 1: Not All Links in a Network Are Tolled ............................................................. 2-11 Case 2: Unable to Differentiate Price by User Group .................................................... 2-12 Implementing Roadway Pricing ........................................................................................ 2-13 Congestion Pricing .......................................................................................................... 2-13 Allocation of Joint and Common Costs .......................................................................... 2-15 VMT with Taxes ............................................................................................................. 2-17 Freight Movements ............................................................................................................ 2-18 Freight in Urban Areas ................................................................................................... 2-19 Freight Pricing Mechanisms ........................................................................................... 2-20 2.4 Road Pricing’s Impacts on Equity ................................................................................... 2-20 Measures to Improve Horizontal Equity ............................................................................ 2-21 Measures to Improve Vertical Equity ................................................................................ 2-21 Congestion Pricing: Is It Equitable? ............................................................................... 2-22 2.5 Summary .......................................................................................................................... 2-23 2.6 References ........................................................................................................................ 2-24
Key Terms
2.1 Introduction A crucial topic in understanding transportation markets, pricing is perhaps the most widely researched area within transportation economics. Transportation pricing refers to fees and financial incentives incurred by travelers, including (but not limited to) transit fares, cargo fees, fuel taxes, variable and flat rate tolls, parking fees, vehicle registration fees, and insurance payments. When the price of vehicles, parking, fuel, and transit fare change, travel activity patterns are affected.
ROH: rule of half CP: congestion pricing VOTT: value of travel time SRMC: short-run marginal cost LRMC: long-run marginal cost
Pivoting off of Chapter 1’s discussion of the costs and benefits of transport, this chapter explores the interaction of demand and supply, congestion and costs, and tolls and revenues. While air and freight modes rely largely on private markets to balance supply and demand, roadways are provided largely as a public service and rely on fuel taxes as the primary source of funding. Travelers are accustomed to paying a premium for airfare during peak holiday travel periods and paying extra for parking during special events, when demand is high. Yet the concept of tolls that rise and fall with traffic demand is relatively new. As the gap between fuel tax revenues and road system provision and maintenance costs widens, tolling is becoming a popular consideration (for example, IH 10 high-occupancy toll [HOT] lanes in Houston, SR 91 Express lanes in Orange County, VMT-based pricing in Oregon, etc.). Furthermore, pricing strategies can help society internalize the negative externalities of congestion, air pollution, noise, and crashes.
2.2 What is an Optimal Price? The appropriateness of a pricing strategy depends on the goal at hand. Is it to keep traffic flowing at 60 mph or to recover roadway maintenance and bridge replacement costs for the next 10 years? Is it to incentivize the purchase of hybrid and electric vehicles or to reduce the number of high emissions vehicles in urban areas? A scheme designed to maximize the profit of a private business in an unregulated environment differs greatly from one designed to maximize social welfare. In order to pick a good tax or toll strategy, agencies must define their objectives. Simplifying the case to a single mode, optimal pricing can still be viewed from multiple perspectives. In theory, each trip provides each user with some net benefit known as consumer surplus. Consumer surplus is the difference between the maximum price a consumer is willing to pay and the price he or she actually pays. The sum of all realized net benefits is the market’s overall consumer surplus. In markets with lots of consumers (travelers), consumer surplus can be represented by the area under the demand curve and above the market price, as shown in Figure 2.1 and computed using the integral of the demand curve out to the quantity (number of trips) consumed and above the cost of the trip in Equation 2.1. Chapter 2
2-1
Pricing of Transportation Services
Figure 2.1: Surplus and Demand and Consumer Surplus
=
( )
(2.1)
Concept Example: Consumer Surplus Assume vehicle operating and fuel costs are on average $40 for a trip between Austin and San Antonio, and average value of travel time is $12/hour for the 1.5-hour trip. Then the total cost per traveler for the trip can be approximated as $40 + ($12 × 1.5) = $58/ If, on average, the trip between Austin and San Antonio is worth $60 to the traveler, then the $2 difference between traveler cost and traveler’s valuation of the trip is an individual consumer’s surplus. If there are 40,000 travelers who make this trip on IH 35 each day, then the total consumer surplus (net travelers’ benefit) would be worth $80,000. Producer surplus, on the other hand, is the amount of benefit that producers gain by selling at a higher market price than the least price they would be willing to sell for. As seen in Figure 2.1, consumer surplus is maximized when the price is zero, while producer surplus is maximized when the price is at a maximum. The actual market price generally falls between these two extremes, and the sum of consumer surplus and producer surplus is called social welfare. Pricing of Transportation Services
2-2
Chapter 2
Transportation Application: Traveler Welfare Traveler welfare, sometimes called consumer or firm surplus, depends on direct user costs (such as fuel, tolls, fares, and vehicle maintenance), travel times (an opportunity cost), and the base attractiveness of various choices (time of day, destination, and mode). Savings in direct costs are direct benefits.
Estimation of Welfare Value Changes in traveler welfare can be estimated using the Rule of Half (RoH). Figure 2.2 shows the gain in traveler welfare (surplus) when travel cost falls. The benefit to existing users equals Area 2, while the benefit to new users equals Area 1. The RoH assumes a linear demand function between before and after demand points for each origin-destination pair. Travel Cost
Figure 2.2: Ruleof-Half Showing Changes in Consumer Surplus (in Shaded Areas) as Travel Price Falls (and Demand Rises)
Linear Approximation Old travel cost
2
1
New travel cost
Demand curve
Old travel demand
New travel demand
Travel Demand
Maximizing Profit Most private businesses wish to maximize profits, defined as the difference between total revenue (TR) and total cost (TC), as Equation 2.2 shows. =
−
=
∙
−
(2.2)
Here Pmkt is the market price and Y is the quantity of goods sold, like vehicles per day on a tolled road or seats on an airplane between El Paso and DallasFort Worth. Profit is maximized when the increase in total revenue generated by serving one additional trip is equal to the increase in total cost due to serving that one additional trip, or when marginal revenue (MR) is equal to the marginal cost (MC), as demonstrated in Equation 2.3. Chapter 2
:
(
∙ )
−
(
)
=
+
− 2-3
=
−
See Chapter 1 for a complete discussion total cost, marginal cost, and average cost.
= 0 (2.3) Pricing of Transportation Services
Because the demand for transportation is generally somewhat elastic, the higher the price charged, the fewer trips the consumers will make ( < 0), which translates into optimal profit-maximizing price marginal cost.
Maximizing Social Benefits From a transportation agency’s point of view, it is neither ideal to operate free of charge (transportation facilities not rationed by pricing will be rationed by congestion) nor to charge for maximum profit (as the goal of public institution is one of serving the community’s best interests). Public agencies are to put their customers (the public at large) first. In theory, maximizing net social benefits ensures that transportation system resources are used optimally. Agency revenues (a benefit) offset user charges (a cost), and total social welfare (SW) is computed as shown in Equation 2.4: =
+
=
−
+
(2.4)
By the law of diminishing marginal utility, the more trips a traveler makes between destinations A and B, the less he or she is willing to pay for each additional trip between those destinations. Across a market of potential consumers, the same downward sloping demand situation emerges, as marginal social benefit (or willingness to purchase) falls with quantity purchased. The optimal quantity served occurs when social welfare is at maximum, or when marginal social cost rises up to effectively cancel the marginal social benefit of the last unit purchased (i.e., that with the lowest marginal social benefit served), as expressed in Equation 2.5.
:
(
∙ )
−
(
)
+
(
)
=
+
−
−
=
−
=0
(2.5)
Thus, the optimal price to maximize social welfare equals the marginal cost of producing the last unit sold/served. In effect, marginal cost pricing ensures there is no subsidy or waste in transportation service provision and transportation service use. In general, marginal cost pricing for maximum social benefit (PSB) falls between maximum consumer surplus pricing (PCS=0) and maximum profit pricing (PP), as Figure 2.3 depicts.
Pricing of Transportation Services
2-4
Marginal cost pricing allows for an economically efficient system, where social benefits are maximized.
Chapter 2
Figure 2.3: Marginal Cost Pricing
Yield Management Due to the high capital cost of transportation investments, average costs tend to be higher than marginal costs, marginal cost pricing will result in financial losses for the operator. Fixed costs need to be recovered, which requires average-cost pricing, subsidies, or something called yield management. Frequently practiced by the airline industry, yield management involves price discrimination, as discussed below.
Price Discrimination
Yield management can involve various forms of legal price discrimination.
Price discrimination is the practice of a service provider varying the price of the same service for different users, provided that they cover their marginal costs. Generally, three kinds of price discrimination are related in economic textbooks: • First-degree price discrimination varies the price based on each individual user’s willingness or ability to pay. This is rarely practiced in reality. • Second-degree price discrimination varies the price based on the quantity sold. In transportation, this translates to price differentiation by either the number of trips or the length of the journey. For example, regular transit users can buy an unlimited use card each month or year. • Third-degree price discrimination varies the price based on the segment of the market or group of consumers. This is the most common form of price discrimination in transportation. Examples include first Chapter 2
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Pricing of Transportation Services
class versus economy class fares, express versus regular bus services, and senior citizens’ and student discounts. Boyer (1998) described the near-perfect monopoly of Michigan’s Mackinac Bridge, the only surface transportation route that connects the peninsulas. To recover the 1956 construction cost of $150 million, Boyer suggested different toll strategies that exemplify third-degree price discrimination: 1. Set a high standard toll targeted towards tourists and a discounted toll for local residents with residency certification. 2. Charge higher tolls on weekends (when tourists are more likely to use the bridge) and lower tolls on weekdays. 3. Offer lower tolls at off-peak periods to capture users. 4. Discount tolls on a particular day of the week, advertised locally in advance. 5. Distribute coupons through local media sources to capture pricesensitive residents.
Dynamic Yield Management Dynamic yield management is similar to price discrimination in that it results in variable pricing. However, it is more of a scarce resource allocation strategy, whereas price discrimination emphasizes revenue maximization. Concept Example: Dynamic Yield Management Consider the example of airlines raising the prices on a flight as the number of remaining seats falls. The airline then allocates scarce remaining capacity to those with a higher willingness to pay. Airlines also employ minor product differentiation in offering first, business, and coach class fares. Because first-class service costs an airline more than coach class service, this fare structure differs from strict price discrimination (where the same product is priced differently to distinct consumer groups). Dynamic yield management arguably provides a more efficient market structure because firms offering a variety of services can offer lower unit prices than those specializing in a single service. By allowing operators with high fixed costs (like high speed rail and airline operators) to operate at a financially feasible point, dynamic yield management retains a service that may otherwise be financially infeasible. The availability of the transportation service is still beneficial to all travelers, even if some pay more than others. The presence of competing carriers in all markets helps prevent monopolized pricing and excessive profits.
Pricing of Transportation Services
2-6
Chapter 2
2.3 Roadway Pricing Unlike most markets where price-setting firms provide all the economic resources (and thus bear all costs), transportation costs are shared among system users, the operators/owners, and non-users. For example, the total cost of highway transportation can be expressed as Equation 2.6: =
∙
+
∙
∙
+
+
+
(2.6)
+
where ∙ represents vehicle ownership and operating costs ( ) for all travelers ( ), costs such as gas, maintenance, insurance, licensing, financing, and registration, but excluding tolls. Here, ∙ ∙ represents the travel time costs, a function of travel time per trip ( ) and value of travel time ( ), for all travelers ( ). •
represents the fixed costs of roadway infrastructure for the government agency or other highway owner. Like other cost components, the capital cost of building roads varies significantly based on geography and roadway-specific features.
•
represents the variable costs of roadway infrastructure for the owner, including pavement maintenance, law enforcement, and emergency management.
•
represents the social costs borne by both users and non-users from crash risk.
•
represents the social costs borne by both users and non-users from air, noise, water, and other environmental impacts.
Short-Run Marginal Cost Pricing In short-run marginal cost (SRMC) pricing, all costs in Equation 2.6 are considered except the infrastructure cost, which is considered a fixed cost in the short term. Thus, the corresponding SRMC for each user can be expressed as Equation 2.7: =
+
∙
+
∙
+
+
+
(2.7)
Each component of the SRMC equation is discussed below.
User Vehicle Operating Costs (MCUser) For automobile travel, marginal vehicle operation and ownership costs )—such as tire wear and vehicle depreciation—are private costs borne ( by the users. Vehicle capital ownership depreciation and interest costs can be calculated by standard discounting techniques, as discussed in Chapter 6.
See Chapter 1 for a detailed discussion of fixed versus variable costs, plus ways to estimate the MCs of transportation.
Travel Time Costs The marginal cost of travel time ( ∙ ) accounts for travel time costs borne by the marginal traveler and any delays he or she imposes on others. The term represents the change in total system travel time due to the addition of one more vehicle, which depends on existing congestion levels in Chapter 2
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Pricing of Transportation Services
the system. The Bureau of Public Roads (BPR) function relating travel time to the volume-capacity ratio is typically used to estimate changes in average travel time on individual links in the roadway system ( − ). The overall change in link TT is thus ∙ − ∙ , as shown in Equation 2.8. =
(1+∝
)
(2.8)
For efficient or optimal travel decisions, users imposing delays on others should, in theory, pay for such costs, in addition to their own travel time costs. One researcher makes the following point: (E)ven though each user is simultaneously and instantly providing and consuming [his/her] own time…[he/she] is demanding an increase or decrease of other economic resources, namely, other individuals’ time. Unless that change in demanded resources is paid or rewarded for, the market will be not be socially efficient (Jara-Diaz 2007, p.117).
Wide variations are present in individual values of travel time (VOTTs). Because the value of travel savings typically is tied to wealth and wage rates, high-income individuals regularly exhibit VOTTs higher than those of lowerincome individuals.
Schedule Delay Costs In addition to placing value on travel time itself, individuals value certainty in information or expectations and being on schedule. Schedule-delay costs, like travel time costs, can be substantial. Such uncertainty can be estimated as a convex function of volume-capacity ratios, similar to a shifted version of the link performance function, as follows in Equation 2.9: =
1+
+
See Chapter 1 for details on estimating VOTT and VOR.
(2.9)
is the free-flow travel time variance of link , and , and are where function parameters. fa*gnant et al. (2010) estimated these parameters to be = 2.3, = 0.7, and = 8.4 from data for 2- to 5-mile-long freeway segments in Atlanta, Los Angeles, Seattle, and Minneapolis. Similar to calculations for travel time costs, the travel time unreliability multiplied by users’ value of reliability (VOR) determines the total system (un)reliability costs.
Provider Service Costs Marginal provider costs ( ) include maintenance and operation costs. Small and Verhoef (2007) estimated this cost to be $0.019 per vehicle-mile using 2006 FHWA data, including law enforcement, crash response and system administration. The majority of is dominated by highway maintenance expenses, which are traditionally covered by state and local governments through title, license, and registration fees and subsidies from general tax revenues. Pricing of Transportation Services
2-8
Chapter 2
Safety and Environmental Costs Crash costs ( ) and environmental costs ( ) are external costs resulting from increases in crash risk and impacts to air, water, noise, and other ecological elements.
Long-Run Marginal Pricing
See Chapter 1 for estimates of marginal safety and environmental costs.
Though the idea of social welfare optimization using marginal cost pricing is well favored, there exists much debate about whether SRMC or long-run marginal cost (LRMC) pricing is more appropriate. SRMC does not account for the capital cost of roadway construction, which has significant implications for cost recovery and congestion. Potentially, the shorter the time period under consideration, the lower the SRMC, which can lead to minimal charges for road use that are ineffective in containing excess demand. However, Verhoef (2000) examined three models of long-run marginal external cost pricing involving the factors listed above and concluded that in the long run, SRMC pricing still provides economic efficiency by effectively controlling demand. To account for capital costs, LRMC treats capacity either as a continuous variable or a discrete variable measurable in units such as lanes. In the short run, these costs are considered fixed and can be incorporated in road tolls based on average cost. Small and Verhoef (2007) used 2005 US Department of Commerce data to estimate the average capital cost at $0.056 per vehiclemile for urban passenger vehicles. They accounted for depreciation of the entire U.S. highway capital stock assuming a 7% interest rate and 20-year infrastructure lifespan.
See Chapter 1 for a discussion of fixed vs. variable costs.
Static vs. Dynamic Pricing The optimal tolling approaches discussed thus far assume flat-rate tolling or static pricing, appropriate for congestion levels that stay relatively constant through an extended period (such as the peak). In cases of more dynamic bottlenecking due to construction, crashes, or other extraordinary events, dynamic pricing strategies are more effective in substantially changing traveler behavior. Consider the case of the single pure bottleneck, where no delays arise if inflow (V) to the system lies below capacity (C). The rate of queue, when a queue exists, and free-flow travel time through the bottleneck is zero. Under optimal dynamic tolling, no queue should exist and the bottleneck should flow at capacity until demand falls below C. The optimal toll influences the inflow (Qi) and replaces delay cost. This dynamic bottleneck model implies that rescheduling departure times at trip origins as a result of roadway pricing can significantly improve system efficiency. Small and Verhoef (2007) discuss a triangle toll schedule using piecewise linear functions to toll optimally based on the expected travel time through the bottleneck. However, shifting schedules to accommodate optimal departure times may be infeasible without large investments in system administration. Chapter 2
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Pricing of Transportation Services
Second-Best Pricing First-best, or socially optimal, pricing requires perfect information on travel times and tolls and perfectly competitive markets, which cannot realistically be achieved. Travelers are generally not aware of all alternative routes and cannot anticipate a variable toll or congestion conditions before beginning their trip. A first-best pricing scheme assumes that all other competing modes and routes are priced at (optimal) marginal cost, which is rarely the case in transportation. When marginal cost pricing for delays imposed on others is applied on all roadways in a network, the externality of congestion is fully internalized and the socially optimal, or first-best, scenario is achieved. In reality, first-best pricing roadway is infeasible due to the existence of nontolled roads near tolled facilities and the availability of competing modes of transportation (e.g., transit and bike) that are not priced at marginal cost. Additionally, while social welfare optimization through the principle of marginal cost pricing can be modeled “perfectly” in a conceptual network, designing a first-best pricing scheme for real-world applications faces many constraints. The model assumes access to perfect information on prices for travelers and costs for modelers and those setting the tolls. As one researcher states (Verhoef 2000, p. 319), “such a situation can only be realized if one would apply some ‘Big Brother’ type of electronic road charges, using very sophisticated technologies that can monitor the actual emissions, the place and time of driving, the driving style, and the prevailing traffic conditions; and that allows the regulator to adjust the charge accordingly.” Aside from the political controversy that such a pricing scheme would surely cause, presentday technologies are still far from making the idea feasible in practice. Even with advanced technologies, pricing systems still have capital and operations costs that may offset many (and sometimes all) of the benefits achieved through rationalizing traffic flows and reducing delays.
Marginal cost pricing, or firstbest pricing, is not optimal when users don’t have perfect information and transportation markets are not perfectly competitive.
Other issues even technology cannot resolve. For example, drivers will always have imperfect information due to uncertain events (such as traffic crashes) and lack perfectly substitutable routes and modes (and the available modes may be imperfectly priced). Marginal cost pricing is not optimal when drivers do not know all their options when choosing and/or altering their routes (such as all the local streets that could bypass a bottleneck, or all the variable tolls at any point in time). When first-best pricing cannot be achieved due to market imperfections (including imperfect information), second-best pricing schemes costeffectively are considered. Second-best pricing strives to maximize traveler (or societal) welfare based on marginal cost pricing principles while reflecting technological, political, and financial constraints. Many different second-best pricing strategies exist, but this discussion focuses on two simplified cases to illustrate the concept.
Pricing of Transportation Services
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Chapter 2
Case 1: Not All Links in a Network Are Tolled Consider the case of two completely comparable routes in parallel, one tolled (T) and one untolled (U). In a perfect market, both routes are tolled and the number of trips on each will be roughly the same (QU = QT = Qequilibrium). However, because one route is not tolled, many travelers will shift to the untolled route, due to its lower out-of-pocket costs so that QU > Qequilibrium > QT. Marginal social cost of the tolled route ( ∗ = – ) only captures the benefit of those trips on T and not the spillover trips now on U. Thus, the second-best toll for T in this scenario is shown in Equation 2.10: ∗∗
= (
Here, the term
−
)−(
−
)(
)>
∗
(2.10)
represents the number of trips added to U per
trip removed from T. When demand is elastic (responsive to tolls), this term will be less than one as some trips removed from T will no longer be made on either road. This term is also negative (QT < Qequilibrium), so the second-best toll is the ∗ plus a fraction of the marginal social cost price of U (representing recapture of the benefits of the trips lost by T to U). The welfare gain generated by tolling just one route at ∗∗ is greater than that generated by tolling ∗ under the toll and untolled scenarios, but less than the welfare gained if both routes were tolled at marginal social cost and flow (first-best scheme). Additionally, the calculation of a second-best toll requires more information than the first-best scenario (demand and cost elasticities in addition to marginal external costs). Second-best tolling can result in more “mistakes” and less optimal toll settings, which can reduce traveler welfare. A Verhoef and Small (2004) study of the efficiency of three express-lane pricing schemes found that relative welfare gain at ∗∗ increased as the capacity of the express lane (as a fraction of total capacity of the highway) increased. This makes intuitive sense because zero express lane capacity corresponds to two parallel routes that are both untolled, while express-lane capacity of 100% corresponds to all routes, both tolled. For express lanes offering approximately one-third of the total capacity, the relative efficiency of the second-best toll ( ∗∗ ) is about 30% of first best tolling. Surprisingly, they also found that if tolled capacity is less than 65% of total capacity, pricing at a “quasi first-best” ∗ on the tolled route with an untolled parallel alternative actually harms travelers overall (Small and Verhoef 2007). The relatively simple case of the parallel routes demonstrates that the second pricing equation becomes more complex (requires more inputs) with more alternative routes, increasing the potential for error when strictly optimizing. However, with a well-calibrated travel demand model and appropriate measures of traveler benefits, second-best pricing can still be effective at managing travel demand. In general, expressways and major urban arterials Chapter 2
2-11
Deriving secondbest pricing requires more information than first-best pricing.
Pricing of Transportation Services
with fewer substitutes make easier candidates for road pricing.
Case 2: Unable to Differentiate Price by User Group As mentioned earlier, the costs each user imposes on other travelers varies with vehicle, driver, and traffic characteristics. Imagine the relatively simple case of two vehicle classes: those with high emissions (H) and those with low emissions (L), and MECH > MECL,. When technology or political acceptance prohibits differentiation of pricing groups, a weighted average second-best toll (PT) higher than PL-MEC and lower than PH-MEC can be assigned. In the case where the demand curve for low-emission vehicles and high-emission vehicles are identical, PT is simply the average of PL-MEC and PH-MEC. With the same PT assigned to both vehicle classes, the low-emission vehicles are overpriced and the high-emission vehicles are underpriced, resulting in some welfare losses (represented by the shaded areas of the graphs in Figure 2.4) as compared to the first-best, group-specific toll scheme.
Figure 2.4: Second-Best Pricing With Two User Groups
Even though the two cases presented above are simplified, they highlight important characteristics about second-best pricing. Fundamentally, first-best is always better than second-best. The welfare gains of a second-best pricing scheme cannot surpass that of a first-best pricing scheme. In addition to not offering welfare-maximizing prices, placing identical tolls on both vehicle classes does not encourage drivers of high-emission vehicles to purchase lower emission vehicles. Moreover, second-best schemes require more information to formulate than first-best schemes, thus increasing the odds of imperfect pricing choice. However, second-best schemes can provide insight as to which factors are most influential in pricing and generally perform quite a bit better than pricing schemes that ignore indirect effects. In addition to formal optimization methods, trial and error can also be used to determine second-best prices, such as the use of Safirova et al.’s (2007) LUSTRE model for second-best tolling in the Washington DC area.
Pricing of Transportation Services
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Chapter 2
Implementing Roadway Pricing As discussed in Chapter 5, most transportation revenues do not base user fees on marginal social-cost pricing. Automobile travel is currently underpriced because user fees do not account for most external costs. Traditional user fees include vehicle registration fees, which are independent of road use and fuel taxes. The flat-rate nature of federal and state fuel tax has not kept pace with increasing transportation capital and maintenance costs. Furthermore, as natural gas and electric vehicles (including hybrid electric vehicles) increase their market share, traditional fuel taxes will reflect smaller shares of actual road use. As fuel tax revenues fall, a move toward alternate funding instruments such as sales taxes and bonds (which are not related to roadway usage) pulls transportation finance farther away from marginal cost pricing, leading to a less efficient market in which demand does not reflect transportation costs. To better reflect user costs, the following pricing strategies are recommended by Litman (2011b): • Achieve fuller cost recovery by increasing fuel taxes and tolls to reflect actual user costs. • Utilize weight-distance fees to reflect roadway costs that are proportional to vehicle class as heavy vehicles cause more pavement wear and tear than light vehicles. • Price roads variably to reflect congestion costs that vary with location, time, and vehicle type.
See Chapter 5 for a detailed breakdown of transportation revenue and expenditure sources.
• Convert traditional fixed costs, such as insurance and registration fees, to variable costs based on annual usage. • Consider new fuel taxes and emissions fees to reflect the environmental costs of driving. Congestion pricing and distance-based tolls seek to allocate travel costs based on usage. However, the public tends to perceive tolls as a new tax on existing, unpriced infrastructure. Researchers Podgorski and Kockelman (2005) conducted a survey of public perceptions of toll roads in Texas and found that Texans generally preferred to keep existing roads free of tolls, reduce tolls after recovering construction costs, charge higher tolls for trucks, and not implement congestion pricing. The survey also revealed that Texans who already commute on toll roads and during peak periods tended to support HOT lanes. Generally, frequent toll road users were more supportive of a wide range of transportation policies. Familiarity breeds new values and understanding. Several strategies for allocating cost based on usage are described in the following sections.
Congestion Pricing Chapter 2
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Pricing of Transportation Services
Also known as value pricing, congestion pricing (CP) consists of prescheduled or truly dynamic tolls that vary in anticipation of demand or in response to actual demand, in order to avoid excessive congestion and delays. Utilizing the principle of supply and demand, CP manages congestion on roadways by adjusting the price (toll) to control the demand (traffic volume). VOTTs of added traffic vary based on trip type, household income, and other factors. The following example applies a uniform VOTT to illustrate a simple case of congestion tolling to account for the marginal cost of travel time. Concept Example: Congestion Pricing Given a 10-mile section of a 4-lane highway (8,000 veh/hr capacity) with a free-flow speed (FFS) of 60 mph, what is the optimal congestion price for the last or “marginal” vehicle entering? Assume the average VOTT is $12/hr. The optimal congestion charge for the marginal vehicle is the added delay cost that the vehicle imposes on the entire system, which can be estimated using the BPR function discussed previously (Equation 2.8). Initial travel time ( ) based on FFS is 10 minutes. Estimating that “C” level-of-service conditions (FFS) occur at 75% of capacity gives a value of 6,000 veh/hr. When the system has 7,999 vehicles, the average travel time is 14 minutes 44.30 seconds. When the system has 8,000 vehicles, the average travel time is 14 minutes 44.44 seconds. The difference seems minor until the time is summed up over all the vehicles in the system, showing an increased delay of 19 minutes for the whole system when the 8,000th vehicle enters—or with VOTT applied, about $4. Dynamically priced lanes are generally designed to ensure reasonable flows and/or speeds subject to toll rate limits. For example, the IH 15 HOT lanes in San Diego have a maximum toll cap of $8 and SR 167 HOT lanes in Seattle have a maximum toll cap of $9 (DeCorla-Souza 2009). Presently, the U.S. has fewer than 10 HOT lanes with an explicit objective of moderating congestion via variable pricing. Several other locations have peak and off-peak pricing (e.g., New York bridges at $8 and $6), but the rate differentials tend to be very small (e.g., the New Jersey Turnpike, California’s San Joaquin Toll Road, and Florida’s Ft. Myers-Lee County route) with little to no effect on congestion (DeCorla-Souza 2009).
Pricing of Transportation Services
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U.S. Examples Dynamically Priced Lanes In Southern California, pricing on SR 91 is pre-set, and thus not truly dynamic, but near-peak prices vary hourly, and by day of week and direction of travel. Friday afternoon eastbound travelers (between 3:00 and 4:00 p.m.) pay the highest tolls (at 97¢ per mile), while weekend fares are nearly flat (generally around 30¢ per mile between 8:00 a.m. and 10:00 p.m.). As with California’s IH 15, SR 91 essentially aims for a “C” levelof-service target (Roth 2009, OCTA 2009), offering maximum flow at nearly free-flow-speed (zero-delay) conditions. California’s SR 91 authority has been making small adjustments in rates almost every year, with little political fight, thanks to the size of the increments, regularity of past experiences, and clear existing policy (Samuel 2009). SR 91’s policies appear to be the most public; monitoring for rate changes occurs on 12week cycles (though, in practice, its toll schedules tend to change just once a year). In contrast, California’s IH 15 and Minneapolis’s IH 394 offer truly dynamic pricing with capped rates. These caps do not change often, largely because they are set sufficiently high (Samuel 2009). Guidelines for changing these rates vary by location: while Minneapolis targets speeds of 50 to 55 mph along IH 394 (Roth 2009), the Acting Director of Denver’s Colorado Tolling Enterprise has the authority to change IH 25 rates during certain times of day if needed, and the governing board can later adopt the rate increase in a concurring resolution. Colorado’s E-470 toll road is also governed by a board, and bond covenants have a toll rate structure built in, with periodic rate increases scheduled and subsequently approved by the board (Caitlin 2009).
Allocation of Joint and Common Costs Allocating cost responsibility requires different methods for joint versus common costs and is a critical component in roadway tolls, shipping rates, and other modes of transportation. Allocation of joint costs in a market with varying demand between points A and B, such as empty rail cars or trucks on many return trips, requires setting rates that reflect the different demand in each direction. Costs for “true joint products are produced in fixed proportions,” which “means there can be no variability in costs making it logically impossible to specify the cost of, say, an outward journey when only the overall cost of the round trip is known” (Button 2010, pp. 80–81). Shipping rates along a low-demand direction (e.g., from B to A) tend to be less than the rates for the high-demand direction (e.g., A to B) in order to encourage the market to use the vehicles for the return trips. The joint costs are mostly allocated to the higher demand direction because marginal costs to serve return trips are relatively low. Chapter 2
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Allocation of common costs is an area of ongoing research and policy debate. Studies completed for TxDOT about how to allocate highway costs (such as Luskin et al. 2002) reveal some of the issues, assumptions, and methods. Luskin et al. (2002) used four different cost allocation methods for allocating Texas highway costs to different vehicle classes. They concluded that fees and tax revenues from light vehicles (autos and pickup trucks) exceed their share of highway system costs (with revenue-to-cost ratios over 1), whereas combination trucks and buses do not (revenue-to-cost ratios less than 1). They also describe four different highway cost allocation methods and the desirable properties of such methods, as presented in Table 2.1 and Table 2.2. Applied to our aggregated results, this criterion of fairness would lead to the conclusion that light vehicles—autos and particularly pickup trucks—are cross-subsidizing combination trucks and buses (Table 2.1). Property
Description
Completeness Highway costs are fully paid by highway users. Rationality
Vehicle classes do not pay more than they would if they chose to be part of any smaller coalition of vehicle classes for which an exclusive facility is assumed available.
Marginality
Vehicle classes are charged at least enough to cover their marginal costs.
Consistency
Repeating the method gives consistent results.
Table 2.1: Desirable Properties of Highway Cost Allocation Methods (Source: Luskin et al. 2002)
Two of the approaches in Table 2.2 enjoy all four properties: the modified incremental approach and the generalized method. These were used by TxDOT in a 1994 allocation study, and are considered “superior methods” (Luskin et al. 2002). The incremental and proportional methods are considered more traditional methods.
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Method
Incremental Method
Proportional Method
Modified Incremental Approach
Generalized Method
Description The cost of a highway facility designed for a class of vehicles is calculated. Then, in a process repeated until all vehicle classes have been assigned costs, the incremental cost of also serving the next class (by weight or some other measure) is determined and the added costs (marginal costs) are assigned to the added class. This method offers completeness, rationality, and marginality, but not consistency because cost allocation depends on the order of vehicle class additions (e.g., lightest to heaviest weight vs. heaviest to lightest weight). Vehicle classes are assigned the costs according to a measure of usage (such as vehicle-miles traveled and equivalent single-axle loads [ESALs]). This method offers completeness, but not rationality or marginality. Vehicle-miles traveled are typically used to proportionally divide the costs among classes of vehicles. First, non-overlapping cost portions attributable to each class are computed (e.g., costs associated with the particular vehicle class only). Then the overlapping cost portions for pairs or groups of classes are computed, followed by computation of overlapping costs for all the vehicle classes (e.g., costs common to all vehicle classes). The total costs allocated to one particular vehicle class are the nonoverlapping costs plus the fraction of overlapping costs attributed to the vehicle class in each group of vehicle classes. This method satisfies all four properties. Based on the theory of cooperative games, the method uses a linear programming mathematical formulation to find a single point that allocates cost responsibilities to the vehicle classes. This method satisfies all four properties.
Table 2.2: Highway Cost Allocation Methods (Source: Luskin et al. 2001)
VMT with Taxes Vehicle-mile or VMT taxes seek to more equitably charge for roadway usage based on distance (and ideally incorporating vehicle weight and emissions), rather than travel consumption taxes (which reflect fuel economy more than pavement damage and other costs.) Public services provided to vehicle users include policing, traffic lights, and emergency services, for which the costs are estimated to run about 1 to 4¢ per vehicle-mile. VMT taxes can also be priced to account for emissions or air quality impacts, habitat loss, stormwater management, and heat-island effects (Litman 2011b). Estimates of other external costs of light duty vehicles run about 2 to 5¢ per VMT. In contrast, current U.S. fuel taxes average about 2¢ per mile on a 20-mpg vehicle and 1¢ per mile on a 40-mpg hybrid electric vehicle (Litman 2011b). The 2001 Oregon Legislature established a Road User Fee Task Force “to develop a design for revenue collection for Oregon’s roads and highways that will replace the current system for revenue collection.” The Oregon Department of Transportation (ODOT) conducted a 12-month study of two strategies for more efficiently collecting revenue: 1) replacing the gas tax with a mileage-based fee collected at gas stations, and 2) using this system to Chapter 2
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collect congestion charges. The study demonstrated the feasibility of collecting mileage fees at gas stations, and how different pricing zones can be established electronically with variable fees charged for driving in each zone at particular times of day. The pilot program tested two different kinds of mileage fees: a flat per-mile charge and a premium for travel in congested zones during peak hours. Despite paying a VMT fee equivalent to the current gas tax, the per-mile charge group reduced their VMT by about 12%; the congestion-fee group reduced peak period travel by 22% when compared to the per-mile group. Oregon’s Road User Task Force recently recommended 2011 legislation for new fees on plug-in vehicle owners. Other states currently exploring VMT tax options include Alabama, California, Iowa, Indiana, Kentucky, Michigan, Minnesota, Utah, and Washington. In 2011, The Netherlands tested a GPS-based VMT tax, which varied based on fuel efficiency, day, and route. Drivers were billed monthly, and proponents saw this system as a possible revenue generator in lieu of gas taxes and toll roads. Although politically unpopular, this approach is being considered by several other countries (Rosenthal 2011).
Freight Movements Freight shipments play a critical role in the U.S. economy. Rail freight movements accounted for 15% of the value and 21% of the weight of total U.S. domestic freight activity while truck freight traveling on the Interstate system accounted for 62% of the value and 28% of the weight. Pipelines, maritime modes, and air carried the rest (BTS 2006). The FHWA estimates that domestic freight volumes will more than double between 2002 and 2035, with truck and air/truck combination modes expected to experience the fastest growth. Interstate and international deregulation over the last two decades has increased the number of U.S. operators. Roadway pricing for freight is complicated by the geographic distribution of truck traffic. Highway corridors that experience the heaviest truck traffic run from Indiana to Illinois, Pennsylvania to New Jersey, Michigan to Ohio, and New Jersey to New York as illustrated by Figure 2.5’s plot of the 2002 Freight Analysis Framework (FAF).
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Figure 2.5: 2002 Truck Traffic Volume on U.S. Highway Network (Source: USDOT 2007)
Freight in Urban Areas A 2008 American Transportation Research Institute survey approximates motor carrier marginal expenses at $83.68 per hour, with fuel and oil costs accounting for 39% of these costs; driver pay, benefits, and bonus payments account for 30%. Due to high labor and equipment costs, the trucking industry is particularly sensitive to congestion. A 1999 NCHRP report surveyed truck operators and estimated values of travel time savings during congested conditions to range from $144 to $193 per hour and the value of schedule delay savings at $371 per hour (Small 1999). More recently, Texas Transportation Institute estimated in 2007 the average cost of lost time and fuel for commercial vehicles in congestion to be $77.10 per vehicle-hour. Another study of operators estimated an average value of travel time to be $49.42 per truckhour (Levinson & Smalkowski 2003). Additionally, trucks are high contributors of noise and air pollution, conditions already plaguing urban areas that will only worsen with increased truck traffic. A study for freight demand management in the New York metropolitan area estimated the operator cost of off-hour deliveries to be about 30% less than delivering during regular business hours. Those carriers with fewer delivery stops were most inclined to participate in off-hour deliveries. The researchers estimated that tolls would be required to shift a significant amount of NY freight traffic to off-hours (Holguín-Veras et al. 2010). All of these factors contribute to further incentives to price roadways for efficient use and reduce congestion for both commercial and non-commercial Chapter 2
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users in order to minimize delay, reliability, and transportation-related environmental costs. Some freight pricing mechanisms are discussed below.
Freight Pricing Mechanisms The Tioga Group’s (2011) extensive examination of freight-pricing options considered VMT fees, international trade fees, vehicle sales and excise taxes and fees, freight activity taxes and fees, highway tolls, and fuel-tax reforms. Each option was evaluated for technical and legal feasibility, institutional feasibility, revenue potential, cost, efficiency, environmental impacts, modal impacts, economic impacts, and political and public acceptance. The study concluded that waybill taxes and carbon taxes are not viable pricing mechanisms, at least not at the present time. But VMT fees based on distance, time, and location are one of the most technically viable options. Many trucks already report VMT (as required by the International Fuel Tax Agreement); thus, many fleets can implement distance-only VMT fees without GPS or cellular technology. But the compliance burden will still be great, particularly for smaller operators. VMT fees based on time and location require significantly higher incremental implementation, compliance, collection, and reinforcement costs. Time- and location-specific fees will also require GPS or other location technology, thus requiring longer implementation periods and facing more political and acceptance barriers, due to privacy concerns. However, time-of-day and location-based VMT fees provide a closer connection between congestion and roadway use. International Example Freight Pricing Schemes in Europe In 2001, Switzerland’s flat-rate heavy-vehicle charge was replaced by a performance-related fee. All freight vehicles whose total weight exceeds 3.5 metric tons (about 7,700 pounds) are charged based on weight, and emissions rating. Austria followed suit in 2004 with a similar ETC system for trucks. Using satellite technology, Germany introduced a toll system for trucks in 2005. Operators have the choice of installing on-board units for automated tracking of truck movements or advance booking of truck routes (online or via computerized booking terminals).
2.4 Road Pricing’s Impacts on Equity The distinct roles that local, regional, state, and federal governments play in financing transport systems can lead to heated debates on how funds should be regulating and collected. Should revenues come from income, sales, and property taxes, or through user fees (such as tolls, transit fares, and fuel taxes)? Equity is a key criterion in this debate, for assessing the distribution of benefits and costs associated with transportation investments and policies. Litman (2011a) defines the following types of transportation equity: • Horizontal equity measures whether individuals and groups considered Pricing of Transportation Services
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equal in ability and need receive equal shares of resources and bear equal costs. Horizontally equitable public policies encourage cost-based pricing. This equal exchange of transportation spending versus revenue collections has been called market equity. • Vertical equity in income and social class (also referred to as social and environmental justice) refers to impacts of transportation on different stockholders’ incomes and socially disadvantaged groups. Even though transportation is a derived demand, it is essential in daily life and can be considered a right. Thus, policies favoring disadvantaged groups (such as tiered tax systems) are considered progressive, and policies that burden the disadvantaged (such as flat-tax systems) are called regressive. Policies that distribute funding to help bridge the accessibility and mobility gaps across different groups help improve vertical equity. Addressing equity is complicated, in part because the goals of horizontal and vertical equity can be at odds in practice. While vertical equity favors subsidies for the disadvantaged, horizontal equity calls for economically efficient usage-based pricing. Equity is potentially achieved through economic efficiency, where users bear the costs they impose on society, unless a subsidy is justified. Because economically, socially, and/or physically disadvantaged people typically have less access to cars, policies that favor alternate modes help create vertical equity. Following are Litman’s (2011a) suggestions for improving horizontal and vertical equity.
Measures to Improve Horizontal Equity • Correct current planning biases that favor certain groups and specific modes (e.g., per capita funding and per trip funding tend to favor densely populated areas and the auto mode). • Increase variable roadway user fees (tolls and fuel taxes) to reflect the actual cost of auto travel, which varies by time of day, location, and vehicle type. • Price parking facilities and allow parking cash-outs for workers who choose cash over subsidized parking. • Base vehicle insurance and registration fees on annual VMT. • Consider environmental taxes and emissions fees so that drivers are accountable for the environmental externalities of auto use.
Measures to Improve Vertical Equity • User-based pricing can be structured to favor economically, socially, and physically disadvantaged people (e.g., discounts and vouchers can be provided to those who qualify for low-income benefits). • Reward alternative modes of travel by funding sidewalks, bike lanes, Chapter 2
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and public transit. • Support carsharing and bikesharing programs. • Support multi-mode accessible land use patterns and locate public services in places accessible without a private car.
Congestion Pricing: Is It Equitable? Public and political opposition to CP policies often arise based on vertical equity concerns. Because the traveler (and trip) population is heterogeneous, those with higher values of time tend to benefit the most from pricing (as time-savings values are more likely to exceed the toll). Referring back to Figure 2.2, even though the optimal price maximizes net welfare, users of trips between YTMSC and YAC have been “priced off” the toll road and are taking a less-preferred route or mode, or eliminating the trips altogether. U.S. Example A Case Study of Income Equity: Tolling vs. Taxation Schweitzer and Taylor (2008) evaluated the income equity of a local option sales tax to fund roadways was evaluated against a tolling option for Orange County’s SR 91 freeway. The local option sales tax was more popular politically, partly because the tax burden can be imposed at a low level across more people. However, shifting financing via tolls to a local sales tax was found to shift the cost burden away from middle-income travelers to those in low and high-income groups. In comparison to the sales tax, tolling was more progressive for lower-income households. Redistribution of CP Revenue Eliasson and Mattson (2006) examined three different revenue redistribution schemes of Stockholm’s cordon charges. The study found that using CP revenues to lower value-added taxes benefitted high-income travelers the most and using the revenue for transit benefitted low-income travelers the most. Although higher-income travelers use CP more often and bear most of the charges, a low-income traveler going by car in the peak direction during the peak hour is still more affected by the charges. Alternatives for these lowincome travelers can be provided in the form of toll exemptions or rebates and discounted “lifeline” pricing based on income. For example, Kalmanje and Kockelman’s (2004) credit-based congestion pricing (CBCP) grants drivers a monthly allowance of travel credits (typically monetized) to use on priced roads. The policy proposes drivers do not pay money out of pocket unless they exceed their allowance. Those spending less than their limit can use the credits later or exchange them for cash, bus passes, or other benefits. For drivers with special, socially desirable travel needs (e.g., welfare-to-work participants and single-parent low-income household heads), extra credits may be allotted. In essence, CBCP encourages travelers to budget their travel based on congestion. Similar to CBCP, DeCorla-Souza’s (2000) Fair and Pricing of Transportation Services
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Chapter 10’s Case Study 3 examines the economic impacts of CBCP. Chapter 2
Intertwined (FAIR) Lanes work on the basis of providing toll credits to those regularly using/needing the free lanes adjacent to tolled lanes. Accumulated credits allow for periodic free use of the tolled lanes. Revenue redistribution, which can aid in bridging the gap in user benefits for different income groups, has also played a key role in increasing the political acceptability of CP. Finally, as populations and travel behaviors vary from region to region, the distributional impacts of pricing policies need to be evaluated on a city- and scheme-specific basis.
2.5 Summary This chapter focuses on the concepts behind optimal transportation pricing. In other words, who should pay for transportation services and how? In order to implement appropriate pricing, decision-makers must first define their objectives. The concept of consumer surplus is critical in defining and solving for optimal marginal cost prices; system benefits are (theoretically) maximized when user prices are equal to marginal benefits received. Shortrun marginal-cost pricing requires that tolls reflect vehicle operating costs, travel time costs, schedule delay costs, government service costs, motor vehicle crash costs, and vehicle emissions costs. In addition to these costs, long-run marginal-cost pricing also should reflect the capital costs of roadway construction. However, transportation markets do not offer perfect competition and the amount of information required to determine real-time, congestion-sensitive, and vehicle-specific pricing levels is unrealistic. This chapter describes two cases of second-best pricing, to maximize benefits in light of technological, political, and financial constraints. The last part of the chapter departs from pricing theory to discuss real-world pricing applications. Pricing strategies to achieve fuller cost recovery include congestion pricing, highway cost allocation methods, and VMT fees. The chapter also examines equity issues that can arise from transportation pricing (and other) policies, such as impacts on specific socio-economic groups and/or people with special mobility needs.
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2.6 References American Transportation Research Institute (2008) An Analysis of the Operational Cost of Trucking. Arlington, Virginia. American Trucking Association (2008) American Trucking Trends: 2007–2008. Arlington, VA. Boos, M.A. & Moruza, A.K. (2008) Vehicle Miles Traveled (VMT) Tax: An Alternative to the Gas Tax for Generating Highway Revenue. Virginia Department of Transportation, Research Synthesis Bibliography No. 19. Available at: http://vtrc.virginiadot.org/rsb/RSB19.pdf Boyer, K. D. (1998) Principles of Transportation Economics. New York: Addison Wesley Longman. Button, K. (2010) Transport Economics. Northampton, MA: Edward Elgar Publishing. Caitlin, P. (2009) Acting Director, Colorado Tolling Enterprise. Email communication. March 17. DeCorla-Souza, P. (2000) FAIR Lanes: A New Approach to Managing Traffic Congestion ITS Quarterly, 3(2). De Corla-Souza, P. (2009) Value Pricing Pilot Program Manager USDOT. Phone conversation. March 16. Eliasson, J. & Mattsson, L. (2006) Equity Effects of Congestion Pricing: Quantitative Methodology and a Case Study for Stockholm. Transportation Research, A(40), 602– 620. fa*gnant, D., Kockelman, K., & Xie, C. (2010) Anticipating Roadway Expansion and Tolling Impacts: A Toolkit for Abstracted Networks. Available online at: http://www.ce.utexas.edu/prof/kockelman/public_html/TRB11Toolkit.pdf. Holguín-Veras, J., et al. (2010) Integrative Freight Demand Management in the New York City Metropolitan Area. Center for Infrastructure, Transportation and the Environment, Troy, New York. Available online at http://transp.rpi.edu/~usdotp/index.shtml. Jara-Diaz, S. (2007) Transport Economic Theory. Amsterdam: Elsevier. Kalmanje, S. & Kockelman, K. (2004) Credit-Based Congestion Pricing: Travel, Land Value and Welfare Impacts. Transportation Research Record, 1864, 45-53. Levinson, D. & Smalkowski, B. (2003) Value of Time for Commercial Vehicle Operators in Minnesota. University of Minnesota, TRB International Symposium on Road Pricing. Lindsey, R. & Verhoef, E.T. (2001) Traffic Congestion and Congestion Pricing In Button and Henshers (Eds.), Handbook of Transport Systems and Traffic Control. New York: Pergamon. Litman, T. (2011a) Evaluating Transportation Equity: Guidance for Incorporating Distributional Impacts in Transportation Planning. Victoria Policy Institute. Available online at http://www.vtpi.org/equity.pdf. Litman, T. (2011b) Socially Optimal Transport Prices and Markets: Principles, Strategies, and Impacts. Victoria Policy Institute. Available online at http://www.vtpi.org/sotpm.pdf. Pricing of Transportation Services
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Luskin, D., Garcia-Diaz, A., Walton, C. M., & Zhang, Z. (2002) Highway Cost Allocation in Texas: Executive Summary. Center for Transportation Research at University of Texas at Austin, Research Report 1810-3. Available online at http://www.utexas.edu/research/ctr/pdf_reports/1810_3.pdf. Mohring, H. & Harwitz, M. (1962) Highway Benefits: An Analytical Framework. Evanston, IL. Northwestern University Press. Orange County Transportation Authority (OCTA) (2009) 91 Express Lanes, Toll Schedules. Accessed July 2011 from http://www.91expresslanes.com/tollschedules.asp. Oregon Department of Transportation (ODOT) (2011) Road User Fee Task Force. Accessed at http://www.oregon.gov/ODOT/HWY/RUFPP/ruftf.shtml. Pigou, A. C. (1920) The Economics of Welfare. London: Macmillan. Podgorski, K. & Kockelman, K. (2006) Public Perceptions of Toll Roads: A Survey of the Texas Perspective. Transportation Research, 40A, 888–902. Rosenthal, E. (2011, August 10) In Auto Test in Europe, Meter Ticks Off Miles, and Fee to Driver. The New York Times. Roth, G. (2009) The Independent Institute. Email communication. March 16. Safirova, E., Houde, S., & Harrington, W. (2007) Marginal Social Cost Pricing on a Transportation Network: A Comparison of Second-Best Policies. Resources for the Future, DP 07–52. Samuel, P. (2009) Editor TOLLROADnews. Phone conversation. March 12 Schweitzer, L. & Taylor, B. (2008) Just Pricing: The Distributional Effects of Congestion Pricing and Sales Taxes. Transportation, 35(6), 797–812. Small, K. (1999) NCHRP Report 43: Valuation of Travel-Time Savings and Predictability in Congested Conditions for Highway User-Cost Estimation. Transportation Research Board, Washington, DC. Small, K. A. & Verhoef, E. T. (2007) The Economics of Urban Transportation. New York, Routledge. Taylor, B. D. (2004) The Geography of Urban Transportation Finance. In S. Hanson and G. Giuliano (Ed.), The Geography of Urban Transportation. New York, Guilford Press. Texas Transportation Institute (2007) 2007 Annual Urban Mobility Report. College Station, Texas. http://www.commutercars.com/downloads/UrbanMobility07.pdf The Tioga Group (2011) New Dedicated Revenue Mechanisms for Freight Transportation Investment. Presentation P11-0307 at Transportation Research Boards 90th Annual Meeting. Washington, DC. US Department of Transportation, Bureau of Transportation Statistics (BTS) (2006) North American Freight Transportation. Washington, DC. http://www.bts.gov/publications/north_american_freight_transportation/pdf/entire.pdf.
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US Department of Transportation, Federal Highway Administration (FHWA) (2000) Comprehensive Truck Size and Weight Study. Available online at http://www.fhwa.dot.gov/reports/tswstudy/. US Department of Transportation, Federal Highway Administration (FHWA) (2007) FAF2 Freight Traffic Analysis. Available online at http://ops.fhwa.dot.gov/freight/freight_analysis/faf/index.htm. US Department of Transportation, Federal Highway Administration (FHWA) (2009) Freight Facts and Figures 2009. Accessible online at http://www.ops.fhwa.dot.gov/freight/freight_analysis/nat_freight_stats/. Verhoef, E. (2000) The Implementation of Marginal External Cost Pricing in Road Transport: Long Run vs. Short Run and First-Best vs. Second Best. Papers in Regional Science, 79, 307–332. Verhoef, E. & Small, K. (2004) Product Differentiation on Roads: Constrained Congestion Pricing with Heterogeneous Users. Journal of Transport Economics and Policy, 38, 127– 156. Vickrey, W. S. (1963) Pricing in Urban and Suburban Transport. American Economic Review, Papers and Proceedings, 53, 452–65. Whitty, J. M. (2007) Oregon’s Mileage Fee Concept and Road User Fee Pilot Program: Final Report. Oregon Department of Transportation. Available online at: http://www.oregon.gov/ODOT/HWY/RUFPP/docs/RUFPP_finalreport.pdf. Wiseman, J. (1957) The Theory of Public Utility Price—An Empty Box. Oxford Economic Papers, 9, 56–74.
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Chapter 3. Regulation and Competition 3.1 Introduction ........................................................................................................................ 3-1 3.2 Regulations ........................................................................................................................ 3-1 Environmental ...................................................................................................................... 3-2 Regulating Emissions and MPG ....................................................................................... 3-2 Infrastructure’s Role in Emissions .................................................................................... 3-4 National Environmental Policy Act (NEPA) .................................................................... 3-4 Safety ................................................................................................................................... 3-5 Reducing Fatalities ........................................................................................................... 3-7 Designing Safer Work Zones ............................................................................................ 3-7 Workers’ Wages................................................................................................................... 3-8 3.3 Deregulation....................................................................................................................... 3-8 Railroad and Motor Carrier Deregulation ............................................................................ 3-8 Airlines Deregulation ......................................................................................................... 3-10 Deregulation Effects in the Airline Industry................................................................... 3-11 3.4 Competition ..................................................................................................................... 3-12 Competition in Public Transit Systems.............................................................................. 3-12 Competition between Bidders ............................................................................................ 3-13 Cost-Control Recommendations ..................................................................................... 3-13 3.5 Summary .......................................................................................................................... 3-13 3.6 An In-Depth Look ............................................................................................................ 3-15 Regulatory Evolution ......................................................................................................... 3-15 3.7 References ........................................................................................................................ 3-17
Key Terms
3.1 Introduction As the U.S. transportation system has evolved over the decades, the policies and regulations surrounding its use have developed accordingly. Regulatory policy affects competition, which is addressed as a major concept in this chapter. The variation of competition by market (air, trucking, and public transit) and the effects on final price are discussed and presented as examples of how regulations can impact markets and affect users. Landmark cases involving regulation (and deregulation) are discussed in the context of their impacts on the public and private sectors. Concepts of competitive behavior are also applied to the construction industry, where guidelines are presented for improving bid selection and reducing costs.
CAFE: Corporate Average Fuel Economy NAAQS: National Ambient Air Quality Standards NEPA: National Environmental Policy Act
3.2 Regulations To govern and control procedures and behaviors, governments set regulations that fall into two general categories: economic and social. Economic regulation traditionally has been designed to prevent monopolistic behavior in private-sector firms by controlling • Maximum prices • Rates-of-return on investment (profits) • Conditions of service provision • Market entry and exit
Regulation is defined as “controlling human or societal behavior by rules or restrictions” (Koops et al. 2006)
• Mergers and acquisitions, and • Accounting practices In transportation, independent regulatory agencies—such as the Interstate Commerce Commission and the Civil Aeronautics Board—were established by the U.S. Congress to first investigate and then render administrative decisions on the above-mentioned aspects of railroad, motor carrier, and airline behavior. The federal government also exercises some control over private-sector activities relating to health, product and worker safety, and the environment. Such social regulatory tools include • Promulgation of standards • Financial penalties • Outright prohibition of harmful activities, and • Requirements to monitor and measure adverse impacts The extent of social regulation has grown over the years as the general public has become increasingly alarmed by any number of issues related to the pollution of the environment, defective products, worker safety, Chapter 3
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hazardous materials, and highway crashes. The many regulations in the transportation sector address a wide range of issues such as the environment, safety, and workers’ wages, which are discussed in more detail below.
Environmental The transportation sector accounted for nearly 30% of total U.S. energy consumption and greenhouse gas emissions (GHG), as Figures 3.1 and 3.2 show.
Residential
22%
29%
19% 30%
Commercial Industrial Transportation
Figure 3.1: EndUse Sector Shares of Total Energy Consumption, 2009 (Source: EIA 2009)
Residential Commercial
5% 6% 8% 28%
Agriculture 20%
33%
Industry Electricity Generation
Figure 3.2: Percentage of U.S. GHG Emissions from Different Sectors, 2006 (Source: EPA 2008)
Transportation
Regulating Emissions and MPG Many regulations govern vehicle emissions and fuel consumption, as well as air quality, which is impacted by transportation systems. Following the 1973 oil crisis, fuel economy was first regulated in the form of Corporate Average Fuel Economy (CAFE) standards. As Figure 3.3 shows, the average new passenger vehicle fuel economy has improved from 13.5 miles per gallon Regulation and Competition
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(MPG) in 1975 to 25.8 MPG in 2010. Manufacturers are under continuing pressure to improve fuel economies as standards increase over time; lightduty vehicles must achieve over 35 MPG by 2016 (USDOT 2010) and proposed standards of nearly 55 MPG by 2025 (NHTSA 2011d). Despite this increase, average heavy and medium duty truck fuel economies (as well as those of motorcycles and recreational vehicles) have remained relatively constant over the same period. Federal regulators (the Environmental Protection Agency [EPA], USDOT, and National Highway Traffic Safety Administration [NHTSA]) addressed part of this issue in 2008 by setting CO2 emissions and fuel consumption standards for new medium- and heavyduty vehicles. Fuel Consumption (MPG)
30.0 25.0
Figure 3.3: Vehicle Fuel Consumption Trend from 1975 to 2010 (Source: EPA 2011b)
20.0 15.0 10.0 5.0 0.0 1975
1980 Car
1985 1990 1995 Truck Year
2000
2005
2010
Despite ambitious plans to improve fuel economy in the future, the U.S. currently lags well behind the rest of the world in fuel economy standards, and will likely continue to do so, even with the newly approved U.S. standards of 35 MPG by 2016. Figure 3.4 compares global light-duty vehicle fuel economies, converted to the U.S. CAFE test cycle standards.
Figure 3.4: Global Fuel Economy Standards (Source: An et al. 2007)
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The EPA limits new-vehicle emissions of non-methane hydrocarbons (NMHC), carbon monoxide (CO), oxides of nitrogen (NOx), particulates, and formaldehyde. If a manufacturer’s products do not meet emission standards, the EPA can mandate a recall under Section 207 of the Clean Air Act. In addition to new vehicle regulations, conformity-based policies can also govern current fleet emissions. As vehicles age, their emissions control devices become less effective, so inspection and maintenance (I&M) programs exist in many U.S. non-attainment regions. Manufacturers must ensure emissions meet standards for the “useful life” of the vehicle, which the EPA has set at 120,000 miles for cars and light trucks (EPA 2006). The EPA’s emissions regulations have significantly improved air quality across the U.S. Since 1970, on-road CO and PM emissions have decreased by 50% and hydrocarbon emissions by 30% (EPA 2012). NOx emissions have increased, but are projected to be less than 1970 levels by 2020 (EPA 2012).
Infrastructure’s Role in Emissions Transportation infrastructure also influences emissions by changing travel patterns, shaping land use, and affecting speeds and acceleration profiles. Billions of dollars are invested in transportation infrastructure each year to improve connections between housing, jobs and other destinations. The location and timing of constructing new and expanding existing roads, airports, railroads, and other transportation facilities can change travel modes and distances, and, as a result, impact energy consumption and conformity with National Ambient Air Quality Standards (NAAQS). The Clean Air Act and National Environmental Policy Act processes require evaluation of all significant environmental impacts before FHWA funds can be used.
Transportation infrastructure also influences vehicle emissions by changing travel patterns and shaping land use.
National Environmental Policy Act (NEPA) NEPA created a framework for environmental policy in the U.S. by requiring impact assessments for projects involving federal agencies. NEPA requirements apply to TxDOT projects involving federal funding or approval. NEPA requires agencies to analyze social, economic, and environmental impacts, consider alternatives, inform and involve the public, and implement measures to avoid, minimize, or mitigate environmental impacts. Sensitive areas and resources addressed by NEPA include floodplains, historic and archeological sites, wetlands, endangered species, parklands, and wildlife habitats. Under NEPA, projects must provide an environmental impact statement (most extensive), an environmental assessment (less stringent), or prove the project will have minimal impacts and thus qualifies for categorical exclusion (CE). An environmental impact statement (EIS) is required for any project or action that may significantly impact the environment, typically for projects such as new freeways or new separated high-occupancy vehicle (HOV) or bus lanes, as well as rapid, light, or commuter rail facilities. By first notifying the public of project intent and receiving input in return, as well as Regulation and Competition
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coordinating with other affected agencies, TxDOT can determine whether the project will have significant environmental impacts. If such impacts are expected, a draft EIS is prepared. Otherwise an environmental assessment or categorical exclusion is required. An EIS must be approved by the FHWA before a project can begin. Pursuing projects eligible for CE or a Finding of No Significant Impact (FONSI) avoids extensive analysis costs and expedites implementation. Examples of projects eligible for categorical exclusion are as follows: • Highway resurfacing, restoration, rehabilitation, reconstruction. • Adding shoulders and auxiliary lanes for parking, weaving, turning, or hill climbing. • Highway safety or traffic operations improvements, including ramp metering devices and lighting. • Bridge rehabilitation, reconstruction, or replacement and grade separations to replace existing at-grade railroad crossings. TxDOT’s 2004 Environmental Manual contains extensive information on NEPA requirements and project exceptions. To qualify for CE in general, a TxDOT project must be a maintenance or rehabilitation-type improvement, involve minimum public impact, require little to no additional right-of-way (ROW), relocate a minimal number of people, and have insignificant social, economic, or environmental impacts. CEs can bypass expensive and timeconsuming environmental impact analyses, but may be denied due to controversy over environmental impacts, potential interference with historic or archaeological sites, or other issues. Texas Example NEPA in Action In 2011, TxDOT planners and local officials discussed the best course of action to widen RM 1431 in Cedar Park, which runs adjacent to a well-known archaeological landmark (the Wilson Leonard site) that TxDOT helped excavate in the 1980s. TxDOT reported that the area south of the ROW may have contained historically significant artifacts and remains, but the north side would not require any further archeological investigation, greatly expediting the process and avoiding damage to a sensitive site (TxDOT 2011).
Safety As shown in Figures 3.5 and 3.6, almost 38,000 people are killed and over 2 million injured in motor vehicle crashes each year in the U.S. While these numbers appear to be trending downward (in part due to an economic Chapter 3
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recession), traffic crashes deprive the nation of more person-years and young lives than almost any other activity or disease.
Figure 3.5: Fatalities and Fatality Rates per 100 Million VMT from 1961 to 2008 (Source: NHTSA 2009)
Figure 3.6: People Injured and Injury Rate per 100 Million VMT from 1988 to 2008 (Source: NHTSA 2009)
In 2000, U.S. and Texas motor vehicle crashes cost $230 and $20 billion, respectively (NHTSA 2008). Approximately half of these costs come from direct market productivity losses and property damage, as shown in Figure 3.7. Medical care costs and emergency services account for another 14% of all costs, while travel delays caused by crashes are estimated to account for 11% (NHTSA 2002). In Texas, 11% of the total $20 billion in crash costs is $2.3 billion, or over $100 in delay-related crash costs per year per Texan.
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Market Productivity Property Damage
14% 9%
26%
Figure 3.7: Distribution of Motor Vehicle Crash Cost (Source: NHTSA 2002)
Legal Costs
7%
2%
Travel Delay
11%
5%
26%
Workplace Costs Insurance Admin Household Productivity Medical Costs and Emergency Services
Reducing Fatalities The NHTSA regulates vehicle design, including air bags, brakes, car seats, seat belts, and tires. In September 1997, the NHTSA required that all passenger cars be equipped with air bags. Then, in January 1999, the NHTSA required all multipurpose passenger vehicles, trucks, and buses heavier than 10,000 pounds to be equipped with anti-lock brake systems. If new vehicles do not meet all safety standards, the NHTSA can require a recall. In addition to safety regulations for new vehicles, current fleet safety components are inspected during annual registration renewal to reduce existing or potential safety deficiencies on the road. In another step to increase traveler safety, transportation engineers identify and survey crash sites to make those locations safer. The Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU) established the Highway Safety Improvement Program (HSIP) to allow states to use funds to correct or improve hazardous road locations and address other highway safety problems. In 2009, the funds available for the HSIP program totaled about $1.3 billion (FHWA 2011). The HSIP requires each state to develop and implement a Strategic Highway Safety Plan (SHSP) to improve highway design, construction, and maintenance so that the number of traffic crashes and costs will fall.
In an effort to reduce vehicle fatalities and injuries, several types of regulations exist to govern vehicle and highway design safety regulations.
Designing Safer Work Zones In addition to vehicle and highway safety regulations, designing safer work zones is a priority for improving transportation safety. In 2008, the U.S. had 716 work zone fatalities, 139 of which were in Texas (The National Work Zone Safety Information Clearinghouse 2011). To decrease the number of fatalities in work zones, the National Cooperative Highway Research Program (NCHRP) released a guide to address work zone safety with these recommended strategies: Chapter 3
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1. Implement improved methods to reduce the number and duration of activities. 2. Adopt improved procedures to ensure more effective practices including traffic control devices for managing work zone operations. 3. Enhance and extend training for the planning, implementation, and maintenance of work zones to maximize safety. 4. Enhance safe work zone driving through education and enforcement actions.
Workers’ Wages While the Fair Labor Standards Act (FLSA) covers most U.S. workers, those who work for contractors performing federally funded construction, alteration, or repair projects in excess of $2,000 are covered by the DavisBacon and Related Acts (DBRA). The FLSA requires that nonsupervisory private sector employees in Texas be paid at least $7.25 per hour and DBRA minimum wages vary by job type, as illustrated in Table 3.1. Activity Asphalt Distributor Operator Asphalt Raker Bulldozer Operator Laborer, Common Scraper Operator
Minimum Wage $11.45/hr $9.30/hr $11.80/hr $8.69/hr $10.29/hr
Table 3.1: Highway Construction Workers’ Minimum Wage for Federally Funded Projects (USDOL 2011)
3.3 Deregulation Deregulation is the removal or simplification of government rules and regulations to facilitate a more efficient operation of markets. Deregulation acts seek to relax or remove excessive government control. Economic deregulation has not been total and varies by mode. However, its net result has been positive for freight and long-distance passenger transport growth. Most importantly, regulatory reforms have enabled an increased interaction and cooperation between modes (intermodalism). The following sections discuss the effects of deregulation on the operation of these industries.
Perceived economic regulatory failure can become a catalyst for regulatory reform.
Railroad and Motor Carrier Deregulation The U.S. freight network links businesses with suppliers and markets by moving an incredible volume of goods each year. In 2009, over 16 billion tons of goods, worth about $14.6 trillion, were moved (Figure 3.8) (Center for Transportation Analysis 2011). The truck and rail modes carry the largest portion of freight shipments. Bulk goods—such as grain, coal, and ores— have a large share of the tonnage moved on the U.S. freight network. Highvalue, high-velocity goods such as electronics, machinery, textiles, and leather comprise a large share of the value moved. Regulation and Competition
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12,000 10,000 8,000
Value (billion $)
6,000
Tons (millions)
4,000 2,000 -
Figure 3.8: U.S. Freight Shipments by Mode in 2009 (Source: Center for Transportation Analysis 2011)
Before 1980, heavy rail and trucks were subject to rate, entry, and exit regulations, and freight rates were based on the value of the commodities shipped (Winston et al. 1990). Regulations increased costs for potential new carriers, which were already substantial, due to the cost of vehicles, along with ROW, rail yards, and track, in the case of railroads. In addition, a railroad’s exit from the market was costly and time consuming, as the Interstate Commerce Commission would not approve an abandonment request if shippers or local governments opposed the decision (Winston et al. 1990). As a result, one-third of the U.S. rail industry was bankrupt or close to failure by 1975 (Rodrigue et al. 2009). Moreover, trucking firms could enter the market only if they could justify their entry as necessary and convenient to the public. If existing carriers could make a case proving that new carrier entry would hurt them financially, new entry applications could be denied. In addition, truckers were required to have route- and commodity-specific operating authority. For example, if a carrier with the authority to go from Austin to Houston and Houston to Denver wanted to carry goods from Austin to Denver, it had to carry goods through Houston, even though the direct route was shorter. Such regulations raised costs, and shippers often had to deal with multiple carriers because each carrier had limited location authority. To overcome such inefficiencies, the Staggers Rail Act of 1980 allowed direct negotiation between railroads and shippers, enabling them to set rates and facilitate entry to and exit from the railroad industry. The Motor Carrier Act of 1980 loosened restrictions on trucking companies’ shipping rates and removed geographical constraints on their service regions. Trucking deregulation resulted in lower trucking rates, as shown in Figure 3.9, and an increase in productivity, thanks to better use of labor and equipment, as shown in Figure 3.10. In addition, freight costs decreased from 16.2% of gross domestic product (GDP) in 1981 to 7.7% in 2009 (ICF Chapter 3
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Motor carrier industry deregulation resulted in no adverse safety effects.
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& HLB 2002). The number of interstate trucking firms increased from 18,000 in 1975 to over 500,000 in 2000 (BTS 2001). Increases in truck VMT on U.S. highways have raised concerns about safety. Nevertheless, the crash rate has been steady since deregulation while fatality and injury rates have fallen.
Figure 3.9: Truck and Rail Revenue Rates (Source: ICF & HLB 2002)
Figure 3.10: Productivity Trends in the Trucking Industry (Source: ICF & HLB 2002)
Airlines Deregulation Prior to 1978, the Civil Aeronautics Board (CAB) regulated airfares, the number of flights, and which cities airlines could fly between. Also, airlines were subject to lengthy delays for CAB permission to establish new routes or eliminate services, making the industry less productive. To address such issues, President Jimmy Carter signed the Air Transportation Regulatory Reform Act in 1978 to deregulate the airline industry. Following are some provisions of this act: • Facilitated new entry to the air transportation industry. • Eliminated the CAB’s authority to set fares, routes, schedules, and market entry. • Authorized international carriers to offer domestic service. Regulation and Competition
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Deregulation Effects in the Airline Industry
450
20
400
18
350
16
300 250 200 150
14 Revenue/ Passenger Mile (1980 Cents Passenger Miles (Billions)
12 10 8 6
100
4
50
2
Real Airline Fares
Billions of Passenger-Miles
The first consequence of this deregulation was a reduction in the average fare level, as Figure 3.11 shows, due to changes in the fare structure. By 1986, about 90% of passenger-miles were flown on discounted tickets, with average costs 61% below the standard coach fares—translating into $11 billion in total savings for passengers in 1986 alone (Kahn 1988).
Figure 3.11: Trends in U.S. Airline Fares and Traffic (Source: Boyer 1997)
0 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 Year
The second consequence of deregulation was a surge in the number of air passengers. Table 3.2 shows the percentage changes in passengers for different U.S. markets between 1950 and 1995. To accommodate demand increases, airlines used larger planes, put more seats in existing planes, and increased flight frequency. As a result, the average number of seats for longhaul flights increased by 21% (Moore 1986). Smaller planes were used for short- and medium-haul flights. Type of Markets
Number of Passengers
Passengers per Departure
Long-Haul (over 800 miles)— Major Cities (over 1.2 million population)
+ 63%
+ 20%
Medium-Haul (200–700 miles)— Large Cities (over 200,000 population)
+ 29%
+ 20%
Short-Haul (under 200 miles)— Large Cities
- 16%
- 30%
Medium-Haul— Small Cities (under 200,000 population)
- 12%
- 2%
Short-Haul—Small Cities
- 60%
- 77%
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Table 3.2: Passengers and Departures— Percentage Change (Source: Moore 1986)
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Deregulation has increased productivity with more seats per flight filled via sale of discounted tickets. In 1982, the average percentage of occupied seats, also called the industry load factor, was 59% (prior to deregulation in 1976, it was 55%). Note, however, that these increased load factors have resulted in decreased service quality, amenities, comfort, and on-time service. Another consequence of airline deregulation was that the number of passenger and freight carriers more than doubled. In 1976, the numbers of certified carriers and certified passenger carriers were 33 and 28, respectively. By 1983, after deregulation, these numbers had increased to 98 and 61 (Moore 1986). As new non-union airlines offering lower fares with higher load factors entered the market, the industry saw less restrictive work rules and lower wages. By the late 1980s and mid-1990s, almost all of these airlines went bankrupt or merged because of mismanagement and crashes of under-maintained aircrafts (Boyer 1997).
As a result of airline deregulation, passengers paid less but also got fewer amenities.
3.4 Competition In general, as the number of companies providing services increases, prices fall, thanks to competitive forces. Urban transit systems used to be privately owned and relatively competitive, but many such systems faced serious economic issues in the 1950s. As the U.S. recovered from the war, incomes rose and automobile ownership became quite common.
Competition in Public Transit Systems In order to help public transit systems, the 1964 Urban Mass Transportation Act (UMTA) authorized $2.23 billion (in 2003 dollars) of federal initiatives (Hess and Lombardi 2005) and many cities purchased private transit operations. Federal funding for public transit has continued through the 1956 Federal-Aid Highway Act, the 1982 Surface Transportation Assistance Act, and others. The most recent is the 2005 SAFETEA-LU, which authorized approximately $2.3 billion to address transit programs in fiscal year 2009 (APWA 2005). As U.S. public transit systems are largely funded by government sources, some feel that they have become overly subsidized and inefficient monopolies. Subsidies have a negative effect on performance and productivity by reducing incentives for innovation and initiative, and financial mismanagement of transit properties may occur. With direct competition, transit firms may face less pressure to reduce costs and operate more efficiently. For example, Anderson (1983) estimated that the average operating cost per bus-hour of public firms is 28% more than private firms. Although urban or intra-city busses are mostly operated publicly, inter-city busses are operated privately. In 2010, inter-city bus service was the fastest growing mode of intercity transportation for the third year in row as a result of rising travel demand and fuel prices, investment in new routes, and the Regulation and Competition
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In contrast to public transit systems, privately owned transit companies grow in response to competition.
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emergence and expansion of low-cost operators. For example, the Chicagobased Megabus handled 500,000 passengers in its first 15 months of operation beginning in 2006, and has since expanded hub locations to include Atlanta, New York, Toronto, Pittsburgh, Philadelphia, and Washington D.C. As a result, conventional operators have upgraded their product by offering free WiFi connections, more spacious cabins, television monitors, and other amenities.
Competition between Bidders Construction project bidding represents another source of competition. In general, an increase in competition among bidders reduces construction costs as more bidders compete to win a project. Factors that impact the number of bidders include bid timing and project type, duration, and size. For example, fewer companies bid on long-duration projects because they are subject to more price fluctuations (Sanderson 2006). While many highly qualified large contractors do not bid for small projects, small or medium-sized firms may not have the resources to bid for large projects. According to AASHTO survey results, bundling smaller projects together and splitting large projects into smaller projects are two top strategies to achieve lower bid prices.
Cost-Control Recommendations TxDOT (2011) also recommends some cost-control strategies related to competition: 1. Where competition is limited, using flexible and delayed start dates allows smaller contractors to adjust schedules and bid. As a result, the contractors’ overhead decreases and competition increases. 2. Although project design can take months, contractors have a limited time to bid. Giving more time to contractors for planning can increase the number of bidders.
Resource Following these recommendations can result in a more productive bidding process.
3. Prequalification can cost a bidder $2,000 to $50,000, while a reviewed financial statement costs only a few hundred dollars. By waiving prequalification, competition is likely to increase. 4. When the construction completion time seems unreasonable (too short), contractors tend not to bid. More realistic project schedules can increase the number of bidders.
3.5 Summary The transportation sector is responsible for a large share of urban and rural air quality issues, and tens of thousands of premature deaths each year in the U.S., along with hundreds of billions of dollars in pollution, crash, and delay costs. Transportation is also a key component of the U.S. economy, responsible for nearly 20% of the U.S. GDP. Environmental and safety regulations help reduce external and other costs. Competition also affects costs and benefits. Regulatory policies affect the Chapter 3
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level and nature of competition in different transportation sectors. Airline carriers and public transit operators work in very different contexts. Competition adds bidders and reduces project costs. Regulations are not uniform and vary by industry, function, need, and beneficiary. The broad application of competition to various sectors reflects the importance in striking a balance in regulatory policy that promotes healthy growth in the private sector while moderating negative impacts on the public.
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3.6 An In-Depth Look This section provides supplementary information on the history of transportation regulation and deregulation.
Regulatory Evolution Shifts in user demands, mode choice, technological development, market forces and international trade have prompted the federal government to pass laws aimed to optimize the transportation system for both public and private sector use. Government regulations generally aim to improve welfare of the public in terms of health, safety, and efficient use of public funds. Federal regulations like the Clean Air Act and automotive safety standards set by the NHTSA are examples of proactive legislation aimed to benefit public interests. Over time, the federal government recognized the benefit of relaxing certain regulations on transportation industries and repealing many laws that governed certain business and trade requirements, generally resulting in more efficient businesses with lower fares, ticket prices, and shipping costs. Most social regulatory agencies belong to the executive branch of the federal government. Examples are the Food and Drug Administration (FDA), Occupational Safety and Health Administration (OSHA), Consumer Product Safety Commission (CPSC), Environmental Protection Agency (EPA), National Highway Traffic Safety Administration (NHTSA), and the Federal Railroad Administration (FRA). Many of these agencies have their counterparts in individual states. For example, the Texas Commission on Environmental Quality (TCEQ) is the state counterpart of the EPA. Prior to 1980, many regulations governed the operations of railroads, motor carriers, and airlines. Governments had control of fares, routes, and market entry and exit through the ICC and the CAB. Beginning in the late 1970s, perceived economic regulatory failure in the electric utility, telecommunications, banking, and transportation sectors of the economy became a catalyst for regulatory reform. In transportation, Congress passed several deregulation acts that relaxed market entry and exit, increased freedom to set rates, permitted horizontal and vertical mergers, and increased competition within and between modes. Some of the more important acts include the following: • Air-Cargo Deregulation Act of 1977 • Airline Deregulation Act of 1978 • Motor Carrier Act of 1980 • Staggers Rail Act of 1980 • Bus Regulatory Reform Act of 1982 • Shipping Act of 1984 Chapter 3
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• Freight Forwarder Deregulation Act of 1986 • Interstate Commerce Commission Termination Act of 1995 Regulation and deregulation has significant impacts on competition (both within and across modes), as discussed in this chapter.
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3.7 References An, F. (2007) Passenger Vehicle Greenhouse Gas and Fuel Economy Standards: A Global Update. Prepared for the International Council on Clean Transportation Available online at http://www.theicct.org/sites/default/files/publications/PV_standards_2007.pdf. Alexander, D. L. (1992) Motor Carrier Deregulation and Highway Safety: An Empirical Analysis. Southern Economic Journal, 59(1), 28–38. American Association of State Highway and Transportation Office (2004) AASHTO Strategic Highway Safety Plan. Available online at: http://safety.transportation.org/doc/SafetyStrategicHighwaySafetyPlan.pdf. American Public Works Association (APWA) (2005) SAFETEA-LU: A Guide to Provisions Related to Local Governments. Available online at: http://www2.apwa.net/documents/Advocacy/SAFETEA/APWA-SAFTEA-LU.pdf. Anderson, S. H. (1983) The Effect of Government Ownership and Subsidy on Performance: Evidence from the Bus Transit Industry. Transportation Research A 17A(3), 191–200. Boyer, K. (1997) Principles of Transportation Economics. Addison Wesley Longman, Inc. United States. Center for Transportation Analysis, Freight Analysis Framework Data Extraction Tool. Available at: http://cta-gis.ornl.gov/faf/Extraction1.aspx. Last access on 3/6/2011. Federal Register (2011) Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles. U.S. Environmental Protection Agency, U.S. Department of Transportation, and National Highway Traffic Safety Administration. 76 179). Hajek, J. J. & Billing, J. (2007) Trucking Trends and Changes that Affect Pavements. Transportation Research Record 96-103. Hess, D. B. & Lombardi, P. A. (2005) Governmental Subsidies for Public Transit History, Current Issues, and Recent Evidence. Public Works Management & Policy, 10(2), 138156. ICF Consulting & HLB Decision-Economics (ICF & HLB) (2002) Economic Effects of Transportation: The Freight Story. Available online at http://www.ops.fhwa.dot.gov/freight/documents/freightstory_12902.pdf. Jacobsen, M. (2012) Evaluating U.S. Fuel Economy Standards in a Model with Producer and Household Heterogeneity. University of California at San Diego. Available at http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf. Kahn, A. E. (1988) Surprises of Airline Deregulation. The American Economic Review, 78(2), 316–322. Koops B. J., Lips, M., Prins, C., & Schellekens, M. (2006) Starting Points for ICT Regulations, Deconstructing Prevalent Policy One-liners. Cambridge University Press, Cambridge. Kralaftis, M. G. & McCarthy, P. S. (1999) Subsidy and Public Transit Performance: A Factor Analytic Approach. Transportation, 24, 253–270. Chapter 3
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Lewis, D. & Williams, F. L. (1999) Policy and Planning as Public Choice Mass Transit in the United States. Ashgate Publishing Company, USA. Luther, L. (2005) The National Environmental Policy Act: Background and Implementation. Congressional Research Service Report for Congress. The Library of Congress. Available at http://www.fta.dot.gov/documents/Unit1_01CRSReport.pdf. Moore, T. G. (1986) U. S. Airline Deregulation: Its Effects on Passengers, Capital, and Labor. Journal of Law and Economics, 29(1), 1–28. National Highway Traffic Safety Administration (NHTSA) (2011a) Uniform Guidelines for State Highway Safety Programs, Title No. 1. Available online at http://www.nhtsa.gov/nhtsa/whatsup/tea21/tea21programs/402guide.html#g1. Last access on 3/12/2011. National Highway Traffic Safety Administration (NHTSA) (2011b) Uniform Guidelines for State Highway Safety Programs, Title No. 21. Available online at http://www.nhtsa.gov/nhtsa/whatsup/tea21/tea21programs/402guide.html#g21. Last access on: 3/12/2011. National Highway Traffic Safety Administration (NHTSA) (2011c) Federal Motor Vehicle Safety Standards and Regulations. Available online at http://www.nhtsa.gov/cars/rules/import/fmvss/index.html#SN212. Last access on: 3/12/2011. National Highway Traffic Safety Administration (NHTSA) (2011d) President Obama Announces Historic 54.5 mpg Fuel Efficiency Standard. July 29, 2011. Available at http://www.nhtsa.gov/About+NHTSA/Press+Releases/2011/President+Obama+Announc es+Historic+54.5+mpg+Fuel+Efficiency+Standard. National Highway Traffic Safety Administration (NHTSA) (2009) Traffic Safety Facts. Available online at: http://www-nrd.nhtsa.dot.gov/pubs/811172.pdf. Last access on: 3/12/2011. National Highway Traffic Safety Administration (NHTSA) (2008) Traffic Safety Facts. Available online at: http://www-nrd.nhtsa.dot.gov/Pubs/811162.PDF. Last access on: 3/12/2011. National Highway Traffic Safety Administration (NHTSA) (2002) The Economic Impact of Motor Vehicle Crashes 2000. Available online at: http://www.citavehicleinspection.org/Portals/cita/autofore_study/LinkedDocuments/literature/NHTSA% 20the%20economic%20impact%20of%20motor%20vehicle%20crashes%202000%20US A%202002.pdf. National Research Council (NRC) (2010) Technologies and Approaches to Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles. Washington, DC: The National Academies Press. National Work Zone Safety Information Clearinghouse (2011) Fatalities in Motor Vehicle Traffic Crashes by State and Construction/Maintenance Zone (2008). Available at: < http://www.workzonesafety.org/crash_data/workzone_fatalities/2008>.
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Rodrigue, J.P., Comtois, C., & Slack, B. (2009) The Geography of Transportation System. Routledge, Oxon, UK. Sanderson, V. (2006) Current Strategies to Address Increasing Highway Construction Costs and Reduced Competition. Technical Agenda, AASHTO Subcommittee on Construction, Washington, D.C. Schwieterman, J. P. & Fischer, L. (2010) The Intercity Bus: America’s Fastest Growing Transportation Mode 2010 Update on Scheduled Bus Service. Chaddick Institute for Metropolitan Development, DePaul University. Sullivan, A. & Sheffrin, S. M. (2002) Economics: Principles in Action. New Jersey: Pearson Prentice Hall. Texas Department of Transportation (TxDOT). Texas DOT’s Cost Control Task Force Recommendations Highway Construction and Maintenance Cost Control Ideas. Available at : < http://www.fhwa.dot.gov/programadmin/contracts/costcont.cfm>. Last Access on 1/3/2011. Texas Department of Transportation (TxDOT) (2004) Manual Notice 2004-2. Available at: http://onlinemanuals.txdot.gov/txdotmanuals/env/env_mns.htm. Last Access on 12/12/2012. Texas Department of Transportation (TxDOT) (2008) Manual Notice: 2008-1. Available at :< http://onlinemanuals.txdot.gov/txdotmanuals/hsi/manual_notice.htm>. Last Access on4/22/2011. Texas Department of Transportation (TxDOT) (2011) RM 1431 Improvement Project Archeological Review Meeting Notes. November 1, 2011, TxDOT Austin District. U.S. Department of Labor (DOL) (2011) Minimum Wage Laws in the States. Available at: < http://www.dol.gov/whd/minwage/america.htm#Texas>. U.S. Department of Transportation (USDOT) (2010) Transportation’s Role in Reducing U.S. Greenhouse Gas Emissions, Volume 1: Synthesis Report. Available online at: http://ntl.bts.gov/lib/32000/32700/32779/DOT_Climate_Change_Report_-_April_2010__Volume_1_and_2.pdf. U.S. Department of Transportation, Bureau of Transportation Statistics (BTS) (2001) National Transportation Statistics 2000. BTS01-01. Washington, DC. U.S. Department of Transportation, Federal Highway Administration (FHWA) (1992, August 21) NEPA and Transportation Decisionmaking. Environmental Review Toolkit. Available online at http://environment.fhwa.dot.gov/projdev/tdmpdo.asp. U.S. Department of Transportation, Federal Highway Administration (FHWA) (2002) The Freight Story: A National Perspective on Enhancing Freight Transportation. Available online at http://ops.fhwa.dot.gov/freight/publications/fhwaop03004/index.htm. U.S. Department of Transportation, Federal Highway Administration (FHWA) (2004) Programmatic Agreement for the Review and Approval of NEPA categorically Excluded Transportation Projects between the Federal Highway Administration Texas Division and the Texas Department of Transportation. Accessed April 10, 2012. Available at: http://environment.transportation.org/pal_database/agreement_details.aspx?pal_id=132. Chapter 3
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U.S. Department of Transportation, Federal Highway Administration (FHWA) (2008) 2008 Status of the Nation’s Highways, Bridges, and Transit: Conditions & Performance. Available online at http://www.fhwa.dot.gov/policy/2008cpr/pdfs/cp2008.pdf. U.S. Department of Transportation, Federal Highway Administration (FHWA) (2011) Highway Safety Improvement Program (HSIP). Available online at http://safety.fhwa.dot.gov/safetealu/fact_sheets/ftsht1401.cfm. Last access on 4/22/2011. U.S. Energy Information Administration (EIA) (2009) 2009 Annual Energy Review. Available at: http://www.eia.doe.gov/aer/pdf/pages/sec2_4.pdf. U.S. Environmental Protection Agency (EPA) (2006) Emission Durability Procedures for New Light-Duty Vehicles, Light-Duty trucks and Heavy-Duty Vehicles. 40 CFR Part 86. Available online at http://www.epa.gov/EPA-AIR/2006/January/Day-17/a074.htm. U.S. Environmental Protection Agency (EPA) (2008) 2008 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2006. Available online at http://www.epa.gov/climatechange/emissions/downloads/08_CR.pdf. U.S. Environmental Protection Agency (EPA) (2011a) Cars and Light Trucks. Available at:< http://www.epa.gov/oms/recall.htm>. Last access on 3/12/2011. U.S. Environmental Protection Agency (EPA) (2011b) Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 Through 2010 Available at:< http://www.epa.gov/otaq/fetrends.htm>. Last access on 3/12/2011. U.S. Environmental Protection Agency (EPA) (2012) Mobil Source Emissions—Past, Present, and Future. Updated on January 3, 2012. Available online at http://www.epa.gov/oms/invntory/overview/results/onroad.htm. U.S. Code (2011) Title 23, United States Code. House Transportation and Infrastructure Committee. Available online at http://www.fhwa.dot.gov/legsregs/title23.pdf. U.S. Senate (1984) Oversight of the Motor Carrier Act of 1980: Hearing Before the Committee on Commerce, Science, and Transportation. September 18, 1984. Winston, C., Corsi, T. M., Grimm, C. M., & Evans, C. A. (1990) The Economic Effects of Surface Freight Transportation. Washington, D.C., The Brookings Institution.
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Chapter 4. Movement, Transportation, and Location 4.1 Introduction ........................................................................................................................ 4-1 4.2 Accessibility and Mobility ................................................................................................. 4-1 4.3 Transportation and Location Choice.................................................................................. 4-1 Theories of Business Location ............................................................................................. 4-2 Theories of Residential Location ......................................................................................... 4-5 Policy Impacts ...................................................................................................................... 4-7 4.4 Transportation and Land Values ........................................................................................ 4-7 Theoretical Expectations ...................................................................................................... 4-7 Rail Transit........................................................................................................................... 4-8 Highway Investment ............................................................................................................ 4-9 4.5 Transportation and Wages ............................................................................................... 4-10 4.6 Transportation and Economic Development ................................................................... 4-11 Economic Impact of Relief Routes .................................................................................... 4-13 Economic Impact of Access Management ......................................................................... 4-13 4.7 Summary .......................................................................................................................... 4-15 4.8 References ........................................................................................................................ 4-16
Key Terms
4.1 Introduction Travel is an important facet of most people’s lives, and the permanent nature of transportation infrastructure directly shapes urban form. This form, in turn, impacts land use, land values, and wages. Travel is a byproduct of the need to work, shop, run errands—essentially, produce and consume. The need for travel is a derived demand, as opposed to a direct demand (which consumers get direct satisfaction from), such as the need for food, clothes, and other consumer goods. When transportation infrastructure improvement is the trigger that changes accessibility and mobility, what are the subsequent impacts on business and residential location choice? When businesses and households change their choice of locations, what happens to the land values and wages? This chapter explores the relationship between transportation investment and all of these issues.
CBD: central business district
4.2 Accessibility and Mobility Two key concepts describe the relationship between transportation and location: mobility and accessibility. Mobility conveys the efficiency and amount of movement, usually in terms of travel speeds and distances. Increased mobility generally means more ability to move from one point to another. Accessibility conveys the ease of reaching quality destinations, reflecting both the attractiveness of potential destinations and the ease of reaching them. Those residing in highly accessible locations can more easily reach attractive or desirable activity sites (i.e., within a certain distance or travel time) than those dwelling in less accessible places. Although greater mobility can provide more accessibility for travelers by reducing travel times, accessibility does not depend solely on mobility. Accessibility, especially walk, bike, and transit accessibility, tends to rise with higher densities. In contrast, enhanced mobility can contribute to increased separation of land uses as people are able to travel farther given the same travel times or budgets.
4.3 Transportation and Location Choice Do business and household locations determine the design expansion of the transportation system? Or does the design and extent of the transportation system determine locations choices? Button (2010) likens the continual cycles of cause and effect between transportation and land use to the age-old “chicken or egg” dilemma. Given the longevity of transport infrastructure, system changes often have long-term effects on economic activity. Subsequent changes to residential and employment location patterns will, in turn, influence future transportation demand. The practical decision of whether to treat transportation as a cause or an effect of land use depends upon the research question at hand. Urban and regional Chapter 4
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planners treat transportation as an influential variable that impacts development decisions, whereas transportation planners traditionally model transportation decisions using a four-step process based on trips generated according to land use. The NCHRP’s Land Use Impacts of Transportation Guidebook (1999, p.12) identifies the land use/transportation relationship as “an interaction of supply and demand for accessibility that is further affected by public policies,” as illustrated in Figure 4.1. While land use and transportation influence the supply of accessible residential and commercial properties, demand is affected by the preferences of individuals and businesses (which interrelate with public policies). Figure 4.1: Relationship between Transportation, Land Use, and Accessibility (Source: NCHRP 1999, p.13)
The relative accessibility of locations has important impacts on their land values. Existing land use conditions determine near-term travel behaviors and regional mobility, which influence transportation investment decisions, both long term and near term. Long-term traffic forecasts benefit greatly from long-term land use forecasts, which are affected by near-term (and longer-term) transportation system changes. The spatial distributions of a region’s jobs and households are essential inputs to transportation planning (travel demand) models. To this end, land-use forecasting models, such as the Gravity Land Use Model (GLUM) developed by researchers at The University of Texas at Austin under TxDOT project 0-5667, help analysts forecast future travel times across a region by forecasting job and household locations.
Resource G-LUM is open source software, freely available at http://www.ce.utexas. edu/prof/kockelman/ G-LUM_Website/ homepage.htm.
Theories of Business Location Various business location theories address the different industry types. Many economic activities, such as retail sales and services, depend on access to consumers, while others do not. Central place theory (Christaller 1966) describes a distribution of market centers based on consumer range (the distance that consumers are willing to travel for a certain type of good), while meeting a business’s market threshold (the minimum sales volume or customer base required to meet profit goals). When transportation costs fall, central place theory predicts larger and more Movement, Transportation, and Location
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dispersed market centers as workers and consumers are willing to travel greater distances to access jobs and goods. For example, the introduction of an access-controlled facility in place of an existing arterial allows consumers to travel greater distances for the same amount of travel time. As a result, in many cities, developments near freeway interchanges have become secondary market centers to central business districts (CBD). Likewise, when transportation costs rise, the theory predicts smaller and more concentrated market centers. In contrast, industrial location theory suggests that non-market-sensitive businesses choose locations based on transportation cost considerations such as the distance and weight of goods carried (Weber 1929). Unlike the central place theory, industrial location theory focuses on goods movement rather than employee and customer access. Research suggests that industrial location models are no longer adequate for predicting U.S. business location choices due to a shift over the last 30 years away from basic industries to manufacturing and services. Siting decisions are now less sensitive to transportation investments, particularly at the interregional level. With the nation’s transportation network well established, the cost of moving goods has declined more rapidly than commuting costs, making employee and customer access more relevant for business location. In fact, since the 1960s, jobs have been steadily decentralizing from urban city centers to the suburbs. Allen and Robertson (1983) studied the factors that influence location choices of high-technology businesses in Pennsylvania found that proximity to the market and desired workers ranked higher than proximity to regional surface transport and airports. In a survey of Texas businesses in Lubbock, Houston, San Antonio, and Dallas, accessibility and convenience were most often cited for reasons for business location (Buffington et al. 1997).
Per central place theory, the optimal location of a marketsensitive business is one that minimizes total consumer transportation costs while meeting the minimum market threshold requirements.
On a local level, passenger facilities may be more influential on business location selection than freight transportation, particularly in retail where customer access is key.
The NCHRP Guidebook looks at the relative importance of factors influencing business location primarily from the perspective of access (to workers, customers, and suppliers), as summarized in Table 4.1.
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Relative Importance
Factor Cost and availability of space
High
Comments Businesses weigh the advantages of various locations with the costs of leasing or owning. The availability of lower-cost space outside the CBD leads to suburbanization of businesses.
Access to labor
Businesses have different labor needs. Some locate in the CBD to have the best access to a more diverse pool of highly skilled labor. Some locate near residential areas, which may be preferred by key technical and managerial staff.
Access to customers
Customer base is critical to retail and customer service industries. Customer access is also important to manufacturing firms, although to a lesser degree because manufacturers also consider the locations of suppliers.
Access to highways
Highways receive higher importance as the dominant form of transportation. Highway interchanges give some suburban locations accessibility that rivals the CBD. Highways also support the movement of large manufacturing facilities to suburban and rural sites where land is less expensive.
Near like businesses
Colocation (agglomeration) of similar businesses improves access to workers and customers while facilitating information. Examples include auto dealerships and retailers of the same product locating together to facilitate comparison shopping.
Near suppliers and support services
Proximity to suppliers is the most important for manufacturers but also relevant for office location choices.
Amenities, quality of life, prestige
These factors are most important for firms with many professional and technical workers.
Quality of public services
Public services are most important to manufacturing firms requiring large amounts of water and sewer services for production.
Property tax rates
Manufacturing industries are more sensitive to taxes due to the land-intensive nature of their facilities.
Access to airports
The rise of business travel increases the importance of airport access for headquarters of national and global businesses.
Economic development incentives
Incentives influence the amount and location of redevelopment by reducing costs of development in a specific community or area.
Location of competitors
Retailers want access to large customer bases in areas with multiple stores, but generally do not want to be too close to competitors.
Moderate
Low
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Table 4.1: Factors Influencing the Location Decision of Businesses (Adapted from NCHRP 423A 1999)
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Theories of Residential Location Alonso (1964) developed theories of residential location choice based on agricultural land rents and usage. These theories view household location choice as a utility maximization problem constrained by resources such as housing cost, commuting cost, and costs of all other goods and services. Residential location models typically take the following form: = (
,
,
)
Chapter 7 addresses utility functions.
(4.1)
where housing demand is a function of housing price , transportation cost , and price of all other goods . Households located near employment centers experience lower travel costs, and so can allocate more to housing. In the traditional model with a single CBD, the highest land values are at the CBD, and population density and land values both fall with increased distance from the CBD as accessibility to employment decreases (illustrated in Figure 4.2).
Figure 4.2: BidRent Curve (Source: S-cool.co.uk, undated)
However, the models assume identical relative preferences for location and saving across households, which is far from reality. Proximity to public assets such as parks and schools, ethnic and family loyalty to specific neighborhoods, and preference for architectural styles and housing type or size all influence residential location choice (Giuliano 2004). The basic models also do not account for the growing number of multi-worker Chapter 4
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A survey of Texas households revealed that proximity to work and schools, freeway access, neighborhood type, and school quality were the top considerations for residential location (Buffington et al. 1997).
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households that must accommodate more than one commuter’s work trip. Further, not all jobs are in a CBD. Lastly, the trend towards higher rates of job turnover and greater moving costs suggests that households may locate to increase accessibility to future employment opportunities instead of reducing commute costs to current jobs. Table 4.2 summarizes the relative importance of factors influencing household location. Relative Importance
Factor
High
Housing costs
Household budgets must accommodate cost of housing and other goods.
Access to jobs
High access to jobs is especially important in large metropolitan areas, where travel distances can be long and transport systems congested.
Access to goods and services
Preferences vary with household type. Singles prefer to live near entertainment, families with school age children prefer access to good schools and parks, and empty nesters seek leisure and culture.
Community residents
Most people prefer to live near others who are like them.
Quality of nonschool public services
Sometimes households consider police protection and/or other public services when selecting a neighborhood. Residences with appealing views, attractive design, and low crime rates are preferred over those located directly adjacent to industry or heavy traffic. Study results are mixed on whether or not property taxes influence household location choice.
Moderate
Low
Amenities and quality of life
Property tax rates
Comments
Table 4.2: Factors Influencing the Location Decision of Households (Adapted from NCHRP 423A 1999)
As out-of-pocket travel costs have declined more rapidly than housing costs, jobs have decentralized and commute distances have increased. The average work trip distance rose from 8.55 miles in 1983 to 12.08 miles in 2001, and then to 12.20 miles in 2009 (USDOT 2004, USDOT 2011). Though suburbanization has served as the primary pattern of residential growth in urban areas, note that low-income and minority groups have not been able to decentralize to the same degree as other groups. As jobs decentralize, disadvantaged groups face decreasing access to employment opportunities. The term “spatial mismatch” was coined to describe this geographic mismatch between inner-city workers’ home locations and suburban employment locations (Kain 1968). Movement, Transportation, and Location
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Policy Impacts The business and residential location theories discussed here assume a perfectly competitive land market. But municipal policies such as zoning regulations and infrastructure provisions contribute to a highly regulated land market—far from perfect competition. Most communities have long-range land use plans that drive zoning ordinances and growth management policies. Concerned with tax revenue generation, local governments tend to encourage land uses that contribute positively to the tax base and discourage land uses that incur public costs. The appetite for tax revenue often drives neighboring municipalities to compete for economically beneficial developments, often with incentives like property tax abatements and subsidized loans (Giuliano 2004). Due to low property taxes, California cities are increasingly relying on sales, hotel, and other consumption taxes to generate revenue. To boost consumption taxes, municipalities encourage the development of high-volume big box retailers. At the same time, low-income housing developments are discouraged because they increase public expenditures service requirements such as police departments and schools, which often cannot be covered by the meager property taxes generated by these housing developments (Altshuler et al. 1993).
Because urban development cannot occur without public services and utilities, policy decisions regarding infrastructure largely determine the available supply of developable land.
4.4 Transportation and Land Values Transportation investments are often viewed as growth generators. Transit investments, particularly rail, are frequently perceived as triggers for economic revitalization in central cities, while highway facilities encourage growth by increasing access to inexpensive land farther from the city center (Ryan 1999). Such perceptions have made transportation investment decisions hot topics of debate among policy makers.
Theoretical Expectations Many theories seek to describe the land value/accessibility interaction. Per the theory of residential location choice, households with lower commute costs can allocate more of their budget to housing, thereby bidding up property values. Based on this bid-rent curve, when commuting costs fall, city center rents will decrease due to a relative decrease in location advantage. Consequently, consumers taking advantage of decreased commute costs will increase commute distances, extending the city’s boundaries. When commute costs rise, the theory predicts the reverse: households will reduce travel by locating closer to the city center and thus bid up land values (Giuliano 2004). When new transportation infrastructure is introduced to a network, destinations served by the new facilities experience decreased travel times or travel cost, thus increasing these locations’ relative accessibility. Households and businesses served by the new transportation infrastructure should experience a rise in their land value (Ryan 1999).
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Rail Transit Unlike highway investment, rail transit investment generally changes accessibility only in the immediate vicinity of the rail corridor, specifically near access points, or rail stations. When activity increases with rail station access, a corresponding increase in land values should reflect the uptick in activity. A traditional approach to assessing the impact of rail transit investments on land use is to examine how property values vary with distance to a station. Results of these price-vs.-distance case studies have been inconsistent, as indicated by the following examples: • A 1992 analysis of home sale prices within 1.25 miles of the Metropolitan Atlanta Rapid Transit Authority’s (MARTA) New East Line found that the line had positive effects on property values to the south, a neighborhood with lower-middle-class households, but negative effects on property values to the north, in a neighborhood of primarily middle class households with some wealthy households. To the south, values increased approximately $1,045 for every 100 feet a property was closer to the line. To the north, property values fell approximately $965 for every 100 feet a property was closer to the line (Nelson 1992). • Gatzlaff and Smith (1993) examined the sale prices of specific properties in successive sales before and after the 1984 opening of Miami’s Metrorail, which was planned to revive development in economically depressed areas of the city. In higher priced neighborhoods experiencing growth, the Metrorail weakly increased existing property values. For neighborhoods already in economic decline, the Metrorail provided no benefit to property values. • A 1997 study of the impact of the Northern California Bay Area Rapid Transit (BART) system on property values also yielded contradictory results. While apartment units in booming suburban Contra Costa County near the Pleasant Hill BART station rented for 15¢ more per square foot than apartments farther than a quarter-mile from the station, apartment rents did not differ with proximity to the Richmond BART station in northern Alameda County, where a poor local economy and high crime rates deterred station-area development (Cervero & Landis 1997). In the Atlanta case, low-income households are more likely to use transit and benefit from improved transit service, so their property values rise. Higher income households, in this case, are less likely to use the transit service and experience insignificant time savings. Property value benefits arise only in areas that value the service. In Miami’s case, with low Metrorail ridership, property values saw no impact from the investment. Systems that have the highest ridership rates and reach more locations experience the greatest gain in property values from rail transit investment. Furthermore, rail transit’s impact on property values is felt within very limited distances from the stations. The highest impacts are localized and experienced in fast-growing Movement, Transportation, and Location
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These case studies support the idea that property values increase when travel times fall—but only in areas that value the service.
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core areas that are already economically robust. For areas in economic decline, rail investment alone is not sufficient to induce development. While the previous three case studies used only distance to measure the effect of rail transit on nearby property values, Bowes and Ihlandfeldt (2001) developed a price model to measure impacts of four potential variables: 1) reduction in commute costs, 2) attraction of retail activity to a neighborhood, 3) increase in noise and emissions, and 4) potential rise in crime. Using Atlanta homes sales data, they estimated that properties within a quarter mile of MARTA rail stations are sold for 19% less than properties more than 3 miles away, while properties between 1 and 3 miles from a station enjoyed significantly higher values. These results suggest the following: • Properties adjacent to rail stations may be negatively impacted by externalities such as noise and traffic, but those at an intermediate distance (beyond the externality effects) still benefit from transportation access. • Additionally, houses within a half-mile of a rail station with parking lots experienced higher densities of crime while houses between half a mile and 3 miles of the rail station experienced decreased crime, suggesting that the presence of a parking lot at the rail station changes the crime distribution. • Property values rose more for intermediate distances in high-income neighborhoods than in low-income neighborhoods.
Highway Investment Though the greatest land value increases tend to occur near infrastructure investments, relative accessibility changes occur throughout the roadway network. Furthermore, investments in already extensive urban networks will have less pronounced impacts than those in rural areas where access is relatively limited to begin with. Early highway studies following the highway building boom of the 1950s and 1960s showed significant increases in land values alongside new interstates (see Adkins 1959 and Mohring 1961). Almost all studies, however, focus on nearby parcels and do not examine whether land values fall elsewhere. More recent studies yielded the following insights:
Because highways exist as a component of a larger network, their impacts on land value are not as directly measurable as those of rail transit.
• Gamble et al. (1974) examined residential sales prices within 4000 feet of Virginia’s IH 495 corridor and found average property value increases of about $3,000 in 1972 dollars. However, for houses abutting the highway (within 400 feet), the new freeway reduced land values, presumably due to noise and boundary effects, and perhaps emissions impacts. • A 2000 study of home sales in Toronto found that the presence of a highway within 2 kilometers had negative effects on property values Chapter 4
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(Haider & Miller 2000). • Boarnet and Chalermpong (2001) examined residential sales price impacts of the new Orange County tollway system in the Foothill and San Joaquin Hill corridors between 1988 and 1999 (before and after the tollways’ construction) as compared to sales prices along a control corridor. The nearest highway interchange was found to be a significant and positive factor. • Lewis et al. (1997) examined the impact of elevated, at-grade, and depressed highway sections on Texas property values, and found that cities with strong land use regulations more frequently experienced land value increases following highway construction. The type of highway improvement and property also played a role: the highest residential land values occurred near depressed freeway sections (which reduce the nuisance of noise, emissions, and other negative effects), whereas community properties benefitted most near at-grade freeway sections, thanks to added visibility. Both types of land use were estimated to exhibit their lowest values near elevated freeway sections. Like rail transit, the impact of highway investment on adjacent property values is context-specific. Due to the presence of frontage roads alongside most Texas highways, the impacts on immediately adjacent properties can be significant, particularly due to increased access for commercial properties. In general, economically thriving communities experiencing population and employment growth with available land for development tend to benefit the most from investments, at least from a local perspective. When growth is not present, investment impacts are not significant. However, from a regional perspective, there is little evidence that highway investment generates net economic development.
Local economic growth is often a result of activity redistribution from adjacent locations.
Just as added transportation capacity can improve the accessibility and property value of specific locations, the temporary delay caused by major highway construction can hamper access and economic activity and value locations. Luskin and Chandrasekaran (2005) surveyed Dallas office tenants who had experienced traffic delays during the construction of the nearby High Five Interchange (US 75 at IH 635). The researchers asked for tenants’ stated preferences for office rents based on temporary commute time increases and estimated an average rent decrease of $22 per person-hour of delay higher than the average hourly wage of private-sector U.S. employees at that time ($15.71 in July 2004).
4.5 Transportation and Wages In addition to transportation costs affecting land values, the traditional model of the city implies that transportation costs affect wages. All else equal, a worker residing in the outlying areas of a city would be willing to give up a higher-paying job in the CBD for a lower-paying job close to home, as long as the commute cost savings makes up the wage difference. Then, as is the case Movement, Transportation, and Location
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with land values, the highest wages should exist at the city core, with wages declining as jobs migrate outward. The concept is described in the term urban wage gradient, which refers to the theoretical decrease in wages with increasing distance to the urban core. When a secondary employment center is introduced, some workers residing near the CBD may reverse commute to this secondary center. Because reverse commuting costs are typically less than those of the traditional commute (due to decreased congestion), the slope of the reverse-commute wage gradient is theoretically less steep than that of the commuting wage gradient (Button 2010).
A recent study of the urban wage gradient in two metro areas found that up to 15% of wage differences could be attributed to commute time difference (Timothy and Wheaton 2001).
Although the studies are not completely consistent, the evidence supports the idea that urban wage gradients exist in some form, particularly for cities with significant CBDs and for workers commuting in from the suburbs. However, imperfections in the transportation market (e.g., public transportation subsidies and employer-subsidized parking) and job benefits not reflected in wages (e.g., flexible work hours and free meals) complicate the simple nature of the urban wage gradient, along with the specifics of different occupations and uniqueness of each job and worker.
4.6 Transportation and Economic Development Transportation infrastructure enjoys a special relationship with regional economic development. Investment in transportation systems typically facilitates economic growth, but it does not necessarily guarantee direct economic gains. Clearly, efficient transportation systems and economic development depend heavily upon each other, but in a way that is difficult to quantify or state explicitly. In general, transportation infrastructure investments are most beneficial to regional development in the early stages of economic growth. Opening up new markets and creating a more mobile network for workers and consumers is vital to initiate growth. Transportation improvements bring not only direct injections of cash in a local economy throughout the construction process (thanks to having local workers involved in planning, design, and construction) but provide multiplier effects that reverberate on a larger spatial and temporal scale. Transportation economists have identified distinct types and levels of economic benefits from infrastructure investments:
The nature of transportation’s influence on wage rates is far more complex than the basic theory holds.
• Primary – economic gains as a direct result of infrastructure construction. These benefits are muted by outside bidders and out-ofarea planning and design work. • Secondary – benefits derived from operation and maintenance of the facilities. Upon construction, more steady local employment is generated through tasks such as road maintenance, toll collection, airport facilities operation, etc. Chapter 4
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• Tertiary – typically emphasized as a critical source of benefits, this level includes the economic development that is drawn by the newly available infrastructure. For example, a developer may decide to bring a shopping center to the area after the completion of a major highway that brings large volumes of traffic through a previously underdeveloped region. In turn, the shopping center generates revenue and creates jobs. • Perpetuity – more abstract concept of large-scale shifts in economic structure. For example, through the extensive interstate connections, airports, and warehousing investments in Memphis, Tennessee, the area has developed into an economy supported greatly by the shipping industry. This stage is a long-term result of major investments. Figure 4.3 illustrates how these economic multiplier benefits are related to long-term and widespread economic growth.
Figure 4.3: Types and Levels of Economic Benefits from Transportation Investment (Source: Button 2010)
While providing a certain level of mobility and accessibility is key to stimulating economic growth, a law of diminishing returns tends to apply to infrastructure development and economic growth. Once basic levels of mobility and access are provided, further system improvements do not bring the same magnitude of economic gains (Button 2010). In established areas, relief routes (such as loops and bypasses) are typically built to relieve traffic pressures along the main roads through the downtown. This relief can increase the function of a previously established system, acting as a catalyst for economic growth. The economic benefits of infrastructure improvements are often moderated economic losses from business relocations, sale slumps (and reduced tax revenues) during construction, and shifts in employment and land values. Buffington et al.’s (1997) 4-year study of economic impacts from Texas freeway improvements indicated a variety of positive and negative impacts on rural and urban areas. Among the conclusions were indications that Movement, Transportation, and Location
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Most studies of highway impacts report great variability in the results of infrastructure improvements. Chapter 4
construction processes negatively impact sales and tax revenues, while user costs and employment effects depend on freeway type. Depressed freeways tended to be less productive for business, while elevated sections were less desirable for residents. Land value effects varied widely, based mostly on “factors of location and accessibility, overall economic health of the locale, growth rates, and subsequent demand for various types of property” (Buffington et al. 1997, p. 34).
Economic Impact of Relief Routes Relief route construction is a common practice for growing suburban and rural communities in Texas. While their construction relieves congestion in the town center arterials, a community’s CBD can be significantly impacted by the reduced traffic. Studies of relief routes in Texas indicate a positive public reaction to the reduced traffic, a negative reaction from downtown business owners due to a noticeable decline in sales, and overall changes in the economic structure (Handy et al. 2000). The short-term business loss in CBD is often counteracted by business relocations, resulting in overall economic rebounds. In Wisconsin, Kansas, Iowa, Texas, and North Carolina, the general consensus was mixed, in that bypasses neither spurred substantial economic growth nor hindered it overall (Leong & Weisbrod 1999). Outside of economic considerations, the decline of an historic and aesthetically pleasing CBD is frequently cited as a loss. Of course, counter arguments claim that new investment potentially captured by the relief route, in term of generated taxes, could be used to revitalize the CBD, creating a safer, quieter community more separated from the busy flow of commerce on the periphery (Handy et al. 2000).
The economic impact of relief routes is discussed in detail in Chapter 10’s Case Study 2.
Economic Impact of Access Management Access management controls traffic movement between roadways and adjacent land uses with medians, driveway design, turn lanes, traffic signals, and similar facilities. The goal is to improve safety and facilitate travel while maintaining access to abutting properties. Roadway designs that provide a structured pattern of flow with fewer conflict points expose travelers to fewer risks, allow a greater response time to potential hazards, and reduce delay (TxDOT 2011). Driveway density and median type are features that can significantly impact performance and safety. As compared to two-way left turn lanes and undivided roadways, raised medians are most effective at reducing crashes (Gluck 1999) and improving flow (TRB 2000). A nontraversable median removes left turns from through travel lanes, clearly defines upcoming conflict points, and creates pedestrian refuges. Controlling driveway density is another effective method of increasing safety and flow. Most studies find a direct rise in crash rates with increasing driveway frequency; for example, Gluck (1999) estimated a 4% increase in crashes for each additional access point per mile. Driveway density has a very similar relationship with flow as well; the Highway Capacity Manual indicates a 2.5 mph drop in free flow speed for every 10 access points per mile, up to 40 or more access points (TRB 2000). Chapter 4
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A nontraversable median can reduce pedestrian crash counts by up to 50% (ODOT 1996).
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Although access management techniques may improve safety and flow along a corridor, adjacent businesses fear a reduction in sales, mainly from left-turn restrictions and driveway access changes that reduce customer access. Local economies may be largely unaffected by access management projects, but individual businesses can experience varied results. For example, Eisele (2000) found that after a median project was completed in Texas, overall corridor land values increased by nearly 7% while a property owner in another study (Weisbrod & Neuwirth 1998) indicated rent rates on a property fell from $6.50 to $5.00 per square foot after a left-turn restriction was implemented. Individual cases of these negative impacts may be explained by considering the store type and customer base: establishments that rely on passing traffic tend to be more negatively impacted by access management than destinationbased stores with a more permanent set of customers (Rose et al. 2005). Specifically, gas stations, convenience stores, and motels are more likely to be negatively impacted than restaurants and grocery stores, but the former tend to be more easily relocated, resulting in economic shifts rather than losses (Weisbrod & Neuwirth 1998). Negative effects on businesses are most likely to occur during construction phase of a project (Eisele & Frawley 2000). While sales may drop during this time, recovery typically occurs within a few months (Weisbrod & Neuwirth 1998). Some businesses report that increased advertising is necessary to maintain competitiveness during and immediately after construction.
Studies have shown that business owners typically overestimate the negative impacts of access management (Eisele 2000) and generally experience minimal negative impacts (TxDOT 2011).
A study of customer opinion in Texas indicated that site accessibility was less important than customer service, product quality, and product price, which are all factors under the control of the business owners (Eisele & Frawley 2000). Surveys indicate that most customers are unaffected by or unaware of left-turn restrictions. Furthermore, 80% of patrons surveyed reported that left-turn restrictions did not affect the frequency of their visits to the business, and 84% reported making a U-turn or multiple-turn maneuver to access the business, which indicates a willingness to travel out of the way (Weisbrod & Neuwirth 1998). Vu (2002) used probit and logit models with willingness-to-pay (WTP) considerations to further interpret the relationships between access management and public perception of impacts. The higher a business’s WTP, the more pessimistically the owner viewed access management’s effect on business. The results suggest that “economic thresholds exist for any particular business, and at a certain limit the loss of patronage due to access will force it to consider relocating to maintain economic viability” (Vu 2002, p. 29). Vu’s 2002 Washington State study also noted that retail businesses (e.g., salons, banks, and clinics) perceive less negative impacts due to their more established customer base. Interestingly, convenience stores tend to view access management in a positive light, perhaps because most are already located at strategic corner locations, rather than mid-block. Movement, Transportation, and Location
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Chapter 8 details probit and logit model techniques.
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4.7 Summary To fully appreciate the “chicken and egg” relationship between transportation infrastructure and location choice of households and businesses, the distinction between access and mobility must be understood. Relative accessibility is the foundation for location choice theories, providing a basis for land values and wage gradients. The complex relationship between transportation and urban form will likely grow more complicated as we move towards an information-based economy that allows more flexibility in household and business location. When job turnover rates increase, households tend to value access to overall job opportunities over access to any specific job. When flexible work relationships (e.g., telecommuting) are introduced, the existing location choice models become even more inadequate, given the complex relationship between transportation and land use in modern metropolitan areas.
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4.8 References Adkins, W. (1959) Land Value Impacts of Expressways in Dallas, Houston, and San Antonio, Texas. Highway Research Bulletin 227, 50–65. Allen, D. N. & Robertson, G. E. (1983) Silicon, Sensors and Software: Listening to Advanced Technology Enterprises in Pennsylvania. Institute of Public Administration, Pennsylvania State University. Alonso, W. (1964) Location and Land Use. Cambridge, MA: Harvard University Press. Altshuler, A., Gomez-Ibanez, J., & Howitt, A. (1993) Regulations for Revenue: The Political Economy of Land Use Exactions. Washington, DC: Brookings Institution. Boarnet, M. & Chalermpong, S. (2001) Travel by Design: The Influence of Urban Form on Travel. New York: Oxford University Press. Bowes, D. R. & Ihlanfeldt, K.R. (2001) Identifying the Impacts of Rail Transit Stations on Residential Property Values. Journal of Urban Economics 50, 1–25. Buffington, J., Vadali, S., Womack, K., Zimmer, R., McCully, W., Nikolaou, M., & Lewis, C. (1997) Social, Economic, and Environmental Effects of Elevated, Depressed, and AtGrade Level Freeways in Texas. Texas Transportation Institute, Texas A&M University. Available online at http://tti.tamu.edu/documents/1327-6F.pdf. Buffington, J. L., Womack, K. N., & Lewis, C. (1997). Social and Economic Effects of Elevated, Depressed, and At-Grade Freeways in Texas, TxDOT Research Report 1327-1. Button, K. (2010) Transport Economics. Northampton, MA: Edward Elgar Publishing. Cervero, R. & Landis, J. (1997) Twenty Years of the Bay Area Rapid Transit System: Land Use and Development Impacts. Transportation Research , 31A(4), 309–333. Christaller, W. (1966) Central Places in Southern Germany. Translated by C. Bushin. Englewood Cliffs, NJ: Prentice Hall. Cohen, J. P. & Paul, C. M. (2007) The Impacts of Transportation Infrastructure on Property Values: A Higher-Order Spatial Econometrics Approach. Journal of Regional Science, 47(3), 457–478. Crane, R. (1996) The Influence of Uncertain Job Location on the Urban Form and the Journey to Work. Journal of Urban Economics, 39(3), 342–358. Eisele, W. & Frawley, W. (2000) A Methodology for Determining Economic Impacts of Raised Medians: Final Project Results, Texas Department of Transportation, Texas Transportation Institute, Texas A&M University. Available online at http://tti.tamu.edu/documents/3904-4.pdf. Gamble, H., Sauerlender, O., & Langley, J. (1974) Adverse and Beneficial Effects of Highways on Residential Property Values. Transportation Research Record 508, 37–48. Gatzlaff, D. & Smith, M. (1993) The Impact of the Miami Metrorail on the Value of Residences Near Station Locations. Land Economics, 69(1), 54–66. Gluck, J., Levinson, H. S., & Stover, V. G. (1999) NCHRP Report 420: Impacts of Access Management Techniques, National Cooperative Highway Research Program, Movement, Transportation, and Location
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Transportation Research Board, Washington, D.C., National Academy Press. Available online at http://www.accessmanagement.info/pdf/420NCHRP.pdf. Guiliano, G. (2004) Land Use Impacts of Transportation Investments: Highway and Transit. Chapter 9 in The Geography of Urban Transportation. New York: The Guilford Press. Haider, M. & Miller, E. (2000) Effects of Transportation Infrastructure and Location on Residential Real Estate Values: Application of Spatial Autoregressive Technique. Transportation Research Record 1772, 1–8. Handy, S. L., Kubly, S., Jarrett, J., & Srinivasan, S. (2000) Economic Effects of Highway Relief Routes on Small and Medium-Size Communities Center for Transportation Research, The University of Texas at Austin. Available online at http://www.utexas.edu/research/ctr/pdf_reports/1843_1.pdf. Handy, S. L. & Niemier, D. A. (1997) Measuring Accessibility: An Exploration of Issues and Alternatives. Environment and Planning 29, 1175–1194. Kain, J. F. (1968) Housing Segregation, Negro Employment, and Metropolitan Decentralization, Quarterly Journal of Economics 82(2),175–197. Leong, D. & Weisbrod, G. (1999) Summary of Highway Bypass Studies. Wisconsin Department of Transportation and Economic Development Research Group. Available online at http://www.edrgroup.com/pdf/Town-Bypass-Case-Studies.pdf. Lewis, C.A., Buffington, J. L., Vadali, S. R., & Goodwin, R. E. (1997) Land Value and Land Use Effects of Elevated, Depressed, and At-Grade Level Freeways in Texas. TxDOT Research Report 1327-2. Luskin, D. & Chandrasekaran, A. (2005) Employer Valuations of Employee Commuting Time: Case Study of Office Tenants in North Dallas, Texas. Transportation Research Record 1902, 10–17. Mohring, H. (1961) Land Values and the Measure of Highway Benefits. Journal of Political Economy 72, 236–249. National Cooperative Highway Research Program (1999) NCHRP Report 423A: Land Use Impacts of Transportation: A Guidebook. National Cooperative Highway Research Program, Washington, DC: National Academy Press. Nelson, A. C. (1992) Effects of Elevated Heavy-Rail Transit Stations on House Prices with Respect to Neighborhood Income. Transportation Research Record 1359, 127–132. Oregon Department of Transportation (ODOT) (1996) Discussion Paper No. 4: Medians. Oregon Department of Transportation. Available online at http://www.oregon.gov/ODOT/HWY/ACCESSMGT/docs/Medians.pdf?ga=t. Rose, D., Gluck, J., Williams, K., & Kramer J. (2005) NCHRP Report 548: A Guidebook for Including Access Management in Transportation Planning, National Cooperative Highway Research Program, Transportation Research Board, Washington, D.C., National Academy Press. Available online at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_548.pdf.
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Ryan, S. (1999) Property Values and Transportation Facilities: Finding the Transportation-Land Use Connection. Journal of Planning Literature 13(4), 412–427. S-cool, the Revision Website (undated) Central place and Bid-rent theories. Available at http://www.s-cool.co.uk/a-level/geography/urban-profiles/revise-it/central-place-and-bidrent-theories. Srour, I., Kockelman, K. M., & Dunn, T. (2002) Accessibility. Transportation Research Record No. 1805, 25–34. Timonthy, D. & Wheaton, W. (2001) Intra-Urban Wage Variation, Employment Location, and Commuting Times. Journal of Urban Economics 50, 338–366. Transportation Research Board (TRB) (2000) Highway Capacity Manual Transportation Research Board. Texas Department of Transportation (TxDOT) (2011) Access Management Manual. TxDOT Design Division. Austin, Texas. Available online at http://onlinemanuals.txdot.gov/txdotmanuals/acm/acm.pdf. US Census Bureau (2000) Working at Home: 2000. PHC-T-35. Available online at http://www.census.gov/population/www/cen2000/briefs/phc-t35/index.html. US Department of Transportation (USDOT) (2011) Summary of Travel Trends: 2009 National Household Travel Survey. Available online at http://nhts.ornl.gov/2009/pub/stt.pdf Vu, P. (2002) Economic Impacts of Access Management. Washington State Department of Transportation, Washington State Transportation Center, University of Washington. Available online at http://www.wsdot.wa.gov/research/reports/fullreports/554.1.pdf. Wang, Y., Kockelman, K. M., & Wang, X. (2011) Anticipating Land Use Change Using Geographically Weighted Regression Models for Discrete Response. Forthcoming in Transportation Research Record. Weber, A. (1929) Theory of the Location of Industry. Chicago: Chicago University Press. Weisbrod, G. & Neuwirth, R. (1998) Economic Effects of Restricting Left Turns. National Cooperative Highway Research Program. Available online at http://www.edrgroup.com/pdf/left-turns-digest.pdf. Williams, K. (2000) Economic Impacts of Access Management. Center for Urban Transportation Research, University of South Florida. Available online at http://www.cutr.usf.edu/research/access_m/pdf/Econeffects.pdf. Zax, J. (1991) Compensation for Commutes in Labor and Housing Markets. Journal of Urban Economics 30(2), 192–207.
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Chapter 5. Investment and Financing 5.1 Introduction ........................................................................................................................ 5-1 5.2 U.S. Railroad, Road, and Bridge Investment Needs .......................................................... 5-1 Importance of Transportation Infrastructure ........................................................................ 5-1 U.S. Railroad Infrastructure ................................................................................................. 5-2 Freight Rail ....................................................................................................................... 5-2 Passenger Rail ................................................................................................................... 5-2 U.S. Roads and Bridges ....................................................................................................... 5-3 Current Condition ............................................................................................................. 5-3 Pavement Maintenance ..................................................................................................... 5-4 Bridge Maintenance .......................................................................................................... 5-4 5.3 Financing ........................................................................................................................... 5-5 Revenue Sources .................................................................................................................. 5-5 Expenditure Sources ............................................................................................................ 5-6 Traditional Project Delivery Methods ................................................................................. 5-8 Innovative Financing ........................................................................................................... 5-8 Section 129 Loans ............................................................................................................. 5-9 State Infrastructure Banks (SIBs) ..................................................................................... 5-9 Private Activity Bonds (PABs) ....................................................................................... 5-10 The Transportation Infrastructure Finance and Innovation Act (TIFIA)........................ 5-11 TIGER Grants and MAP-21 ........................................................................................... 5-12 Tax Increment Financing (TIF) ...................................................................................... 5-13 Public-Private Partnerships (PPP) .................................................................................. 5-14 Existing Facilities (Concession or Lease) ....................................................................... 5-17 More Alternative Finance Methods in Texas ................................................................. 5-21 5.4 Summary .......................................................................................................................... 5-22 5.5 An In-Depth Look ............................................................................................................ 5-23 FY2010 Funding Breakdown ............................................................................................. 5-23 TIF Legislation and Valuation ........................................................................................... 5-24 Recent Changes in Texas Laws ...................................................................................... 5-24 TIF Valuation .................................................................................................................. 5-24 TRENDS ............................................................................................................................ 5-25 5.6 References ........................................................................................................................ 5-27
Key Terms
5.1 Introduction Transportation agencies face municipal, state, and federal budget constraints, so awareness of funding priorities based on the physical condition of transportation systems is key. The decisions to rehabilitate, expand, and construct new systems depend on the conditions of existing systems and competing modes. For example, expanding rail capacity may decrease the need for building new roadways. Furthermore, the scarcity of traditional transportation funding is contributing to a growing gap between the funds required for improvements and the funds available to do so. This chapter discusses infrastructure improvements required as well as the innovative financing methods designed to fill this gap, such as Section 129 loans, state infrastructure banks (SIBs), the Transportation Infrastructure Finance and Innovation Act (TIFIA), tax increment financing (TIF), private activity bonds (PABs), and public-private partnerships.
VMT: vehicle miles traveled SIB: state infrastructure banks
PAB: private activity bond
TIFIA: Transportation Infrastructure Finance and Innovation Act TIGER: Transportation Investment Generating Economic Recovery TIF: tax increment financing TIRZ: Tax Increment Reinvestment Zone
5.2 U.S. Railroad, Road, and Bridge Investment Needs Importance of Transportation Infrastructure A well-functioning and efficient transportation system depends on both its capacity and infrastructure condition. The impacts of investment in transportation infrastructure fall into four categories:
PPP: public-private partnership
1. User Impacts: monetary cost of travel, safety, travel time, comfort, and reliability. 2. Economic Impacts: change in employment, personal income, property values, business sales volume, and business profit. 3. Government Fiscal Impacts: changes in public revenues and expenditures. 4. Other Social Impacts: effects on social indicators such as air quality and other environmental conditions. One way to calculate the impact of transportation investment is to use an economic multiplier. The economic multiplier approach assesses the direct and indirect impacts of transportation projects on business attraction, expansion, retention, or tourism. For example, Weisbrod and Weisbrod (1997) reported that the economic output multiplier for the national value for most transportation investments is 2.5 to 3.5, while the value for state and local impacts is 2.0 to 2.5 and 1.5 to 2.0, respectively.
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See Chapter 7 for more details on the economic multiplier approach.
Investment and Financing
Concept Example: Economic Multiplier If $100 million is invested in building a highway, economic activity at the state level would be expected to receive a net increase of $200–$250 million. In addition, the USDOT reports that every $1 billion invested in transportation infrastructure creates 42,000 jobs and generates more than $2 billion in economic activity (Sinha & Labi 2007).
U.S. Railroad Infrastructure The U.S. railroad system is divided into two parts: freight and passenger rail. Railroad tracks are privately owned (as opposed to highways, which are predominantly publicly owned). Although railroads are vastly more efficient than roadways, railroad networks in the U.S. are shrinking.
Freight Rail • Approximately 42% of all intercity freight on a ton-mile basis travels via rail (Government Accountability Office 2006).
Federal financing programs are available to refurbish and expand the nation’s valuable rail resources.
• Freight transportation demand is forecasted to increase from 19.3 billion tons in 2007 to 37.2 billion in 2035 (Cambridge Systematics 2007). • Approximately $148 billion is needed to improve the railroad system to accommodate the rising increase in freight demand (Cambridge Systematics 2007). A number of federal loans and grants are available to increase investment in railroad systems. The Federal Railroad Administration (FRA) offers various loan options through the Railroad Rehabilitation and Improvement Financing Program (RRIF) for acquiring, improving, rehabilitating, and refinancing intermodal or rail equipment or facilities. RRIF can also be used for developing and establishing new intermodal or railroad facilities. The FRA is authorized via SAFETEA-LU to provide direct loan and guarantees up to $35 billion (FRA 2011b).
Passenger Rail • The Passenger Rail Working Group (PRWG) anticipates a required investment of $7.4 billion from 2007 to 2016 to address the total capital cost of an intercity rail network (with an additional total of $290.9 billion required between 2016 and 2050. • Per PRWG estimates, approximately $4 billion in fuel will be saved annually by diverting passengers to rail if the proposed investments are made. • Investment in passenger rail may decrease required investment in other modes of transportation. Investment and Financing
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Assisting investment in passenger rail, the FRA provides Amtrak grants for both operation and capital improvement. The Passenger Rail Investment and Improvement Act of 2008 (PRIIA) authorized funds for the USDOT to issue grants for three new federal intercity rail capital assistance programs: Intercity Passenger Rail Service Corridor Capital Assistance, High Speed Rail Corridor Development, and Congestion Relief.
U.S. Roads and Bridges Current Condition The Unites States has over 4 million miles of public roads and nearly 3.7 billion square feet of bridges as of 2008—and on those roads, 3 trillion vehicle miles traveled (VMT)(USDOT 2008b). As Figure 5.1 shows, transportation infrastructure in Texas has not met the increase in VMT in the past 15 years. As a result, a gap is growing between VMT growth and new lane miles. 77%
72% 61%
55%
60%
Figure 5.1: Growth in Major Texas Cities, 1990–2005 (Source: 2030 Committee 2009)
Population
36% 37%
31%
22% 25%
27%
Vehicle Miles Traveled Lane Miles
10%
Austin
Texas roads and bridges require billions of dollars in maintenance and repair; state funding alone will not meet the demand.
Dallas / Ft. Worth
El Paso
Houston
Because of the disparity between highway growth and VMT rise, roadway congestion is increasing. Americans spend 4.2 billion hours each year in traffic, which amounts to an annual cost of $78.2 billion in wasted time and fuel (TRIP 2010a). Greater roadway congestion has increased the quantity of fuel wasted from 1.7 billion gallons in 1999 to 2.9 billion gallons in 2005 (TTI 2007). Inadequate roadway conditions play a significant role in approximately one-third of traffic fatalities and cost American motorists $67 billion a year in extra vehicle repairs and operating costs (TRIP 2010b). From 1990 to 2008, new road mileage increased by 4%, while vehicle travel increased by 39% (TRIP 2010a). A National Surface Transportation Policy and Revenue Commission study showed that for the 15-year period from 2005 to 2020, $130 billion to $240 billion should be invested in highways for adequate capacity and maintenance, yet the current spending level falls well short of that number at $70.3 billion (ASCE 2009). A key element in roadway infrastructure is bridges. In Texas, 18% of bridges are structurally deficient or functionally obsolete (TRIP 2010b). A Chapter 5
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structurally deficient rating reflects the bridge’s integrity with regard to the condition of the bridge deck, superstructure, and substructure, while functionally obsolete refers to inadequate geometry, clearance, or alignment to the roadway approaching the bridge.
Pavement Maintenance Because 13% of Texas’s major roads are not in “good” or better condition (TxDOT 2011e), Texas motorists spend $5.3 billion on extra vehicle repairs and operating costs (TRIP 2010b). (“Good” condition indicates a high-quality ride with low levels of distress [2030 Committee 2009].) Based on the 2030 Committee’s report on Texas’s transportation needs, $7.2 billion will be required for routine pavement maintenance of existing roads between 2008 and 2030. Also, $77 billion will be required for maintenance and rehabilitation for 90% of Texas’s roads to reach “good” or better condition between 2008 to 2030.
% Pavements in 'Good' or Better Condition
Figure 5.2 compares pavement conditions between two different 21-year scenarios: spending $1.2 billion per year for maintenance and rehabilitation (M&R), and spending an average of $325 million per year for routine M&R (2030 Committee 2009). This graph shows that at $325 million a year, the percentage of roads in good or better condition would fall to 50% in just 7 years and 0% in 20 years. In this scenario, more than $1.2 billion annually would need to be spent to keep pavement in good condition. 100 $ 325 Million M&R Annually $ 1.2 Billion M&R Annually
80 60 40 20 0 1
3
5
7
9
Figure 5.2: Change in Percent of Pavements in “Good” or Better Condition for Two Scenarios (Source: 2030 Committee 2009)
11 13 15 17 19 21
Analysis Year
Bridge Maintenance Figure 5.3 shows the distribution of deck area by year built for on-system bridges (for which TxDOT is responsible). It indicates that most Texas bridges were built in the 1960s, 1970s, and 1990s. The life span of a bridge is usually considered 50 years, meaning that a high percentage of bridges constructed in the 1960s and 1970s will need to be replaced over the next 20 years. To replace bridges, TxDOT requires $21.6 billion for the replacement, inspection, and maintenance of regular bridges through 2030 (Figure 5.4. shows cost in detail), and $6.1 billion for replacement of special and large bridges (2030 Committee 2009). Investment and Financing
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16,000,000 14,000,000 Square Feet
12,000,000
Figure 5.3: Distribution of Deck Area by Year Built (Source: 2030 Committee 2009)
10,000,000 8,000,000 6,000,000 4,000,000 2,000,000 1921 1925 1929 1933 1937 1941 1945 1949 1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005
Year Built
$1.10 $0.60
Figure 5.4: Replacement, Maintenance, and Inspection Costs (Billion) for TxDOT Regular Bridges
Replacement Maintenance $19.90
Inspection
To fund the maintenance of current infrastructure and build new infrastructure, a combination of federal grants, state and local taxes, and fees are traditionally used. The next section discusses traditional revenue and expenditures as well as innovative funding sources.
5.3 Financing Revenue Sources Traditionally, highway infrastructure has been built whenever state and federal financing sources are available. In fiscal reporting, TxDOT separates revenue into general, special, debt service, and government funds categories. More specifically, revenue sources include these seven subcategories:
TxDOT revenue issues primarily from seven types of sources.
1. Federal Funding: The U.S. government allocates funding from federal motor fuel taxes, truck tire excise, truck and trailer sales, and heavy vehicle use taxes to different states through the Federal Highway Trust Fund. 2. Bond Proceeds: State and local government entities issue bonds to raise money for transportation projects. A bond is a written promise to pay back borrowed money and interest over the life of a bond. 3. Tolls: Because transportation agencies face constrained budgets, tolls Chapter 5
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Investment and Financing
are used to support transportation investment. Tolls can be considered payment against bonds issued to construct, operate, maintain, and expand facilities, and as a resource to attract private capital to invest in transportation infrastructure. 4. State Motor Fuel Tax: Each state sets a motor fuel tax rate on gasoline, diesel, and other special fuels. In January 2011, the combined local, state, and federal gasoline and diesel tax rates in Texas were 38.4 and 44.4¢ per gallon, respectively (API 2010). Fuel tax revenues depend on the amount of fuel consumed; fuel consumption, in turn, depends on vehicle type. Some increasingly common technologies reduce traditional gasoline use (such as electric vehicles). 5. State Motor Vehicle Tax: All states levy motor vehicle registration fees based on vehicle characteristics.
Resource CTR provides a model (Vcost) to estimate revenue from Texas fuel taxes; see Welter 2009.
6. Other State Funding: Other sources of state transportation revenue include property taxes and motor vehicle operator license fees. 7. Local Funding: Local government revenue from different sources includes local motor fuel taxes, local motor vehicle registration fees, local option sales taxes, value capture, property taxes, and tolls. Figure 5.5 shows this revenue source distribution for Texas in 2008.
Federal Funding
16%
8%
Bond Proceeds 20% Tolls
11% 40%
Figure 5.5: TxDOT Revenue Sources
State Motor Fuel Tax State Motor Vehicle Tax
7% 4%
Other State Fundng Local Funding
Expenditure Sources TxDOT is responsible for using its revenue to provide a safe, reliable, and efficient transportation system for the movement of people and goods throughout the state. To achieve this goal, TxDOT has focused on projects that reduce congestion, enhance safety, expand economic opportunity, improve air quality, and preserve the value of transportation assets. As detailed below, Texas Administrative Code specifies how funds are to be allocated. Similar projects may frequently compete under the same funding category and must be compared in economic terms, to ensure the project that Investment and Financing
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TxDOT expenditures fall into 12 equally important funding categories.
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best meets specific TxDOT responsibilities and goals is selected. TxDOT projects fall under the Statewide Preservation and Safety Program (SPSP), which is divided into 12 different funding categories. Each year, TxDOT allocates funds among these project categories through the Unified Transportation Program (UTP) (TxDOT 2010b): • Category 1 – Preventive Maintenance and Rehabilitation: To perform preventive maintenance and rehabilitation of the existing State Highway System. • Category 2 – Transportation Metropolitan Area (TMA) Corridor Projects: To address the mobility needs in all major metropolitan areas with populations greater than 200,000. • Category 3 – Urban Area (Non-TMA) Corridor Projects: To address the mobility needs in metropolitan areas with populations greater than 50,000 and less than 200,000. • Category 4 – Statewide Connectivity Corridor Projects: To address mobility and added capacity needs of major highway system corridors. • Category 5 – Congestion Mitigation and Air Quality Improvement: To improve the air quality in the non-attainment areas such as Dallas, Fort Worth, Houston, Beaumont, and El Paso. • Category 6 – Structures Replacement and Rehabilitation: To replace and rehabilitate deficient existing bridges and deficient railroad underpasses on the state highway system and construct grade separations at existing highway-railroad crossings.
See the In-Depth Look section at this chapter’s end for a detailed FY2010 funding breakdown.
• Category 7 – Metropolitan Mobility and Rehabilitation: To address transportation needs within the boundaries of urban MPOs with populations of 200,000 or greater. • Category 8 – Safety: To reduce highway fatalities and major injuries, and provide an appealing environment for primary and middle school children to walk and bicycle to schools. Also, to eliminate hazards at highway-railroad crossings. • Category 9 – Transportation Enhancements: To enhance the transportation system in less conventional areas such as historic preservation or tourism programs. • Category 10 – Supplemental Transportation Projects: To address projects that are not qualified for funding in other categories such as state park roads, curb ramp programs, railroad grade crossing replanking programs, and truck weight stations. • Category 11 – District Discretionary: To address projects selected at the district engineer’s discretion. • Category 12 – Strategic Priority: To fund projects with specific importance to the state based on an “as-needed” basis. Chapter 5
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Investment and Financing
Across these categories, TxDOT’s total disbursem*nts were over $4.9 billion in 2008 (Saenz 2008) and distributed as shown in Figure 5.6. Preventative Maintenance & Rehabilitation TMA Corridor Projects 2% 5%
1%
6%
Non-TMA Corridor Projects
8%
28%
7% 19%
12%
7% 2%
2%
Statewide Connectivity Corridor Projects Congestion Mitigation & Air Quality Improvement Structures Replacement & Rehabilitation Metropolitan Mobility & Rehabilitation Safety
Figure 5.6: TxDOT 2008 Disbursem*nts
Transportation Enhancements
Traditional Project Delivery Methods TxDOT has traditionally used, and was once legally limited to, a design-bidbuild (DBB) project delivery method. DBB deliberately separates design and construction by contractors and by sequence (i.e., design must be complete before separate construction firms begin bidding). TxDOT originally favored this deliberate separation because competitive bidding was encouraged and public funds were protected against “graft and favoritism” (Walewski et al. 2001). However, in the 2000s, TxDOT began adopting project delivery methods like design-build (D-B) to expedite project completion and attract private investment for large projects like the IH 635 Managed Lanes project in Dallas and the 183-A Turnpike and SH 130 tolled facilities in Austin. Details on public-private partnerships and their funding advantages and disadvantages are presented later in the following section.
Innovative Financing Beginning in the mid-1990s, the FHWA began developing a series of flexible financing methods to fill the gap between state infrastructure needs and federal funding support measures. A series of new federal loans and credit enhancement programs were provided under the National Highway System Designation Act in 1995, the Transportation Equity Act in 1998, and most recently SAFETEA-LU in 2005. Finance methods developed through these acts allowed for greater public and private investment at lower interest rates through credit enhancement and provided states greater access to capital through loans. Types of financing options developed in this period include Section 129 loans, state infrastructure banks, and public-private partnerships. Many of these innovative methods have now become typical finance procedures, and are discussed in detail here. Investment and Financing
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Flexible financing approaches help DOTs meet their infrastructure needs.
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Section 129 Loans Section 129 loans use federal highway apportionments to fund loans for toll and non-toll projects alike. These loans are extended to public or private entities for projects with a dedicated revenue source (user fees or taxes). Section 129 loans help borrowers (i.e., states) establish credit on the loaned funds, which allows reinvestment and “recycling” in other projects. Up to 80% of the maximum federal share may be lent through Section 129 loans, and the borrower must begin return payments within 5 years of project completion, with all borrowed funds repaid within 30 years of loan authorization (USDOT 2010a). This allotted time in repayment allows flexibility in cases of start-up delays and extended project lengths. Regardless of these benefits, the popularity of Section 129 loans has waned after the introduction of the more recent TIFIA credit program (described later in this section).
The federally funded Section 129 loans have flexible repayment terms.
Texas Example Section 129 Loan Financing in Texas The first project in Texas funded partially by a Section 129 loan was the President George Bush Turnpike (Highway 190) in Dallas. The four-to-eight lane urban toll facility required funding beyond what is typically available at a state level. TxDOT and the Texas Turnpike Authority (TTA) received a $135 million Section 129 loan, which was disbursed in five different payments over a 4-year period (USDOT 2010a). These separated disbursem*nts allowed TxDOT to avoid one single repayment of $135 million and allowed for advanced construction. Use of the Section 129 loan in this manner, alongside toll revenue bonds, reportedly accelerated the project’s completion date by over a decade.
State Infrastructure Banks (SIBs) State infrastructure banks (SIBs) are revolving funds that provide loans and credit assistance for surface transportation projects established and administered by states. SIB assistance attracts non-federal public and private investments to increase the efficiency of states’ transportation investments and leverage federal resources. Highway projects under Title 23 of the U.S. Code, federally aided projects, or Title 49 transit capital projects can use SIB funds. If a community deems a local project vital, SIB funds may be requested to expedite the project’s completion.
For highway projects, stateadministered SIBs offer loans at or below market rates.
SIBs act much like private banks and offer assistance either by loans or credit enhancement, which can accelerate project completion; incentivize state, local, and private investment; and recycle funds for future projects. Historically SIBs were available only to a limited number of states, but have since become an option for all states as part of SAFETEA-LU. Chapter 5
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SIB assistance includes “loans (at or below market rates), loan guarantees, bond insurance, and other forms on non-grant assistance” (USDOT 2010a). Loans through SIBs have a maximum term of 30 years with no more than 5 years allowed between commencement of repayment and project completion. SIBs are considered “revolving” because, although initially capitalized, federal funds are to be repaid directly to the state after they are lent out. These returned funds are then eligible for Title 23 project investment in the state. SIB-funded projects may benefit from flexible project financing and increased private investment, and can aid the state by accelerating certain projects and recouping investments for future works. SIB Financing in Texas In Texas, SIB funds can be used on key projects that may not be immediately eligible for state funding. Timely use of these funds to build a project will spur economic development and increase a local tax base that can be used to pay off the SIB, which is a win-win situation in terms of time and money for the local community and TxDOT’s long-term infrastructure investment and rehabilitation plans. SIB-funded projects must qualify for federal funds and are generally part of the state highway system, but some residential areas or country road bridges may eligible for funding as well. Texas has been using SIB assistance since 1995 and has funded $3.4 billion worth of projects through 88 loans worth $374.6 million (USDOT 2010a). One-fifth of these loans have been applied to international border regions to address trade mobility needs outlined in NAFTA (USDOT 2010a).
Private Activity Bonds (PABs) A section of SAFETEA-LU created private activity bonds (PABs), which are tax-exempt bonds eligible for disbursem*nt on privately developed highway and freight transfer facilities. To be eligible for PAB support, projects must be receiving Title 23 federal assistance or be an international bridge or freight transfer facility initiative. No more than $15 billion can be provided in the form of PABs (USDOT 2005).
PABs are U.S. bonds issued for up to $15 billion.
Texas Example PAB Financing in Texas In Texas, two projects have received PABs: the North Tarrant Express ($400 million) and the IH-635 Managed Lanes ($615 million) (US DOT 2011). These two projects make up a significant proportion of the $2.1 billion total PABs issued in the U.S. as of May 2011.
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The Transportation Infrastructure Finance and Innovation Act (TIFIA) The Transportation Infrastructure Finance and Innovation Act (TIFIA) was proposed to fill market gaps and leverage substantial private co-investment. The TIFIA provides supplemental and subordinate capital amounting to 33% of eligible project costs in the form of secured (direct) loans, loan guarantees, or standby lines of credit (AASHTO 2010c). Highway and transit projects already eligible for federal funding and with a revenue source such as tolls or local sales taxes are eligible for TIFIA credit assistance. As of 2009, projects are given an investment rating, based on the following criteria (Table 5.1), with the associated relative weights of importance. Private Participation
20%
Environmental Impact (Sustainability and Repair)
20%
National or Regional Significance (Livability, Economic Competitiveness , and Safety)
20%
Project Acceleration
12.5%
Creditworthiness
12.5%
Use of New Technologies
5%
Reduced Federal Grant Assistance
5%
Consumption of Budget Authority
5%
Federal assistance from TIFIA is designed to expedite large projects.
Table 5.1: Criteria for TIFIA Project Investment Rating and Relative Weights (Source: adapted from USDOT 2010a)
TIFIA assistance is intended to expedite large projects by providing more flexible repayment terms and reduced interest rates. Thus, restrictions state that TIFIA-supported projects must be no less than $50 million for typical surface transportation projects; Intelligent Transportation Systems (ITS) projects may be eligible at a size of $30 million and above (USDOT 2010a). As of 2010, TIFIA assistance has drawn $29 billion in project investment and provided $7.7 billion for 21 projects (USDOT 2010a).
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Texas Example TIFIA Financing in Texas (USDOT 2011) Project
Project Cost
TIFIA Loan
$3,277.8 million
$900 million
SH 130 (Segments 5–6)
$1.3 billion
$430 million
President George Bush Turnpike Western Extension (SH 161)
$1.1 billion
$418.4 million
183-A Turnpike
$304.7 million
$66 million
IH 635 Managed Lanes
$2,615 million
$850 million
North Tarrant Express
$2,047 million
$650 million
Central Texas Turnpike System (SH 45 North, Loop 1, SH 130)
TIGER Grants and MAP-21 Definition The Transportation Investment Generating Economic Recovery (TIGER) grant program began with the passage of the American Recovery and Reinvestment Act of 2009 (ARRA). Title 12 of the act appropriated funds for supplementary discretionary grants “awarded on a competitive basis for capital investments in surface transportation projects that have a significant impact on the nation, a metropolitan area, or a region.” Eligible applicants included state and local governments, transit agencies, port authorities, MPOs, and multi-state groups. Eligible projects included highways or bridges, public transportation, passenger/freight rail infrastructure, and port infrastructure. With the passing of the latest transportation bill, Moving Ahead for Progress in the 21st Century (MAP-21), large regional projects will be funded under the Projects of National and Regional Significance (PNRS) program (with a year-2013 budget of $500 million), through a competitive process similar to an infrastructure bank or TIGER.
The federally funded TIGER grants are available to many types of jurisdictions and agencies.
Process Evaluation criteria for TIGER grants fell under four categories: job creation and economic stimulus; innovation and partnership; project-specific benefits; and long-term outcomes relating to economic competiveness and improvements to the condition of transport facilities and systems. Applicants for TIGER grants were required to identify, quantify, and compare expected project benefits and costs compared to a base case. Monetized costs include construction, design, right-of-way (ROW) acquisition, operations and maintenance, life-cycle costs, noise, congestion, emissions, and anticipated user costs during construction. Investment and Financing
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The potential project benefits fell under these categories: • Livability: Improved access to jobs, amenities, and for disadvantaged communities, land use changes, transit, pedestrian and bicycle improvements, and transportation and housing affordability. • Economic competitiveness: Changes in operating costs, travel times, user out-of-pocket costs, and reliability. Job creation benefits should only be measured using productivity increases while avoiding multiplier effects and double counting. • Safety: Savings in fatality, injury, and crash costs. • State of good repair: Reductions in long-term maintenance costs and closure time (travel time savings). • Sustainability: Emissions savings valuation for carbon dioxide, sulfur dioxide, nitrogen oxide, and particulate matter. The program required discounting monetized estimates of yearly benefits and costs to represent present value. From 2009 to 2012 the TIGER grant program awarded $2.5 billion to passenger and freight transportation projects. Awards tended to favor public transit projects that encourage environmental sustainability.
Tax Increment Financing (TIF) Definition A special provision in Texas law allows cities to designate areas as Tax Increment Finance (TIF) districts—also called Tax Increment Reinvestment Zones (TIRZs)—to promote development or redevelopment of the area if development is not projected to occur solely through private investment. After it is designated a TIF district, a portion of the tax increase from a district’s investment-related increases in property values can be used to pay off capital bonds for public investments. A state agency has no legal authority to create a TIF; TIFs can be used only by local jurisdictions (such as cities) that can levy and spend the related taxes. Counties, school districts, and other special distracts can participate in the TIF agreement after a city has started to establish a TIF zone. Process Local residents may petition for their neighborhood to be designated a TIRZ, and a city council may initiate the TIF process for an area if it meets at least one of the following criteria: • The area’s present condition substantially impairs the city’s growth, provision of housing, or constitutes an economic or social liability to the public health, safety, morals, or welfare. This condition must exist because of the presence of infrastructure problems, unsafe conditions, taxes or assessments that exceed fair market value of the land, or title problems. Chapter 5
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TIF districts represent creative funding at the level of local jurisdictions.
See the In-Depth Look section at this chapter’s end for details on TIF and TRIZ legislation and valuation.
Resource The Texas Comptroller’s office spells out the TIF process at http://www.window. state.tx.us/taxinfo/ proptax/registry04/ zone.html.
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• The area is predominately open, and because of obsolete platting, deteriorating structures, or other factors, the open area substantially impairs the growth of the city. • The area is in or adjacent to a “federally assisted new community” as defined under Tax Code Section 311.005(b). Texas Example TIF Financing in Texas In 1996, the City of San Antonio established a TIRZ for a 30-acre tract of commercial and residential land. The proposed development included affordable housing for both first-time homebuyers and senior citizens, and also included construction of streets, sidewalks, utilities, drainage, and other improvements related to the new development. In 1996, the tax increment base for the property was $453,300. In 2003, the total appraised value of the property was $6.7 million, resulting in a 2003 captured appraised value of almost $6.2 million. As of 2003, the city was able to capture $205,532 in its tax increment fund for this project (Window on State Government 2004b).
Public-Private Partnerships (PPP) Public-private partnership (PPP) agreements are another way to overcome budgetary constraints and achieve higher standards and operation efficiencies for infrastructure services. PPPs are agreements between a public agency (federal, state, or local) and the private sector. Through public-private agreements, the private sector can contribute to the delivery of the highway infrastructure by participating in the financing, development, and operation of such projects for a specific period of time under a concession agreement. The private investor intends to recover their investment through a guaranteed revenue stream such as fees, tolls, and tax increment financing. These agreements are not always straightforward, and governments have concerns with issues such as transparency and competitiveness of the bids and appropriate allocation of risks. Table 5.2 presents the various PPP structures based on level of responsibility taken by private sector.
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Type of Facilities
Public-Private Partnership Options
Public New Build Facilities
Design-Build (DB)
Existing Facilities
Operation and Maintenance Concession
Private Design-BuildOperate-Maintain (DBOM)
Design-BuildFinance-Operate (DBFO)
Table 5.2: Various PPP Structures (Source: USDOT 2010a)
Long-Term Lease Lease-DevelopOperate (LDO)
Hybrid
New Build Facilities (DB Partnerships) PPP is a project delivery method in which owners may have a single fixed-fee contract with the private sector for its services, such as designing, constructing, maintaining, operating, and/or financing. The private sector representative can be a single firm, joint venture, consortium, or other organization assembled for a particular project. To build new facilities, three different DB partnership options are available with varying levels of private sector responsibilities: Design-Build, Design-Build-Operate-Maintain, and Design-Build-Finance-Operate. This type of project delivery method has both advantages and disadvantages. Advantages of DB partnerships • High level of communication between the design and construction teams often allows projects to be easily fast-tracked and decreases unforeseen problems (Gould & Joyce 2003). • Risk-sharing makes it easier for both parties to build large, costly infrastructure projects (Dutzik & Schneider 2011). • These projects demonstrably accelerate project completion time and provide a more efficient and quicker method of project delivery (FHWA 2010b). • Overlapping design and construction tasks result in both construction and design influencing each other in a continuous and dynamic manner (FHWA 2010b). Disadvantages of DB partnerships • They lack the checks and balances of traditional partnerships—because the designer and the contractor are co-workers in a DB setup, the designer might not oversee the contractor’s work or identify potential deficiencies work (Gould & Joyce 2003). Chapter 5
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One example of DB risk occurred in July 2011, when a Las Vegas contractor abruptly went bankrupt and quit construction of two flyover bridges in Austin, causing months of delay (Austin Business Journal 2011).
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• Risk-sharing—a potential benefit—become less beneficial when the project’s outcome is utterly dependent upon the success (or failure) of a private company (Dutzik & Schneider 2011). • Poorly written contracts may allow private companies excessive power to slow work or alter terms of delivery in their favor; the public agency could lose oversight control of PPP projects that have become “too big to fail.” Responsibilities The private sector’s responsibility is based on the type of the contract. In a Design-Build setup, the private sector is responsible for the majority of the design work and all construction activities, while in the Design-BuildOperate-Maintain setup, the private sector is responsible for designing, building, and providing long-term operation and maintenance services. In the Design-Build-Finance-Operate setup, the private sector has the responsibility for almost all activities in a project and retains the operation revenue risk as well as any surplus operating revenue, while the owner retains only ownership over the contract. Figures 5.7, 5.8, and 5.9 illustrate the contract setup for the three different build options.
Figure 5.7: Design-Build Chart (Source: USDOT 2010a)
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Public Owner/Financer
Engineer
Figure 5.8: Design-BuildOperate-Maintain Chart (Source: USDOT 2010a)
Contractor
Operator
Figure 5.9: Design-BuildFinance-Operate Chart (Source: USDOT 2010a)
U.S. Example New Build PPP To build 14 miles of the Capital Beltway High Occupancy Toll Lanes, the Virginia Department of Transportation made an agreement with a joint venture of Fluor and Transurban, two private transportation firms. The project delivery system was Design-Build-Finance-Operate, with a lump sum contract and a fixed contract time. The total length of the concession is 5 years of construction and 80 years of operation, totaling 85 years (USDOT 2011).
Existing Facilities (Concession or Lease) For existing facilities, public operating agencies can transfer operations and maintenance concession of existing facilities to the private sector. This transfer can be done in two ways: as an Operation and Maintenance Concession, or as a Long-Term Lease. In the Operation and Maintenance Chapter 5
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Concession scenario, the private sector can be paid on either a fixed fee or on an incentive basis, including premiums for meeting specified service levels or performance targets. In the Long-Term Lease option, the private sector has the right to collect tolls on the facility (USDOT 2010a). Advantages The owner can more easily take advantage of life cycle cost and asset management practices, emphasizing cost effective planning and resource allocation for the preservation, upgrade, and timely replacement of highway assets. Also, the public sector can benefit from private sector operational and maintenance efficiencies to reduce ongoing operating and maintenance costs. Disadvantages Long-term operating contracts can lead to inefficiencies due to lack of competition. The private entity with the exclusive rights to the infrastructure facility is essentially operating a monopoly. Responsibilities In the Operation and Maintenance Concession scenario, the owner retains ownership and overall management of the public facility. The contractors are responsible for ongoing activities such as snow removal, mowing, maintenance, and major repairs. In the Long-Term Lease option, the owner retains only project ownership and the contractors may be asked to make improvements to the facility. Figures 5.10 and 5.11 illustrate these contracts.
Figure 5.10: Operation and Maintenance Concession Chart (Source: USDOT 2010a)
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Figure 5.11: Long-Term Lease Chart (Source: USDOT 2010a)
U.S. Example Long-Term Lease PPP In October 2004, Cintra/Macquarie bid $1.83 billion to operate and maintain the Chicago Skyway for a period of 99 years. The private company acquired the right to collect all tolls and concession revenue. This project was the first long-term lease of an existing facility in the U.S. This agreement funded a $500 million long-term and $375 million medium-term reserve for the city of Chicago and gave the city a $1.83 billion cash infusion (Skyway Concession Company, LLC 2005). Hybrid Partnerships for Existing Facilities In this type of agreement, a private firm leases an existing public facility and invests its own capital to improve and expand the facility under a revenuesharing contract for a fixed term. The private sector can recover its investment plus a reasonable return from the facility’s revenue. Legal ownership is still maintained by the public in Lease-Develop-Operate agreements, but in this arrangement private investment can bring alternative solutions to projects that are losing money for the public agency. This leasing flexibility allows private firms to share profits of a project without having to fully purchase the project, which may be outside of their purchasing ability (Finnerty 2007). This arrangement is charted in Figure 5.12.
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Figure 5.12: Lease-DevelopOperate Chart (Source: USDOT 2010a)
U.S. Example Lease-Develop-Operate PPP The private company Transurban has been given the rights to enhance, manage, operate, maintain, and collect tolls on the Pocahontas Parkway/Richmond Airport Connector for 99 years. Also, Transurban was made responsible for Pocahontas Parkway Association’s underlying debt and was obliged to construct a 1.58-mile extension of the toll road. The financing techniques described in this section are meant to fill the gap between traditional government funding sources and transportation needs in order to improve the nation’s transportation infrastructure systems. Many government organizations across the United States have used these tools. Texas Example Comprehensive PPP in Texas In March 2007, TxDOT signed a Comprehensive Development Agreement with the SH 130 Concession Company for designing, constructing, financing, operating, and maintaining a 40-mile extension of SH 130 (Segments 5–6) under a 50-year concession from the date of opening. The delivery method for this project was Design-BuildFinance-Operate-Maintain. Total cost was estimated at $1.3 billion, to be funded by a combination of financing methods such as TIFIA loans ($430 million), private equity ($210 million), senior bank loans ($686 million), and interest income ($2.3 million) (USDOT 2010a).
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More Alternative Finance Methods in Texas In addition to widespread methods of innovative finance, other methods of project finance are possible in Texas. The following sections briefly introduce additional methods unique to the state. Proposition 12 (General Obligation) Bonds In 2007, Texas voters approved a nearly $2 billion distribution of generalrevenue-backed bonds for highway improvements across the state as part of Proposition Program 1 (TxDOT 2011a). As of October 2011, Proposition 12 Program 1 funding has been allotted for 73 projects focused on corridor improvement, roadway rehabilitation, safety enhancement, and congestion reduction (TxDOT 2011a). Proposition 12 was extended to a second program in summer 2011 to include an additional $3 billion in bond authorization (TxDOT 2011b). Program 2 of Proposition 12 will distribute bonds to the program areas presented in Figure 5.13. 25 Metropolitan Planning Organizations, $600
Statewide Connectivity, $200
Bridges, $500 TxDOT's 25 Districts, $1,400
Figure 5.13: Distribution of Proposition 12 Program 2 Funding (Source: TxDOT 2011b)
Metro Regions, $300
Both programs of Proposition 12 support transportation projects with general obligation bonds backed with general revenue funds rather than fuel tax revenues. Proposition 14 Projects In a similar fashion to Proposition 12, 2008’s Proposition 14 allows the distribution of bonds for statewide transportation projects. However, Proposition 14 bonds are backed by the state highway fund rather than the general fund and are to be used for different purposes, such as the following (TxDOT 2011c): • Projects facing funding-related delays • Priority projects such as completion of multiple-phased projects or construction of infrastructure with statewide significance (hurricane evacuation routes, for example) • Projects to address neglected congestion problems • Projects improving safety in areas with high crash rates Chapter 5
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As of October 2011, Proposition 14 funding has allocated over $3 billion in funds to 223 projects across the state (TxDOT 2011c). Pass-Through Financing Program Pass-through financing allows local communities to fund the initial costs of state highway projects in their proximity. Local agencies are reimbursed over time by the state on the basis of a fee per vehicle use. As of May 2011, the Texas Transportation Commission approved pass-through finance measures for 14 construction projections, with total local up-front funding of $174 million (TxDOT 2011d).
5.4 Summary This chapter examined the impact of transportation infrastructure on society by looking at economic outputs, the condition of Texas rail, roads, and bridges, and the gap between available and needed funds. Innovative methods such as Section 129 loans, SIBs, and TIFIA assistance can provide alternative financing options when conventional funding sources are inadequate. To encourage the private sector to participate in designing, building, operating, and maintaining transportation infrastructure, various types of public-private partnership agreements are included as part of modern transportation policy.
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5.5 An In-Depth Look FY2010 Funding Breakdown Table 5.3 shows starting point, usual funding, and amount of available funding for the fiscal year 2010 for 12 different categories. Funding Category Preventive Maintenance and Rehabilitation Metropolitan Area Corridor Projects Urban Area Corridor Projects Statewide Connectivity Corridor Projects Congestion Mitigation and Air Quality Improvement
Starting Point
TxDOT District TxDOT District
• • • • • • •
TxDOT District
• Federal 80% State 20% • State 100%
TxDOT District
TxDOT District
Bridges
TxDOT District
Metropolitan Mobility/ Rehabilitation
TxDOT District
Safety
TxDOT District
Transportation Enhancement Supplemental Transportation Projects
TxDOT District TxDOT District, Texas Parks and Wildlife Department, Other (federal allocation)
District Discretionary
TxDOT District
Strategic Priority
TxDOT District
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Usual Funding Options (1 option selection per funding category) Federal 90% State 10% Federal 80% State 20% State 100% Federal 80% State 20% State 100% Federal 80% State 20% State 100%
• • • • • • • • • • • • • • • •
Federal 80% State 20% Federal 80% Local 20% Federal 90% State 10% Federal 90% State 10% Federal 80% State 20% Federal 80% State 10% Local 10% Federal 80% State 20% Federal 80% Local 20% State 100% Federal 90% State 10% Federal 90% Local 10% Federal 100% State 100% Federal 80% State 20% Federal 80% Local 20%
• State 100% • Federal 80% State 20% • Federal 100% • • • • •
Federal 80% State 20% Federal 80% Local 20% State 100% Federal 80% State 20% State 100%
Amount of Available Funding for 2010 $393,792,484 $628,620 $10,468,990 $50,691,000 $148,598,114
$313,110,000
$209,000,000
Table 5.3: Different Categories for Funding Projects (Source: TxDOT 2010b)
$144,275,000
$56,082,610
$187,288,182
$73,065,000 $13,000,000 $1,600,000,000
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TIF Legislation and Valuation As detailed earlier, transportation infrastructure demands are outpacing the revenue provided by traditional financing methods. Legislators identified the need for financing reform in order to support healthy infrastructure and began allowing innovative methods of finance, including a recent update to TIF regulations.
Recent Changes in Texas Laws A 2011 law amends the Texas Transportation Code to change the criteria under which municipalities and counties are authorized to create Transportation Reinvestment Zones (TRZs). The Texas House of Representatives Research Organization assessed the changes (while in bill form) and noted that a TRZ could now be used for any transportation project, including highway improvement, passenger or freight rail facility, ferry, airport, pedestrian, bicycle facility, intermodal hub, or transit system. This development significantly changes previous law, which stipulated that only pass-through tolls can be used for the design, development, financing, construction, maintenance, or operation of a toll or non-toll facility on the state highway system (by either the public or a private entity). This multimodal complement to existing coverage will make conforming changes to state law for an expanded range of transportation projects now eligible for TRZs. It also applies to previously designated zones, so these could now choose to undertake multi-modal projects.
TIF legislation made financing more accessible to a variety of infrastructure projects, but can require TxDOT to release authority over a project.
The new law also adds that if all or part of a transportation project in the zone is subject to TxDOT oversight, TxDOT is required (at the option of the governing body of the municipality or county) to delegate full responsibility for the project to the county or municipality. If the project is on the state highway system or located in state highway ROW, it must comply with applicable federal and state requirements and any criteria for project development, design and construction—although TxDOT can grant an exception. TxDOT is not allowed to reduce any allocation of the transitional funding to a district that contains a municipality or county with a TRZ. Any funds that TxDOT may have designated prior to the establishment of the TRZ are not reduced because of the TRZ designation.
TIF Valuation Figure 5.14 illustrates the assessed value of a TIF-funded project over a 25-year period.
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Figure 5.14: TIF Assessed Value over Project Life (Source: Eversberg & Goebel 2005)
TRENDS Transportation Revenue Estimator and Needs Determination System (TRENDS) is a model partly developed by TxDOT and now maintained by the Texas Transportation Institute (available at http://trends-tti.tamu.edu/). This model was developed for transportation decision-makers, and provides estimates of TxDOT revenues and expenditures for years 2010 through 2035. It is updated monthly with various factors that influence state transportation revenues. TRENDS has a web-based interface that allows users to modify the following factors affecting future state revenues: • State investments in transport capacity • State gasoline and diesel fuel taxes (over time) • Share of fuel taxes that Texas receives back from federal fuel taxes • Options to index state fuel taxes to inflation and/or fuel economies • Share of state fuel tax increases dedicated to transportation (default is 74–75%) • Vehicle registration fees • Addition of a VMT fee • Addition of parking fees • Population and vehicle-fleet growth rates • Immigration rates • Local revenue options (for specific TxDOT districts)
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Users can also adjust revenue shares dedicated to the following state funding categories: • Category 2: Transportation Metropolitan Area (TMA) Corridor Projects • Category 3: Urban Area (Non-TMA) Corridor Projects • Category 5: Congestion Mitigation and Air Quality (CMAQ) Improvement • Category 7: Metropolitan Mobility and Rehabilitation • Category 11: District Discretionary TRENDS provides valuable insights into future transportation budgets. For example, simply indexing taxes to match inflation rates provides a sizable boost to year 2030+ budgets. However, some of TREND’s limitations become apparent when noting that some factor inputs are subjective in nature (e.g., low, medium, and high are the options given on fuel economy) and demand-elastic consumer behaviors (such as higher fees lowering VMT) are not reflected in the equations used to generate forecasts. Nevertheless, tools like TRENDS help practitioners and policymakers anticipate how small adjustments in taxes and other policies may impact long-term funding levels. TRENDS’ outputs also highlight the dim future prospects of state transportation budgets, with decreasing revenues for the next few decades, despite rising inflation.
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5.6 References 2030 Committee (2009) 2030 Committee Texas Transportation Needs Report. Available online at http://www.txdot.gov/txdot_library/publications/2030_committee.htm. Akintoye, A., Beck, M., & Hardcastle, C. (2003) Public Private Partnerships Managing Risks and Opportunities. Oxford, UK. Blackwell Science Ltd. American Petroleum Institute (API) (2010) Fuel Motor Taxes. Available online at http://www.api.org/statistics/fueltaxes/index.cfm. American Society of Civil Engineers (ASCE) (2009) Report Card for America’s Infrastructure. Available online at http://www.infrastructurereportcard.org/. Austin Business Journal (2011) Mopac flyover work stopped after contractor files bankruptcy. Available online at http://www.bizjournals.com/austin/news/2011/07/18/mopac-flyoverconstruction-stopped.html. Burchell, R. W. (1978) Fiscal Impact Handbook. The Center for Urban Policy Research, New Brunswick, NJ. Cambridge Systematics, Inc. (2007) National Rail Freight Infrastructure Capacity & Investment Study. Available online at: http://www.camsys.com/pubs/AAR_Nat_%20Rail_Cap_Study.pdf. Dutzik, T. & Schneider, J. (2011) High Speed Rail: Public, Private or Both? The Frontier Group. US PIRG Education Fund. Available online at http://cdn.publicinterestnetwork.org/assets/85a40b6572e20834e07b0da3e66e98bf/HSRPPP-USPIRG-July-19-2011.pdf. Eversberg, R. R. & Goebel, P. R. (2005) ABCs of TIF. Texas A&M University Real Estate Center, College Station, TX. Available at http://recenter.tamu.edu/pdf/1736.pdf. Finnerty, J. D. (2007) Project Financing: Asset-Based Financial Engineering. John Wiley & Sons, Inc., Hoboken, NJ. Gould, F. E. & Joyce, N. E. (2003) Construction Project Management. Upper Saddle River, NJ: Pearson Education, Inc. Government Accountability Office (GAO) (2006) Freight Railroads: Industry Health Has Improved, but Concerns about Competition & Capacity Should Be Addressed. Available online at http://www.gao.gov/new.items/d0794.pdf. Malhorta, A. K. (1997) Private Participation in Infrastructure: Lessons from Asian’s Power Sector. Finance and Development, December, 33–35. National Council for Public-Private Partnerships (2010) How PPPs Work. Available online at http://www.ncppp.org/howpart/index.shtml#define. Passenger Rail Working Group (PRWG) (2007) Vision for the Future: U.S. Intercity Passenger Rail Networking through 2050. Available online at http://www.dot.state.wi.us/projects/state/docs/prwg-report.pdf. Persad, K., Walton, C. M., & Franco, P. (2008) Financing Tools and Partnerships for Rural and Semi-Rural Transportation Projects. Center for Transportation Research, University of Chapter 5
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Texas at Austin. Available online at http://www.utexas.edu/research/ctr/pdf_reports/0_6034_P1.pdf. Saenz, A. (2008) Transportation Program Expenditures – Fiscal Year 2008. Texas Department of Transportation. Available online at http://ftp.dot.state.tx.us/pub/txdotinfo/library/reports/expenditures/fy2008.pdf. Sinha, K. C. & Labi, S. (2007) Transportation Decision Making—Principles of Project Evaluation and Programming. Hoboken, NJ: John Wiley & Sons, Inc. Skyway Concession Company, LLC (2005) Chicago Skyway. Available online at http://www.chicagoskyway.org/. Texas Department of Transportation (TxDOT) (2001) Transportation Planning Manual. Available online at http://onlinemanuals.txdot.gov/txdotmanuals/pln/pln.pdf. Texas Department of Transportation (TxDOT) (2010b) 2010 Unified Transportation Program (UTP). Available online at ftp://ftp.dot.state.tx.us/pub/txdotinfo/tpp/2010_final_utp_0503.pdf. Texas Department of Transportation (TxDOT) (2011a) Proposition 12 – Program 1. Available online at http://www.txdot.gov/project_information/prop12_program2.htm. Texas Department of Transportation (TxDOT) (2011b) Proposition 12 – Program 2. Available online at http://www.txdot.gov/project_information/prop12_program1.htm. Texas Department of Transportation (TxDOT) (2011c) Proposition 14 Projects. Available online at http://apps.dot.state.tx.us/apps/project_tracker/prop14projects.htm. Texas Department of Transportation (TxDOT) (2011d) Pass-Through Financing Program. Available online at http://www.txdot.gov/business/governments/pass_finance.htm. Texas Department of Transportation (TxDOT) (2011e) TxDOT Tracker: Semi-Annual Results – FY 2011. Available online at http://www.txdot.gov/txdot_tracker/. Texas Legislative Budget Board Fiscal Note (2011, May 12) Available online at http://www.legis.state.tx.us/tlodocs/82R/fiscalnotes/html/HB00563F.htm. Texas Transportation Code Chapter 222 (2011). Available online at http://www.statutes.legis.state.tx.us/Docs/TN/htm/TN.222.htm. Texas Transportation Institute (TTI) (2007) The 2007 Urban Mobility Report. Available online at http://www.commutercars.com/downloads/UrbanMobility07.pdf. TRIP, A National Transportation Research Group (2010a) Key Facts about America’s Surface Transportation System and Federal Funding. Available online at http://www.tripnet.org/Fact_Sheet_National.pdf. TRIP, A National Transportation Research Group (2010b) Key Facts about Texas’s Surface Transportation System and Federal Funding. Available online at http://www.tripnet.org/Fact_Sheet_TX.pdf. US Department of Transportation (USDOT), AASHTO Center for Excellence in Project Finance (2010a) State Infrastructure Banks. Available at: http://www.transportationfinance.org/funding_financing/financing/credit_assistance/state_infrastructure_banks.asp x. Investment and Financing
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US Department of Transportation (USDOT), AASHTO Center for Excellence in Project Finance (2010b) TIFIA. Available at: http://www.fhwa.dot.gov/ipd/tifia/index.htm. US Department of Transportation (USDOT), AASHTO Center for Excellence in Project Finance (2010c) Project Finance State by State. Available online at http://www.transportationfinance.org/tools/state_by_state/revenue_sources.aspx?state=tx. US Department of Transportation (USDOT), Federal Highway Administration (2002) Innovative Finance Primer. Available online at http://www.fhwa.dot.gov/innovativefinance/ifp/index.htm. US Department of Transportation (USDOT), Federal Highway Administration (2005) A Summary of Highway Provisions in SAFETEA-LU. Available at: http://www.fhwa.dot.gov/safetealu/summary.htm. US Department of Transportation (USDOT), Federal Highway Administration (2008b) Highway Statistics 2008. Available at: http://www.fhwa.dot.gov/policyinformation/statistics/2008/. US Department of Transportation (USDOT), Federal Highway Administration (2010a) Innovative Finance Primer. Available online at http://www.fhwa.dot.gov/innovativefinance/ifp/index.htm. US Department of Transportation (USDOT), Federal Highway Administration (2010b) U.S. Department of Transportation, Public-Private Partnerships. Available at: http://www.fhwa.dot.gov/ipd/p3/index.htm. US Department of Transportation (USDOT), Federal Highway Administration (2010c) Case Studies. Available at: http://www.fhwa.dot.gov/ipd/case_studies/tx_sh130.htm. US Department of Transportation (USDOT), Federal Highway Administration (2011) Innovative Program Delivery. Available online at http://www.fhwa.dot.gov/ipd/tifia/projects_project_profiles/index.htm. US Department of Transportation (USDOT), Federal Highway Administration (2011b) Federal Debt Financing Tools: Private Activity Bonds (PABs). Available online at http://www.fhwa.dot.gov/ipd/finance/tools_programs/federal_debt_financing/private_acti vity_bonds/index.htm. US Department of Transportation (USDOT), Federal Railroad Administration (2011a) Financial Assistance: Passenger Rail. Available at: http://www.fra.dot.gov/rpd/passenger/34.shtml. US Department of Transportation (USDOT), Federal Railroad Administration (2011b) Railroad Rehabilitation & Improvement Financing (RRIF) Program. Available at: http://www.fra.dot.gov/rpd/freight/1770.shtml. Walewski, J., Gibson Jr., G. E., & Jasper, J. (2001) Project Delivery Methods and Contracting Approaches Available for Implementation by the Texas Department of Transportation. Center for Transportation Research, the University of Texas at Austin. Prepared for Texas Department of Transportation. Weisbrod, G. & Weisbrod, B. (1997) Assessing the Economic Impact of Transportation Projects: How to Choose the Appropriate Technique for Your Project. Transportation Research Circular, 447. Washington, DC: Transportation Research Board. Chapter 5
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Welter, D., Ates, M., Loftus-Otway, L., Matthews, R., & Harrison, R. (2009) Estimate Texas Motor Vehicle Operating Cost. Center for Transportation Research, Austin, TX. Available at http://www.utexas.edu/research/ctr/pdf_reports/0_5974_1.pdf. Window on State Government (2004) Title 3. Local Taxation, Subtitle B. Special Property Tax Provisions, Chapter 311. Tax Increment Financing Act. Available at: http://www.window.state.tx.us/taxinfo/proptax/tc04/ch311.htm. Window on State Government (2004a) Tax Increment Financing Registry. Available at http://www.window.state.tx.us/taxinfo/proptax/registry04/zone.html. Window on State Government (2004b) Tax Increment Financing Registry – Bexar County. Available at http://www.window.state.tx.us/taxinfo/proptax/registry04/bexarco.pdf.
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Chapter 6. Project Evaluation 6.1 Introduction ........................................................................................................................ 6-1 6.2 Engineering Economic Analysis ........................................................................................ 6-1 Discount Rate and Time Value of Money ........................................................................... 6-1 Net Present Value (NPV) ..................................................................................................... 6-2 Single Payment ................................................................................................................. 6-2 Equal Payment Series ....................................................................................................... 6-4 Linear Gradient Series ...................................................................................................... 6-4 Geometric Gradient Series ................................................................................................ 6-5 Internal Rate of Return (IRR) .............................................................................................. 6-8 Incremental Rate of Return (ΔROR) ................................................................................... 6-9 Payback Period................................................................................................................... 6-10 Breakeven Analysis ........................................................................................................... 6-11 Cost-Benefit Analysis (CBA) ............................................................................................ 6-13 CBA Process ................................................................................................................... 6-13 Life Cycle Cost Analysis (LCCA) ..................................................................................... 6-17 Constrained Optimization .................................................................................................. 6-18 6.3 Multicriteria Analysis ...................................................................................................... 6-25 Simple Additive Weighting (SAW) ................................................................................... 6-27 Data Envelopment Analysis (DEA) ................................................................................... 6-29 6.4 Sensitivity Analysis ......................................................................................................... 6-29 Single Factor Sensitivity Analysis: A TxDOT Application .............................................. 6-30 Multiple Factor Sensitivity Analysis: A TxDOT Application ........................................... 6-32 Monte Carlo Methods ........................................................................................................ 6-33 6.5 Summary .......................................................................................................................... 6-33 6.6 References ........................................................................................................................ 6-34
6.1 Introduction While private companies or individuals generally evaluate how new projects will impact their own financial accounts, TxDOT must consider how potential projects and policies will impact a much larger community’s well-being—including economic, environmental, equity, and other impacts. In either setting, the decision to pursue a project or policy requires structured processes to ensure choice of the most beneficial alternatives. Fortunately, a variety of valuable tools exist for project evaluation and decision-making, such as discounting of future costs and generating benefitcost ratios. Such accounting can provide a priceless transparency for an agency and its many stakeholders. This chapter describes the relevant tools for TxDOT employees. These concepts and metrics presented are important in quantifying the net benefits of different alternatives, particularly important in budget-constrained situations, which all state DOTs face. Traditional engineering techniques alone generally cannot address a variety of less tangible agency concerns regarding important economic consequences that are difficult to measure in monetary terms alone, such as environmental justice and community preferences. Thus, this chapter presents multicriteria analysis methods to assess these non-economic elements.
Key Terms NPV: net present value IRR: internal rate of return MARR: minimum accepted rate of return ΔROR: incremental rate of return CBA: cost-benefit analysis B/C ratio: benefit/cost ratio LCCA: life cycle cost analysis MCA: multicriteria analysis SAW: simple additive weighting DEA: data envelope analysis DMU: decisionmaking unit
Moreover, investment in transportation infrastructure is risky, as uncertainty surrounds construction costs, future demand, and maintenance costs. Example of sensitivity analysis and breakeven analysis are therefore also included here.
6.2 Engineering Economic Analysis In order to generate the necessary economic data, some traditional engineering economic concepts are key. This section presents an overview of the discount rate, internal rate of return, payback period, and several other essential economic calculations.
Discount Rate and Time Value of Money The purchasing power of money normally decreases over any given period of time due to inflation and uncertainty. A discount rate adjusts the value of money for time, expressing expected future monetary quantities in terms of their worth today. Following are the two different kinds of interest rates: 1. Real interest: rate exclusive of inflation 2. Nominal interest: rate inclusive of inflation TxDOT may use either kind of interest rate, depending on the type of decision to be made. For most of the Department (all but Finance Division), nominal Chapter 6
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interest will be used, as planning requires using future revenues and costs.
Net Present Value (NPV) A net present value (NPV) calculation is used to state a project’s worth or cost for its entire life cycle in today’s dollars or at some specific point in time. NPV is calculated as follows: =−
1 ( 1+
+
)
−
1 ( 1+
)
+
−
1 ( 1+
)
where is the initial project cost, is the salvage value, IPC are interim project costs (generally involving highway capacity additions and upgrades), is the discount rate (as a proportion, rather than as a percentage, e.g., 0.05 instead of 5%), denotes the monetized benefits realized in year y, and denotes costs realized in year y. Currently benefit assessment methods at TxDOT do not monetize benefits such as crash and air emissions reductions. Therefore, most transportation projects do not have direct monetized benefits (e.g., toll revenue) and the NPV covers only expected costs at a specific point in time. To calculate the present value of money, the following formulas can be used, where F is future value, P is present value, i is the discount rate per period, and N is the number of compounding periods.
Single Payment
Note these variable definitions for the equations in this section.
To calculate the present value of a single payment in the future:
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,
( | , , ):
=
6-2
× (1 + )
Chapter 6
Example of Simple Compound Interest If you have $100 today and invest it at 10% simple annual compound interest rate per year for 2 years, you will have the following: Interest earned in year 1: $100 x %10 = $10 Interest earned in Year 2: $110 x %10 = $21 Total interest earned in 2 Years = $31 Present value = $100 Future value at the end of Year 2 = $131
Example of Interest Calculation Suppose a planned project is suddenly delayed for 2 years. Construction, labor, and materials costs are expected to increase 2% annually during the delay, but the unused funds could meanwhile accrue 1.75% interest in other investments. Current construction cost: $10,000,000 Present value of funds from interest: 10,000,000= ×(1+0.0175)$10,000,000 = => Fi = $10,353,063
× (1 + 0.0175)
Therefore, in 2 years, the money not spent on construction could earn $353,063 from interest. Present value of inflation costs: 10,000,000= ×(1+0.02)$10,000,000 = $10,404,000
× (1 + 0.02)
=> Ff =
Rising materials and labor costs would increase construction costs $404,000 in two years. Considering both interest and inflation, a 2-year delay would cost overall $50,937.
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Equal Payment Series To calculate the present value of constant cash flow (one payment “A” per period):
,
( | , , ): = =
(
[1 − (1 + )
]
= ∞)
Example of Equal Payment Series =
1,000,000 [1 − 1.05 ] = $33,065,954 0.05
Linear Gradient Series To calculate the present value of a cash flow series with a gradient (G) component that either increases or decreases by a constant rate over N time periods:
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,
( | , , ): =
6-4
(1 + ) − −1 (1 + )
Chapter 6
Example of Linear Gradient Series TxDOT considers benefits of a project as shown in this table. Toll revenue is assumed to increase by a rate of $80,000 each year. What is the NPV of the project? Assume the discount rate is 10%. Year 1 2 3 4 5
End-of-Year Payment $1,000,000 $1,080,000 $1,160,000 $1,240,000 $1,320,000
The total project cash flow consists of two cash flows: 1. Annual Cash Flow A = $1,000,000/period 2. Linear Gradient Cash Flow G = $80,000/period So, the NPV of this series of payments at a 10% discount rate is =
(1 + 0.1) − 0.1 × 5 − 1 1,000,000 [1 − 1.1 ] + 80 0.10 0.1 × (1 + 0.1) = $4,339,731
Geometric Gradient Series To calculate the present value of a cash flow that changes by a fixed percentage (g) each time period:
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,
: ( | , , , ):
1 − (1 + ) (1 + ) −
=
( ≠ )
=
1+
( = )
Example of Geometric Gradient Series #1 The first-year maintenance cost of a dump truck is estimated to be $1,000, and is expected to increase at a uniform rate of 2% per year. Using a 10% discount rate, calculate the NPV of the cost of the first 20 years of maintenance. 1 − (1 + ) (1 + ) −
= 100.00
1 − (1 + 0.10) (1 + 0.08) 0.10 − 0.02
= $9739
=
Thus, the present worth of the cost of maintenance for the first 20 years is $9,739.
Example of Geometric Gradient Series #2 Planners have determined that a continuous flow intersection (CFI) alignment at a busy intersection will significantly reduce delay. Engineers report the following details for the intersection: Average no-build intersection delay: 425.0 Average CFI delay: 40.0 Current ADT: 36,000 Traffic growth rate: 2.5% (exponential) Assuming for the users a value of travel time (VOTT) of $25 per hour, and an interest rate of 2%, estimate the total cost savings benefit of the continuous flow intersection over 10 years using the following formula and a geometric gradient series. 1 ℎ = × × × × 3600
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Annual no-build delay costs: $25 × 425.0 × 36,000 × 365 × Annual CFI delay costs: $25 × 40.0 × 36,000 × 365 ×
1 = $38,781,250 3600 1 = $3,650,000 3600
Annual delay cost savings: $38,781,250 − $3,650,000 = $
,
,
Because traffic is increasing exponentially at 2.5% every year, a geometric gradient series can be used to determine present value delay cost savings over a 10-year interval. = $35,131,250
1 − (1 + 0.025) (1 + 0.02) 0.02 − 0.025
=$
,
,
So, $352.122 million in user delays will be eliminated over 10 years by constructing the CFI.
Microsoft Excel’s NPV function calculates NPV with the input NPV(rate, value1, value 2,…), where rate is discount factor, value1 is the cash flow input for the end of the first period, value2 is the cash flow input for the end of the second period, and so on. The returned NPV refers to the value at the end of the initial year. See Figure 6.1.
Use of Excel’s NPV function
Figure 6.1: Excel’s NPV Function Screen Shot
In some cases, NPV is not the best criterion for selecting between alternatives. Chapter 6
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Example of Non-NPV-Based Selection Suppose TxDOT considers building a new highway with alternatives A and B with respective NPVs of $2,000,000 and $3,500,000. The required investment for alternatives A and B are $10,000,000, and $30,000,000, respectively. Although alternative B’s NPV is greater than that of alternative A, alternative B requires significantly more investment. To have a better understanding of alternatives evaluation, internal rate of return and payback period can also be used.
Internal Rate of Return (IRR) As described in Kockelman et al. (2010), “the project’s Internal Rate of Return (IRR) determines the discount rate at which the sum of discounted costs equals the sum of discounted benefits (at their presentyear worth):”
1 ( 1+
+
1 ( 1+
) −
1 ( 1+
=
)
1 ( 1+
+
)
)
Essentially, IRR is the effective (equivalent) interest rate used to measure the value of an investment. IRR can be used only when the project will generate income. To evaluate alternatives using IRR, the alternatives’ IRRs should be greater than the minimum accepted rate of return (MARR), which is also known as the hurdle rate. MARR is the lowest interest rate that investors would accept, given the risk of the investment and the opportunity cost of foregoing other projects. Example of IRR TxDOT considers building a new toll or managed lane highway with the following cash flow for first 5 years. What is the IRR for this period? Year 0 1 2
Cash Flow -$10,000,000 $1,340,000 $2,010,000
10,000,000 =
, (
, )
+
Year 3 4 5 ,
Cash Flow $2,345,000 $2,680,000 $2,847,500
,
(
)
+ …+
, (
, )
IRR = 3.6%
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Chapter 6
To calculate IRR in Microsoft Excel, the function IRR(value) can be used, where value is a reference to the range of cells for which the user would like to calculate the IRR (Figure 6.2).
Use of Excel’s IRR function
Figure 6.2: Excel’s IRR Function Screen Shot
Incremental Rate of Return (ΔROR) As mentioned, the NPV of one alternative can be greater than its competing alternative but require greater investment. In this situation, incremental rate of return (ΔROR) can be used. ΔROR is the interest rate earned on the extra cost of a higher cost alternative. If the alternative’s ΔROR is greater than the MARR, the alternative would be beneficial. Example of ΔROR TxDOT considers two alternatives for a project, A and B. The table shows the required investment and returned benefit of each alternative. If TxDOT’s MARR is 8%, which alternative should be built? Year 0 1 2 3 4 5 IRR
Alternative A -$2,500,000 $746,000 $746,000 $746,000 $746,000 $746,000 15.01%
Alternative B -$6,000,000 $1,664,000 $1,664,000 $1,664,000 $1,664,000 $1,664,000 11.99%
Clearly, the IRR for both alternatives is greater than the MARR. So, both projects are acceptable investments for TxDOT. Now the question is whether alternative B is worth the extra $3,500,000 in initial investment.
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Year 0 1 2 3 4 5 IRR
Alternative A - $2,500,000 $746,000 $746,000 $746,000 $746,000 $746,000 15.01%
Alternative B -$6,000,000 $1,664,000 $1,664,000 $1,664,000 $1,664,000 $1,664,000 11.99%
Δ(A,B) -$3,500,000 $918,000 $918,000 $918,000 $918,000 $918,000 9.78%
In effect, the first $2,500,000 investment in alternative B yields 15.01% IRR and the next $3,500,000 investment in alternative B yields 9.78% IRR. As the incremental rate of return is still higher than TxDOT’s MARR, alternative B should be selected.
Payback Period Payback period is the period of time required before the project’s benefits are equal to the project’s cost. In this method, the effect of interest and economic consequences after payback are ignored. Example of Payback Period Year 0 1 2 3 4 5
Alternative A -$1000 $200 $500 $800 $1100 $1400
Alternative B -$1000 $300 $300 $300 $300 $300
Payback periods for the alternatives shown in the table at a 10% discount rate are as follows: Alternative A 1000 = 200 + 500 + (800 ∗ 0.375) The payback period is equal to 2.375 years. Alternative B Uniform annual benefits
1000 = 3.33 300
The payback period is equal to 3.33 years.
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Chapter 6
To calculate payback period in Microsoft Excel, the NPER function can be used if the payment made each period is constant (Figure 6.3). The function uses argument NPER (rate, pmt, pv, [fv], [type]), where rate is discount factor, pmt is the payment made each period, pv is the present value, fv is the future value—or a cash balance you want to attain after the last payment is made (optional argument)—and Type indicates when payments are due (optional argument). If payments are due at the beginning of the period, a user should enter ‘1.’ Otherwise, the user should not enter anything or enter ‘0.’ The returned value is the payback period.
Use of Excel’s NPER function
Figure 6.3: Excel’s NPER Function Screen Shot
Breakeven Analysis When a future condition is uncertain, breakeven analysis can be employed. The computed breakeven point is the point at which expenses and revenues are equal, with no net loss or gain. For example, breakeven analysis can be used to determine if a highway should be constructed to meet its full future demand or be constructed in multiple stages as additional demand arises. In order to conduct breakeven analysis, a breakeven point should be calculated by setting two alternatives equal to each other using NPV analysis. Then, the present worth of each alternative at each period is plotted, and the point at which the two alternatives’ present worth intersect is the breakeven point (n). Finally, the plotted graphs can be used to determine the best strategy to address the uncertainty.
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Project Evaluation
Example of Breakeven Analysis Assume that TxDOT has two alternatives for a new highway project. Alternative 1 addresses all future demand until year 20 and costs $140 million. Alternative 2 will be built in two stages: the first stage builds initial capacity and costs $100 million and the second, if required, will require an additional $120 million in year N to upgrade to full capacity. Determine which is the best alternative, assuming operations and maintenance costs for both alternatives are equal and interest rate is 8%. The table shows the present worth of each alternative’s cost if the second stage of alternative 2 is built in year N. Year (N) 0 4 8 12 16 20
Alternative 1 $140,000,000 $140,000,000 $140,000,000 $140,000,000 $140,000,000 $140,000,000
Alternative 2 $220,000,000 $188,000,000 $165,000,000 $148,000,000 $135,000,000 $126,000,000
The breakeven point of this example is at 14.3 years. This result means that if the second stage is deferred for 14.3 years, the present worth of the two alternatives would be equal.
= 1,400,000 = 1,000,000 + 1,200,000( | , 8%, ) ( | , 8%, ) = 0.33 = 14.3 As the figure indicates, if the second stage of alternative 2 is needed before year 14.3, alternative 1 is preferred, since it offers a lower present worth than alternative 2. However, if the second stage is not required until after year 14.3, alternative 2 is preferred.
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Chapter 6
Cost-Benefit Analysis (CBA) Cost-Benefit Analysis (CBA) is a method to measure and evaluate all relative direct economic impacts of public investment projects. A useful tool for decision-making in planning and evaluation of projects, CBA can be used to determine whether and when a project should be undertaken and to rank and prioritize projects.
CBA Process Following is a description of the CBA process. 1. Identify project needs. Clearly state project needs so that key relationships are identified and a wide range of alternatives can be examined. The project’s objective should not be too broad, making it difficult to examine all of the trade-offs, or too narrow, excluding key relationships. 2. Identify project constraints. Constraints include policy and legal initiatives, and require specific assumptions about the future, such as expected regional traffic growth. 3. Define the base case. This is also known as the “no action” case—the continued operation of the current facility without any major investments 4. Identify alternatives. Identify project alternatives, which can vary from major rehabilitation of existing facilities to new construction, full reconstruction, or replacement. 5. Define a time period. Set the analysis period over which the life cycle costs and benefits of all of the alternatives will be measured. It should be long enough to include at least one major rehabilitation project. 6. Define work scope. Define the level of effort for screening alternatives. A complete analysis of all options is neither achievable nor necessary. Screening alternatives allows a wide range of initial options to be considered with only a reasonable level of effort. The level of effort is proportional to the expense, complexity, and controversy of the project. 7. Analyze alternative traffic effects. Analyze traffic effects that the alternative would have on the future traffic to calculate the project costs and benefits. 8. Estimate benefits and costs. These estimates include investment costs, hours of delay, crash rates, and other effects of each alternative relative to the base case (Table 6.3). An error in estimating costs and benefits could lead to project failure.
See Chapter 2 for a full description of costs and benefits.
9. Evaluate risk. Look at the risks associated with uncertain costs, traffic levels, and economic values. Chapter 6
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Project Evaluation
10. Conduct sensitivity analysis. Conduct a sensitivity analysis to find factors that have an important effect on the output. 11. Find benefit/cost ratio. Compare net benefits with net costs and rank alternatives based on the benefit and cost ratio (B/C ratio). The benefit cost ratio is calculated by dividing total discounted benefits by total discounted costs. Options with B/C ratios greater than 1.0 are preferable. In cases where the B/C ratios of some mutually exclusive alternatives are greater than 1.0, the incremental B/C ratio should be used. In this method, the alternatives are ranked in order of investment from the smallest to the largest. Then, the incremental benefits and costs between two alternatives are calculated (ΔX-Y) where X is a previously justified alternative. If (ΔB/ ΔC) X-Y is greater than 1.0, alternative Y is selected. If not, alternative X will be the output of CBA.
Working with B/C ratios
12. Make recommendations. Recommendations are based on the B/C ratios.
Agency Costs
• Design and Engineering • Land Acquisition • Construction • Reconstruction/Rehabilitation • Preservation/Routine Maintenance • Mitigation (e.g., noise barriers)
User Costs/Benefits Associated with Work Zones
• Delays • Crashes • Vehicle Operating Costs
User Costs/Benefits Associated with Facility Operations
• Travel Time and Delay • Crashes • Vehicle Operating Costs
Externalities (non-user impacts, if applicable)
• Emissions • Noise • Other Impacts
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Table 6.3: The Considered Benefits and Costs in CBA (FHWA 2003)
Chapter 6
Example of CBA #1 In this example, TxDOT considers five alternatives for expanding a highway. The table shows the costs and benefits of each alternative. Determine the alternative with the highest B/C ratio for this project. Costs Benefits B/C
A $6,000,000 $11,000,000 1.8
B $3,000,000 $7,000,000 2.3
C $9,000,000 $13,100,000 1.4
D $1,500,000 $1,500,000 1.0
E $15,000,000 $12,050,000 0.8
In this example, if alternatives are not mutually exclusive (as the B/C ratio of alternative B is the largest B/C ratio), the alternative B is the best alternative. If alternatives are mutually exclusive, the incremental B/C should be used among alternatives A, B, and C. In the first step, alternatives should be ordered from the smallest to the largest required investment (using the following table). Costs Benefits B/C
B $3,000,000 $7,000,000 2.3
A $6,000,000 $11,000,000 1.8
C $9,000,000 $13,100,000 1.4
Then, the ΔCosts, ΔBenefits, and ΔB/ΔC for two cases—C-A and B-C— should be calculated using the following table. ΔCosts ΔBenefits ΔB/ ΔC
B-A $3,000,000 $4,000,000 1.3
C-A $3,000,000 $2,100,000 0.7
Finally, as ΔB/ ΔC B-A is greater than 1.0, A is a better alternative than B. Also, as ΔB/ ΔC C-A is less than 1, it shows that A is the best alternative.
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Example of CBA #2 A bridge linking two towns over a river is close to failing and will be decommissioned in 5 years if repairs are not made. TxDOT is calculating a B/C ratio to compare the benefits of travel time savings, reduced operating expenses, crashes, and pollution to construction and maintenance costs. Following are the calculation steps taken to determine the B/C ratio. Removing the bridge will require some users to travel further out of their way to reach destinations across the river, resulting in increased VMT overall. Assuming a lifespan of 50 years for the rebuilt bridge, TxDOT projects VMT in the area to be as follows: No-build Bridge rebuild
Total VMT 1,400,500 1,275,000
Total VHT 40,800 39,100
Benefits TxDOT estimates that travel time savings over the 50-year period would be $250 million. Vehicle operating costs, crashes, and emissions are also functions of VMT, so if the bridge helps reduce VMT, then all crashes (fatal, major, minor, property damage), operating costs, and emissions will correspondingly decrease. TxDOT estimates that reduced operating costs will save $185 million, crash costs will decrease by $65 million, and emissions reductions will save another $45 million. Total benefit from bridge repair in present terms is $545 million. Costs Closing the bridge would require some funds, as would deconstruction. Bridge repair has been estimated at $100 million, with total operating and maintenance costs being $85 million over a 50-year lifespan, in present costs. Total cost in present terms for bridge repair is $185 million. =
$545,000,000 = . $185,000,000
This B/C ratio is greater than 1, indicating that the project returns more benefits than costs.
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Chapter 6
Life Cycle Cost Analysis (LCCA) Engineering decision-making often entails choosing the best option among many alternatives. When alternatives require different amounts of investment and yield various levels of benefit (particularly when alternatives deviate in duration), life cycle cost analysis (LCCA) can be employed to compare the alternatives on an even playing field.
Objective
Alternatives
Assumptions
Costs & Benefits
Determining NPV
Final Decision
Figure 6.4: Process of Decision Making
The National Institute of Standards and Technology defines life cycle cost (LCC) as “the total discounted dollar cost of owning, operating, maintaining, and disposing of a building or a building system” over a period of time (Fuller & Petersen 1995). LCCA considers all of the benefits and costs associated with different project alternatives over the project’s lifetime, and can be applied when an agency needs to assess the total cost of a project. It is particularly useful when deciding between various project alternatives that all meet project scope requirements, but have different initial and operating costs. For projects with comparable benefits over the same lifetime, the alternative with the lowest LCC is usually preferred. In addition, for alternatives with different lifetimes, the alternative with the lowest Equivalent Uniform Annual Cost (EUAC) is more desirable. EUAC is determined by converting all project costs into a uniform annual recurring cost over the analysis period. In calculating the EUAC, the NPV of each alternative is shown in terms of its equivalent annual payment amount (NPV and equal payment series are discussed in detail in the next section). The required inputs for LCCA include initial expenses like equipment and right-of-way (ROW) purchase, as well as future expenses such as operation, maintenance, and replacement costs. Note that many LCCA inputs are estimates. LCCA may fail to capture the uncertainty of future events and the impact of technological progress. This inability to account for uncertainty limits LCCA’s application.
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Example of LCCA A TxDOT district is deciding between flexible and rigid pavement for a new roadway. Engineers expect required surface rehabilitation after 20 years for flexible pavement and 40 years for rigid pavement. The following LCCA table was created for a 40-year analysis period, with initial and rehabilitation costs considered for each pavement type. Flexible pavement would cost $4 million initially and $2 million to rehabilitate 20 years later. Rigid pavement would cost $6 million for initial construction. Benefits for flexible pavement are reduced when travel times increase during resurfacing times. Because rehabilitation costs are to occur 20 years in the future, they must be translated to present value before they can be compared with initial costs. Benefits are already provided in present value. Assume a 2% annual interest rate. Present worth of flexible pavement rehabilitation costs in year 20: =$2,000,000×(1+0.02) = $2,000,000 ×
Initial Costs Rehabilitation Costs Total Costs Total Benefits NPV
(
.
)
= $1,345,943
Rigid
Flexible
$6,000,000 $6,000,000 $11,000,000 $5,000,000
$4,000,000 $1,345,943 $5,345,943 $10,000,000 $4,654,057
The rigid pavement returns a higher NPV and would therefore serve as a better choice.
Constrained Optimization Constrained optimization is a mathematical tool used to minimize or maximize a function ( ) subject to certain constraints ( ). The constraints can be a combination of equations or inequalities. Every project faces some type of constraint (e.g., budget or resource), so the goal is to find the optimum, or most efficient, solution that falls within those constraints. For example, TxDOT may want to decide what portion of its transportation budget should go to new road construction and how much should be spent maintenance of existing highways. Here the constraint is the transportation budget—the road construction and maintenance costs must total the budgeted amount TxDOT has available. For this type of problem, a function must be Project Evaluation
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TxDOT can use constrained optimization as a B/C approach in making choices to either maximize benefit within a budget or meet a given level of service with minimum cost. Chapter 6
created that models the total utility, or benefit, the public receives from some unit measure of added roadway capacity or maintenance. The optimization process will then seek to maximize this function to give the most benefit within the constraint of the transportation budget and find the optimal amount to allocate to new roads and maintenance. Another example is allocating monies across a variety of competing projects. Fund allocation has become an issue for all state DOTs. Constrained optimization can be used to overcome this issue. In this situation, the objective function is the following: maximize
See Chapter 5 for discussion of budget constraints.
( )= ≤
subject to ∈
,
Where bi and ci are the benefit and cost associated to each project, while B is the upper bound of the budget. is a 0-1 binary decision variable (which represents the selection of project if = 1 and the deselection of project if = 0). In order to solve constrained optimization problems, Excel’s Solver add-in can be used. Following are common elements in Excel’s spreadsheet model:
Use of Excel’s Solver feature
1. Inputs: all numerical input needed to form the objective and the constraints. 2. Changing cells: instead of using variable names, such as x’s, a set of designated cells plays the role of the decision variables. The values in these cells can be changed to optimize the objective. 3. Target (objective) cell: one cell called the target value or the objective cell contains the value of objective. Solver systematically varies the values in the changing cells to optimize the values in the target cell. 4. Constraints: Excel does not show the constraints directly on the spreadsheet. Instead they are specified in a Solver dialog box. 5. Nonnegativity: normally the decision variables should be nonnegative. Here are the stages of solving the constrained optimization problem in Excel: 1. Formulate the model: enter all the inputs, trial values for changing cells, and formulas in a spreadsheet. In particular, spreadsheet must include a formula that relates the objective to changing cells, so that if the values in the changing cells vary, Chapter 6
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the the the the Project Evaluation
objective values vary automatically. 2. Call solver: designate the objective cell, the changing cells, and the constraints. Then tell Solver to find the optimal solution. Note: based on the Solver option chosen in Excel, the solution may vary. However, the value of the objective function will be within a reasonable range of the optimal solution (performing a completely exhaustive search would be impractical). The following example will show how to use Excel’s Solver for optimizing budget allocation.
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Chapter 6
Example of Optimizing Budget Allocation The Capital Area Metropolitan Planning Organization’s (CAMPO) Transportation Improvement Program identified 14 candidate projects for fiscal years 2011–2014. These projects are a variety of roadway expansion and improvement types. The potential benefits and costs of each project are shown in the table (project costs and descriptions taken from CAMPO’s MIP for FY 2011–2014). Following are the constraints for allocating the budget to these projects: 1. No more than two projects can be implemented in each of the four locations shown in the table (i.e., Austin, Georgetown, Cedar Park, and Other). 2. No more than three projects of the same type (new build, widening, and reconstruction) can be implemented simultaneously. 3. The total budget constraint is $556,780,000 (over 4 years). Determine which projects are feasible within the listed constraints. Proj.
Site
Roadway
1
Cedar Park
RM 1431
2
Other
FM 2001
3
Austin
SH 71
4
Other
SH 195
5
Austin
SH 130 and Cameron Rd
6
Austin
FM 3177
7
Other
IH 35
8 9
Georgetown Georgetown
10
Other
IH 35 IH 35 US 79
Project Description (basic project type in italics) Widen a 4-lane divided arterial to a 6-lane divided arterial with wide outer lanes, raised median and sidewalk fronting public land Realign a 4-lane divided roadway Build an underpass, frontage roads and main lanes Widen existing 2-lane roadway to 4-lane divided roadway Build northbound and southbound entrance ramps and related toll integration equipment Realign FM 3177 Build southbound frontage roads and convert frontage roads to one-way operation Build a 3-lane frontage road and ramps Build ramp and auxiliary lane and reconfigure ramps Widen roadway to a 4-lane divided arterial
Cost (Initial)
Benefit (NPV)
B/C*
$26,809,766
$102,146,400
3.81
$4,899,000
$5,878,000
1.20
$54,016,584
$464,265,000
8.59
$46,191,075
$354,292,900
7.67
$4,610,000
$36,100,000
7.83
$4,955,552
$40,644,000
8.20
$14,026,000
$110,390,000
7.87
$8,486,383
$67,295,000
7.93
$2,250,000
$16,875,000
7.50
$16,346,887
$137,030,000
8.38
Build 6 tolled main lanes and 6 continuous non-tolled frontage $455,900,000 $3,128,500,000 6.86 roads Build an overpass at FM 20 and 12 Other SH 71 $16,624,199 $114,680,000 6.90 frontage roads Widen FM 1626 to a 4-lane 13 Other FM 1626 $47,312,666 $402,157,000 8.50 divided roadway Build northbound and 14 Austin Loop 1 $253,162,143 $739,229,000 2.92 southbound managed lanes *B/C ratio includes only initial costs. A comprehensive CBA would include costs like annual maintenance. 11
Chapter 6
Austin
US 290
6-21
Project Evaluation
The objective of this example is to maximize the benefit accrued to the society by selecting the proper projects, which can be represented mathematically in a standard maximization equation such as the following: maximize (x) =
+
+ ⋯+
where = $102,146,400, = $58,785,000, …, = $739,229,000, and xi represents each project i. This function seeks to maximize the net benefit of a selection of projects by summing their benefit values ( ) multiplied by a binary variable ( ), which takes on a value of 1 if the project is selected, and 0 otherwise. Of course, maximization has constraints as mentioned above, including funding and resource limitations. The following equations can be written to represent each of the specific constraints introduced for this example. (1)(
+
+ ⋯+
≤
(2)
+
+
+
+
≤
(3)
+
+
+
+
+
(4)
+
+
+
(5)
+
+
(6) ,
,…,
+
+
+ ≤
,
≤ +
,
+
≤
,
,
∈ 0, 1
where = $26,809,766, = $4,899,000, …, = $253,162,143; = $556,780,000; = 2 and , = 3. , Constraint 1 ensures that the sum of the costs of the selected projects (∑ ) does not exceed the budget ( ). Constraint 2 ensures that no more than two projects will be implemented in Austin. Likewise, constraint 3 ensures that no more than two projects will be implemented in location “Other.” Note that two or fewer projects are being considered for Cedar Park and Georgetown; thus, constraints are not needed for these two locations. Constraint 4 ensures that no more than three new-build projects are selected. Likewise, constraint 5 ensures that no more than three widening projects are selected. Constraint 6 determines the binary project selection term. Note that because only two realignment projects are being considered, no constraint is necessary.
Project Evaluation
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Chapter 6
Stages for formulating the MS Excel spreadsheet model are as follows: 1. Enter inputs: Enter the various inputs such as cost, benefit, budget, maximum number of projects in each region, and maximum number of projects in each type (F4:G17, F22, F28, and F34, according to Figure 6.8, which shows the example worksheet). 2. Change cells: Enter any values in cells I4:I17. These values do not have to be the values shown. These are the cells where the decision variables are placed. Any values can be used initially. Solver will eventually find the optimal values. 3. Define type of constraint for each changing cell: As x1 to x14 can be either 0 or 1, choose the bin (binary) option in the Add Constraint dialog box (Figure 6.5).
Figure 6.5: Defining Type of Constraint for x1
Constraints include the number of projects in Austin, the number of projects in other areas, the number of widening projects, and the number of new-build projects. To define these constraints, put the left side of equation in designated cells, and define the constraints type in Solver (Figure 6.6). For example, for number of projects in Austin, put I6+I8+I9+I14+I17 in cell F24. Then, in the Add Constraint dialog box, enter $F$24
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