State Highway Cost Allocation Studies (2008)

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The first six subsections of this chapter draw heavily on the guidelines that were prepared for the FHWA to assist states in properly applying the generalized State HCAS Model completed in 2002. It has been refined and updated as a prod- uct of this HCAS synthesis project. EXPENDITURES The work required to prepare expenditure data is usually one of the most time-consuming and tedious tasks involved in a state HCAS. Unfortunately, there is little or no standardiza- tion among the states in the available databases, and, there- fore, examples of this work from previous studies provide only general guidance. Tables 9 and 10 provide two examples of expenditure data developed by this project’s study team for two different state HCASs. The “construction” or “capital expenditures” line items are usually taken directly from each state’s STIP or the equivalent. Typically, a STIP will include officially adopted program expenditures for construction and related projects for several future periods—usually five years or longer. The STIP categories typically include several highway federal- aid categories plus a few other categories involving little or no federal aid—typically minor or specialized type projects such as resurfacing, restoration, rehabilitation, and recon- struction (4R) projects; sign and signalization improvements; rest area improvements; and toll facilities, buildings, and other capital improvements needed for the administration of highway and other state transportation programs. In general, one can expect the composition of projects within each STIP program category to have approximately similar characteristics from a cost allocation perspective, but to have relatively different characteristics from projects in other program categories. For example, most 4R projects in- volve very high proportions of pavement rehabilitation, but very little new pavement construction, bridge work, or grad- ing; most Interstate maintenance projects also involve high proportions of pavement rehabilitation, but they also tend to involve more work on various other highway elements than typical 4R projects. Most bridge projects on the other hand involve primarily bridge construction or repair, but very little pavement, grading, or other work. Table 11 provides an illustration from a state HCAS of how construction program expenditures are broken down by 26 cost allocation category. A conversion matrix such as this should be developed for each state from current project data to obtain an accurate and up-to-date basis for conversion of STIP expenditures into cost allocation categories. Once a conversion matrix of this type is developed for a state HCAS, it may be reused in subsequent studies in that state, with review and revisions as appropriate to reflect any changes in program categories or other factors that might change the matrix. Data are usually available in each state to develop con- version matrices such as those shown in Table 11; however, they are contained in a wide variety of formats. Usually they are project-by-project records with quantities and/or cost estimates broken down into very detailed categories. Typi- cally, the development of a matrix such as Table 11 will in- volve careful consideration of the definition of the individual quantities and the nature of the project, and often all of these factors are computer coded. For example, different cate- gories of concrete may be used in pavements, bridge decks, and structures; different codes may be implemented for as- phalt used for new pavements, resurfacing, and pothole patching, and different definitions of the purpose of the proj- ects can be used to distinguish these elements when other dif- ferences in codes are inadequate. A matrix should also be developed for each state to con- vert STIP expenditures into expenditures by highway class. These types of matrices are somewhat more likely to change over time, but are usually much easier to update from readily available project data. Programmed expenditures should also be separated out by level of government for the source of funding for use in sep- arate analysis by level of government, as will be discussed in a separate subsection. Other expenditures are usually pro- jected each year for the STIP period by each state in a very general manner. This is done to provide a basis for estimat- ing funds that will be available for highway construction and other capital expenditures after maintenance, other commit- ments, and administrative costs are subtracted from projected revenues. Typically, these non-construction expenditure projections for the STIP period must be disaggregated from one or more broad categories into several categories required for cost allocation purposes. Often, the non-construction expenditure CHAPTER FIVE GUIDELINES FOR ANALYSES NEEDED FOR HIGHWAY COST ALLOCATION STUDIES

27 Expenditure Categories $ Millions Transportation Department C onstruction and related construction (split into program and HCA categories) 146 project development (allocate as overhead) 4 other construction related 21 Maintenance and related highway maintenance 64 maintenance related (allocate as overhead) 4 Other operational functions ports of entry operations 4 other district operations 5 non-highway capital facilities (overhead or other) 4 highway safety 1 other highway operations (overhead) 7 Public transportation urban public transportation 2 intercity rail 0 Other modes of transportation aeronautics (not to be allocated) 1 ports, inland waterways, pipelines, etc. (not to be allocated) 1 Departmental services management services to be allocated (overhead on items to be allocated above) 8 management services not to be allocated (overhead on items not to be allocated) 0 support services to be allocated (overhead on items to be allocated) 5 support services not to be allocated (overhead on items not to be allocated) 0 MVA (often separate department) heavy vehicle fee apportionment programs (allocate to heavy-vehicle VMT) 1 audit portion of motor vehicles (allocate to heavy-truck VMT) 1 regulatory and administration related to motor vehicle dealers (allocate to all vehicles) 0 other MVA (allocate to all vehicles) 1 Subtotal, transportation department 280 Department of Law Enforcement Off-highway police services (not to be allocated) 11 Highway patrol (allocate to state highway VMT) 16 Alcohol beverage control (not to be allocated) 1 Academy and training programs (allocate pro rata share to VMT) 1 Heavy vehicle weight enforcement (allocate to heavy-vehicle VMT) 0 Heavy vehicle inspection programs 0 Other Departments (overhead on appropriate items above) Attorney general services to highway agencies 1 Courtsí highway-related functions 1 Air quality and environmental programs related to highways 0 General administrative functions (pro rata for highways) 2 Highway-user revenue collection and related enforcement 0 Local Government Assistance for Street and Highway Programs To cities (to be split as appropriate) 22 To counties and highway districts (to be split as appropriate) 51 Total 386 TABLE 9 EXAMPLE OF EXPENDITURE CATEGORIES FOR STATE HIGHWAY COST ALLOCATION

28 Sources Federal ($ Millions) State Highway Account (excluding state aid) Maintenance of state highway system 0 Highway operations (other non-capital) 0 Capital expenditures (includes project support) 4R projects 333 minor projects 0 bridge projects 40 transportation system management projects 40 ma jor highway projects 173 transit and rail 178 Transportation Planning and Intercity Rail 30 Other Expenditures Related to the State Highway System Department of motor vehicles 0 Highway patrol (enforcement, safety, inspections) 0 Other agencies’ highway-related expenditures (courts, emissions control, user-revenue collection, etc.) 0 State Aid to Local Governments Capital expenditures 0 Maintenance 0 Transportation planning, bicycle lanes, emergency repairs 0 All other 0 Federal aid to local governments 367 Total State ($ Millions) 465 314 150 58 91 34 198 203 42 351 548 70 378 310 88 107 0 3,407 1,161 TABLE 10 PROGRAMMED HIGHWAY-RELATED EXPENDITURES (STATE AND FEDERAL SOURCES) Percentage Split by Cost Allocation Category for Each Program Category Source Total Interstate Maintenance 100% National Highway System 100% Surface Transportation Program (STP)—State 100% STP—Local Rural 100% STP—Local Urban 100% STP—Safety 100% STP—Enhancement 100% Congestion Mitigation and Air Quality 100% Bridge Projects 100% Demonstration Projects 100% 4R Projects New Pavement 2% 11% 9% 11% 2% 0% 9% 9% 6% 7% 5% Rehab. Pavement 54% 31% 36% 35% 17% 14% 36% 36% 3% 66% 67% New Bridge 0% 1% 0% 0% 0% 0% 0% 0% 0% 7% 0% Replace- ment Bridge 11% 4% 11% 13% 6% 0% 11% 11% 69% 0% 1% Bridge Repair 1% 1% 1% 0% 6% 0% 1% 1% 1% 0% 6% Grading 3% 26% 26% 35% 14% 5% 26% 26% 9% 12% 6% Other 29% 26% 17% 6% 55% 81% 17% 17% 12% 8% 15% 100% TABLE 11 ILLUSTRATION OF THE CONVERSION OF A STATE’S FIVE-YEAR CONSTRUCTION PROGRAM EXPENDITURES INTO HIGHWAY COST ALLOCATION CATEGORIES

29 projections will be broken down into agency programs or for- mula allocations—for example, MVA, state police, and state aid to local governments. If not, the first task is usually to estimate such broad breakdowns based on trends and inter- views with program administrators. A critical next step is usually to break down administrative expenditure projections into broad cost allocation categories (e.g., construction, maintenance, motor vehicle, police, tran- sit, and other modes). The critical question to be raised in this context is how such administrative costs should be allocated to vehicle classes. In some HCASs, most or all administrative costs have been allocated as common costs, often using VMT as the allocator. However, most practitioners prefer the more refined approach that has been used in many recent HCASs— that is, treating each major component of administrative cost as an overhead on the specific program category. For exam- ple, construction engineering and construction management costs are allocated to vehicle classes in proportion to the results of the cost allocation for direct construction expendi- tures. Similarly, administrative and management costs for highway construction and maintenance as a whole are allo- cated based on the combined cost allocation results for direct construction and maintenance expenditures. Motor vehicle administrative costs are usually treated somewhat differently. They are first broken down into func- tions relating to different classes of vehicles that are admin- istered in a different manner—usually two or more classes: (1) heavy and larger vehicles whose registration fees are pro- rated among the states, and (2) all other vehicles, sometimes further divided into light- and heavy-vehicle classes if administrative costs per vehicle are significantly different be- tween these two classes. Police costs are also usually broken down into vehicle classes covered by the various functions (e.g., vehicle inspection, weight enforcement, and general traffic service and enforcement). The final major step in analyzing expenditure data is to link all expenditure projections to cost allocation factors. In many cases, this requires significant further analysis to pro- vide a basis for splitting expenditures into cost components, such as the breakdown of pavement expenditures into broad classes of pavement work, the cost of pavements of mini- mum thickness versus the cost of added thickness necessary for supporting axle-load repetitions, and the expenditures for different types of maintenance activities. VEHICLE-MILES OF TRAVEL AND RELATED DATA The travel and related data requirements for the FHWA State HCAS Model include annual statewide VMT broken down into the following categories: • Vehicle configuration, • Registered gross weight, • Fuel type, and • Functional class of highway or other user-specified highway classes. The primary source for the basic set of VMT data in most states is the data compiled for the Highway Performance Monitoring System, which is reported each year by each state to the FHWA. These data include VMT by 12 vehicle con- figurations (13 if motorcycles are separated out) and 12 high- way functional classes. Some states may not have all the breakdowns required, such as splits of VMT for single-unit trucks broken down into two, three, or four or more axles, or splits of combina- tions into all of the standard seven classes of combinations. In such cases, a state’s VMT for larger classes can be disag- gregated into the more detailed classes using FHWA data. FHWA may have developed estimates for more detailed breakdowns for that state and can supply data from other neighboring states or states with similar economies and other characteristics. The developers of the FHWA State HCAS Model recom- mended that each state analyze trends in VMT by vehicle configuration and functional highway class for the most re- cent several years. Unless a state routinely performs such analysis in the process of preparing each year’s estimates, there are usually irregular trend lines for some of the break- downs, particularly for the breakdowns that have very small shares of total VMT (e.g., three- and four-axle combinations, and six- or seven-axle doubles). The analysis of trends in the breakdowns of VMT will usually result in the need for a few small adjustments in the percentage splits for one or more of the several years of data. Once this is done, a decision should be made as to how to project these percentage splits, either by (1) using the most recent splits for the future year(s), (2) using average splits for the several years, or (3) projecting trends in changes in the splits. Care should be taken in choosing the third option, however, because such trends may reflect short-term fluctu- ations that are not likely to be sustained for long. Thus, a compromise might be made between the first or second option and the third option. Annual mileage per vehicle and gallons per mile are vari- ables for which good estimates have been developed for each state in the default data contained in FHWA’s 2001 State HCAS Model, based on analysis of the 1992 Truck In- ventory and Use Survey (TIUS). Some refinements in these estimates might be made, however, if a state desires, using more detailed analysis of that state’s 1997 and 2002 VIUS data. Estimates of new power unit prices were provided in the FHWA State HCAS Model’s default database in the form of equations expressing price as a function of registered gross

30 weight for heavy trucks, and as a single average value for all light vehicles. These values and relationships are approxi- mate estimates judged to be satisfactory for attributing ad valorem revenues (e.g., sales taxes, title fees, vehicle license fees, and other fees that vary as functions of new vehicle price or depreciated value). However, if a state has a major portion of its highway-user revenue from ad valorem taxes, it may wish to refine these values and relationships by analy- sis of recent new vehicle prices, using manufacturers’ data or other published sources. The default data in the FHWA State HCAS Model used for splitting VMT for 12 vehicle configurations into VMT for 20 configurations are based on national VMT data de- veloped in the 1997 Federal HCAS. These factors are sound estimates at the national level, but are considered to be only very approximate estimates at the state level. They may be highly inaccurate for some states that have unusual size and weight limits (e.g., Michigan) or concentrations of industries that use particular types of configurations (e.g., particular types of natural resource hauling in some Rocky Mountain states). Such states may wish to perform special analysis of VMT for heavy- and longer-vehicle configurations, using either detailed Highway Performance Monitoring System data and/or WIM data. Both types of data can be used for such analysis. Registered gross weight breakdowns for each vehicle configuration are likely to vary substantially among the states. The data provided with the 2001 State HCAS Model are only representative data—that is, not considered to be accurate enough for drawing conclusions regarding the equity of the tax structure for different registered gross weight (RGW) classes. Unfortunately, there is no common source among the states for this variable. VIUS might be used for doing this; however, we are not aware of any analysis of this type that has been done for any state. Gen- erally, any state that has an interest in developing estimates of cost responsibility of vehicles by RGW has specialized data that can be used. The best source of this type of data exists in those few states that have tax records on reported mileage by RGW—typically those states that have weight- distance taxes. Many states also maintain good databases on registered vehicles by RGW; however, these data are not adequate, by themselves, for estimating breakdowns of VMT by RGW because of (1) the wide variation in annual miles of travel as a function of RGW, and (2) the wide vari- ation in out-of-state travel as a function of RGW. VIUS data can be analyzed to develop estimates of both of these relationships, and to use the resulting relationships in con- junction with registration data, to develop estimates of VMT by RGW. Estimates of fuel type splits by vehicle configuration that are in the State HCAS Model are considered to be sufficiently accurate to be used in all state HCASs. They are based on an excellent database from one state, and slight inaccuracies in this variable have no significant effect on the results. This is true because the vast majority of all light vehicles are gasoline- powered and the vast majority of all heavy vehicles are diesel-powered, particularly when vehicles are weighted by annual miles of travel. Average power unit and trailer life have a very small effect, or no effect at all, on the results of the revenue attri- bution process. They effect only the results of revenue attri- bution for ad valorem taxes, and then only to a small extent. Therefore, most states need not perform any analysis of this variable unless ad valorem taxes are a very large share of total tax revenue. PAVEMENTS AND RELATED DATA A good HCAS model, such as FHWA’s 2001 State HCAS Model, should be designed to handle four pavement cost cat- egories: new flexible pavements, new rigid pavements, flex- ible pavement repair and reconstruction, and rigid pavement repair and reconstruction. Each should be broken down into the standard 12 functional classes of highway (or other types of highway classes) and by any special funding categories the user wishes to analyze. FHWA’s State HCAS Model con- tains representative values of expenditures for each highway cost allocation category, including the previously mentioned four pavement categories. The following additional inputs may be required for pavement cost allocation, all of which have default values provided in FHWA’s State HCAS Model: • Distribution of VMT by vehicle configuration and high- way class. • Operating gross weight distributions by vehicle config- uration (and optionally by highway class). • Axle-weight and axle-type frequency distributions for each operating weight and vehicle class. • Typical pavement sections and traffic proportions that represent the flexible and rigid pavements for each highway class. • Number of miles on each highway class (to determine average daily traffic loadings from VMT data) for new pavement cost allocation. • Annual ESAL growth rates by highway class for new pavement cost allocation. • Pavement design parameters applicable to the state in question for new pavement cost allocation. • Minimum pavement thicknesses for rigid and flexible pavements. • Pavement distress distributions and load-equivalency factor regression coefficients for each highway class, for pavement rehabilitation cost allocation. • A conversion key, if necessary, to convert state-specified highway classes to the 12 highway functional classes used in NAPCOM.

31 The default values supplied with the State HCAS Model are all based on the 1997 Federal HCAS. These data include: • VMT data by vehicle configuration and functional highway class; • Minimum pavement thicknesses and the specification of which state and coefficient option to use; • Operating gross weight distributions by vehicle config- uration, and space for the user to specify different weight distributions for selected highway classes or groups of highway classes; • Axle weight distributions for each vehicle configuration and operating gross weight group; and • All other pavement data required by the State HCAS Model. The guidelines that accompany the model also contain advice for modifying the data for use in each state. BRIDGE DATA A sound HCAS model should be designed to handle four bridge cost categories: new bridges, bridge replacement, bridge repair, and possibly special bridge costs. An example of special bridge costs is the retrofitting of existing bridges for earthquakes. The following additional inputs are typically required for bridge cost allocation, all of which have default values pro- vided with the State HCAS Model: • Assignment of vehicles to bridge increments based on their live-load moments, • An allocation of the cost of various types of new bridges to bridge increments, • Information on the types of material and span lengths for new and replacement bridges, • Inventory ratings of bridges that are to be replaced, • An estimate of the percentage of bridge replacement costs owing to structural deficiencies in existing bridges, • An estimate of the percentage of bridge repair costs that are load-related, and • An estimate of the percentage of special bridge costs that are load-related. Default values based on the 1997 Federal HCAS are pro- vided for the rest of the information required for the State HCAS Model. The guidelines provided with the State HCAS Model also provide detailed advice for handling each of the following operations: • Assignment of vehicles to bridge increments, • Allocation of new bridge costs to bridge increments, • Types of material and span lengths for new and re- placement bridges, • Inventory ratings of replaced bridges, • Bridge replacements resulting from structural deficien- cies, and • Load-related bridge repair and special bridge costs. MAINTENANCE AND OTHER DATA In addition to the travel and vehicle characteristic data and inputs required for pavement, bridge, and other costs in a sound HCAS model, a carefully performed maintenance cost allocation procedure should require: • Expenditures for different categories of maintenance work, broken down by highway class: travel-related maintenance, wear-related flexible pavement mainte- nance, wear-related rigid pavement maintenance, axle- related maintenance, truck-mile-related maintenance, light-vehicle-related maintenance, and possibly rest area maintenance; and • Specification of allocators for each of these cost cate- gories. Many states maintain detailed records of maintenance costs by specific type of maintenance activity, such as pavement sur- face patching, joint and crack filling, culvert cleaning, bridge painting, snow plowing, etc. These records also often include breakdowns of maintenance costs for specific routes or sec- tions of routes (e.g., by county or highway district). When such data are available, the user’s primary task is to decide what allocation factors to use for each maintenance activity or group of activities. An analyst doing a state HCAS may want to consult the example contained in an appendix to the guidelines for application of FHWA’s State HCAS Model as a guide in preparing a similar table with that state’s maintenance activities (Sydec 2000). In the event that a state does not maintain detailed records of maintenance cost broken down by specific activity, the user of the model should make estimates of breakdowns of maintenance expenditures by major class of activity through interviews with the maintenance managers who are responsi- ble for assigning work to crews. This may require meeting with managers in each district to fill out forms that break down known totals and/or subtotals of maintenance expendi- tures into the desired shares by major class of activity. A few states keep maintenance costs by functional class of highway (or other highway classes that might be used in HCASs). However, more commonly there is a significant problem in converting costs to these highway classes. A fairly common problem is the need to convert costs by route or seg- ment of the route into costs by highway class. This typically involves building a conversion matrix, using the assumption that maintenance costs per lane-mile are constant, at least for each highway class. Average maintenance costs per lane- mile for each highway class can be estimated from routes that

32 are entirely in each highway class. Maintenance costs for routes that are in more than one highway class can then be split between highway classes based on lane-miles in each highway class and average maintenance costs per lane-mile for the different highway classes. The guidelines that accompany FHWA’s State HCAS Model also include specific advice and options for the allocation of the following other categories of highway costs: • Grading costs, • Residual allocators, • Width shares, • General construction costs and transit costs, • Multi-highway system costs, • Other travel-related costs, • State police traffic management, and • Vehicle registration costs. ISSUES IN REVENUE ATTRIBUTION The revenue attribution process is a straightforward splitting of revenue actually collected or projected to be collected for a future program period among the vehicle classes, sepa- rately for each highway-user tax or fee. Usually this is done in a two or more step process; for example, by splitting fuel taxes into revenue by fuel type, then into light versus heavy vehicles, and finally into the specific vehicle classes using historical or projected fuel economy by vehicle class based on data reported in the VIUS conducted every five years by the U.S. Census Bureau. A key part of accurately attributing revenue is to split vehicles in each class into full-fee paying vehicles and exempt-, partially-exempt, and special-fee paying vehicles, if any, based on vehicle registration data. Many states have all three non-full-fee-paying vehicle categories, and the num- ber falling into each class varies by type of tax or fee. In properly assessing the equity of a state’s overall high- way-user tax structure, only the full-fee-paying vehicles are generally included. It is important to recognize that the pro- portion of vehicles that are full-fee-paying varies widely by vehicle class. In most, if not all states, these vehicles are a small minority of buses, but a majority for all other vehicle classes. For these other vehicle classes, subsidized vehicles (mostly government owned) usually make up a larger portion of all vehicles for some of the lighter single-unit truck cate- gories. However, some subsidized vehicles are usually found in all vehicle classes. Two analyses that are critical in the revenue attribution process are: 1. The development of carefully fitted curves for fuel econ- omy by fuel type as functions of registered weights of vehicles. Experience shows that this should be done sep- arately for single-unit trucks and combinations (which generally are more fuel efficient than single-unit trucks at most registered weights). Consideration should be given to developing curves separately for two categories of fuels: gasoline-powered vehicles and all others; that is, those powered by diesel and other special fuels. 2. The development of another carefully fitted curve for average annual mileage per vehicle as a function of registered weights of vehicles. Experience shows that this should also be done separately for single-unit trucks and combinations, which generally travel more annual miles than single-unit trucks, particularly at most higher registered weights. Curves can be developed from VIUS data and have been done using the 1992 TIUS in the default database that is part of the FHWA State HCAS Model. Default data for much of the other data needed for revenue attribution can also be found there, along with guidelines on how these data can be used in the model. Experience in preparing these default data suggests that it is important to focus primary attention on data in the VIUS for each state rather than relying on national data. However, care is needed in doing this because individ- ual state-level data are based on much smaller samples and tend to create much more erratic plots. The curve fitting often requires careful use of judgment and often requires supple- mentary review and analysis of data for states that are simi- lar in their economies and geography. To perform a sound revenue attribution process it is unnec- essary to deal with some related issues that have arisen in some states. For example, a tangential diversion from the basic analysis required for good practice in revenue attribution or cost allocation is to confuse subsidies with costs and then allo- cate the amounts of a tax subsidy to the vehicle classes or any other taxpayer. Economists often consider subsidies as costs in the sense one might think of the loss of revenue as an “opportunity cost” to the economy. However, a tax subsidy is by no means a cost attributable to vehicle classes in an HCAS because the responsibility for extending the subsidy is that of a policy maker or legislator and not the highway user. Another issue dealt with in at least two recent state HCASs revolves around the issue of evasion. This is some- what related to the analysis required in the revenue attribu- tion process in that an analysis of miles of travel within a state by full-fee-paying vehicles could in theory be used to make rough estimates of evasion of diesel fuel taxes or weight-distance taxes. In turn, some states calculate revenue that is not being collected and then effectively consider that lost tax revenue when determining the fair share that each vehicle class should pay. For example, if payments from heavy vehicles fall 10% short of cost responsibility but eva- sion is estimated at 10%, the argument has been advanced that no adjustments to tax rates are necessary. Rather, those who are paying are currently meeting their cost responsibility

33 and the state should increase revenues from heavy vehicles by addressing the evasion issue. This argument is generally not accepted by HCAS practitioners and evasion analysis is not a generally accepted part of a sound revenue attribution process. Evasion studies are often extremely complex and well beyond the scope of a typical HCAS. When construct- ing an evasion estimate, the simple analysis of VMT and MPG is not an accepted method owing to the margin of error in VMT and MPG calculations. The margin of error in these calculations typically exceeds the expected level of evasion. DIFFERENT LEVELS OF GOVERNMENT AND EQUITY RATIOS Unfortunately, many state HCASs have not recognized the importance that the level of government has in influencing study findings. Many states’ equity ratios depend on what levels of government are being considered. For example, in the case of the 2002 Idaho HCAS, when state and federal programs were combined, the typical 18-wheelers (combina- tion trucks with registered weights in the 70,000-lb to 80,000-lb range) were found to be substantially underpaying with an unadjusted equity ratio of 0.74 and adjusted equity ratio of 0.89 (percent of total state and federal revenue paid divided by percent of total cost responsibility for state and federal programs combined). However, when only state programs were considered, these typical 18-wheelers were substantially overpaying (ratios of 1.23 and 1.27 for the unadjusted and adjusted equity ratios, respectively). Historically, state HCASs more often focused on the state highway network, state taxes and fees, and state expenditures for highways. However, once the Interstate network was completed and the use of federal and state funds became more flexible, more studies have examined at least state and federal funds (Virginia and Wisconsin), whereas others have examined federal, state, and local funds as well (Arizona, California, Idaho, Indiana, Nevada, and Oregon). Programmed expenditures are generally separated out by level of government for the source of funding, as the exam- ple provided in Table 10 illustrates. A sound highway cost allocation model should provide for separate allocation of expenditures for (1) state funds used in state-level programs; (2) state aid to local governments; (3) federal aid and possi- bly, depending on the level of state interest, policies, and the size of the program, direct federal construction and operations on federal lands; and (4) local expenditures from local fund- ing sources, again depending on the level of state interest and policies. Separate allocation of expenditures for each level of government makes it possible to present results in different ways (e.g., state and federal separately or in combination). Revenues from each level of government should be ana- lyzed separately for essentially the same reason. In general, revenues and expenditures for any selected period for the analysis cannot be expected to be exactly equal, either as a whole or for any level of government. Even in states where, either by established legislative policy or by constitutional requirement, all revenues are dedicated for highway pur- poses, there will be differences, if only resulting from lags between revenue collection and obligations of funds or actual expenditures. In some states, and at other levels of government, users pay taxes or fees that are legally not considered highway rev- enues. In other cases, highway revenues are used for other modes based on the argument that those expenditures benefit highway users by reducing congestion and/or reducing the need for costly highway improvements. More often, highway programs are paid for in part by non-user revenues (generally without regard to any local connection, such as when general revenues are used) or, alternatively, user revenues are diverted for other purposes, such as deficit reduction or balancing budgets. In any case, good practice requires proper accounting for all user fees on the revenue side, regardless of where they are used, and all highway-related expenditures, regardless of the source of funds (at that level of government). HCAS practice generally includes the reporting of these imbalances, and then to fairly assess the tax structure’s degree of equity for each vehicle class, examiners generally modify raw equity ratios to “adjusted” equity ratios by expressing each ratio as the percent of total user revenue paid divided by the percent of total cost responsibility. The level of interest and responsibility that states have for local street and highway programs varies widely. How- ever, most states will at least include an analysis of cost responsibility for state aid to local governments for high- way construction or for highway construction, mainte- nance, and other highway-related programs. Some states have also done separate analysis of cost re- sponsibilities for local highway expenditures by local gov- ernments from their own local funds. For almost all states, these expenditures by local governments are primarily from local non-highway-user revenue sources—for example, general revenues or property taxes. Many states, however, do have some local highway-user taxes or fees, such as vehicle registration fees or fuel taxes. However, these local sources usually yield a relatively small amount of revenue compared with total local highway-related expenditures from local funding sources. If a state wishes to perform an analysis of cost responsi- bility for local expenditures from local sources, practitioners generally recommend that a parallel analysis of local highway- user revenues also be conducted, to the extent that such local highway-user taxes exist, to present a complete picture and produce local level equity ratios. Typically, these equity ra- tios will be small; for example, in the range of up to only about 0.2 or less.

34 A survey of local governments to determine how funds are used is a principal additional task that is necessary in most, if not all, states to do an equity analysis at the local level. Survey forms may be sent either to a sample of local governments (appropriate in states with many local govern- ments of the same type) or to all units of local government (appropriate in states with relatively few units of each type of local government). If samples are used, a representative sam- ple should be implemented for each type of local government that has responsibility for streets and highways (e.g., cities, towns, counties, and local highway districts). One possible way of simplifying the local-level analysis is to reduce the number of highway-functional classes to just one for local rural areas and one for local urban areas. This eliminates the need to develop data needed to split local ex- penditures among several highway-functional classes; how- ever, this raises the additional complication of preparing data specifically for the user-defined highway classes or preparing special tables to convert default data prepared for the 12 functional classes to data by the user-defined highway classes. The federal-level analysis is very similar to the direct state-level analysis. When compared with the local-level analysis, it is simpler in one important way—no special sur- vey of expenditures is necessary. The major complexity involved in the federal analysis is that state and federal ex- penditures should be separated from each other at the most detailed level in preparing inputs. Ideally, this should be done in analyzing project-level data to prepare factors for convert- ing programmed expenditures into expenditures by cost allo- cation category. For example, a state will normally have both state and federal funds programmed separately for several construction categories (e.g., interstate maintenance, Na- tional Highway System projects, and 4R projects) and will have to analyze a project database to develop two matrices (for state and federal funds) to convert these programmed ex- penditures into cost allocation categories (e.g., new pave- ments, pavement rehabilitation, and new bridges). Similarly, separate sets of factors should be developed for splitting state and federal expenditures into classes of highways. States with substantial direct federal construction programs may choose to include these expenditures in the federal-level analysis. Highway Statistics has data on such expenditures by state for historical years and these can be used with appropri- ate growth factors for a forecast year. Most of these expendi- tures occur on lower-level rural functional classes of highway (see http://www.fhwa.dot.gov/policy/ohpi/hss/index.htm). Re- lationships developed for state-level construction programs that are concentrated on these types of highways (e.g., sec- ondary highway programs) can be used for most direct federal construction programs. The federal revenue attribution process should develop revenue control totals by type of federal tax using data from Highway Statistics for historical years with appropriate growth factors for a forecast year. EMERGING ISSUES AND OTHER PROGRAMS Equity principles should logically be applied to any highway program involving collection of substantial user fees and/or expenditure of substantial funds for highway-related pur- poses. A prime example of a common program of substantial size is bond financing of highways with repayment from user fees. In the simplest case, there is a potentially large equity imbalance in that current highway users are the primary ben- eficiaries and future highway users are the source of the primary payments. Cases like this can be evaluated in equity terms using all the relevant good practices described in these guidelines. Toll systems are another potentially important application of equity principles. FHWA’s State HCAS Model was set up specifically to conduct such an analysis because the FHWA understood the potential for growing extensions of toll sys- tems. Automatic toll collection systems and associated regional fund transfer systems are now in place, and there is growing evidence that users are accepting such systems, in part because the user payments are more convenient. The important thing to note when it comes to toll systems, particularly as they are considered for widespread highway system extensions, is that they provide the potential for real- time payment to be made based on short-run marginal costs. Of primary interest from an equity perspective is that the basic supporting argument for implementing real-time vari- able pricing is that the external or social costs of congestion could cease to exist (to the extent that the costs are accurately estimated and applied) and could, instead, become “internal” highway-user costs. High-occupancy toll (HOT) lanes also represent an emerg- ing issue with an important equity dimension. HOT lanes are expanding in heavily congested regions around the country. These are the only significant operational systems for which external congestion costs have largely, if imperfectly, been in- ternalized. The algorithms being used in HOT lane systems are designed to apply real-time adjustments to the tolls col- lected so that specified levels of service will be maintained. For toll systems and HOT lanes, the equity analysis issues that arise are: (1) what are these short-run marginal cost-based fees, and (2) how do they compare with the actual payments being collected based on the algorithms being used? Finally, the other increasingly large emerging category involving large equity questions are PPPs. As with bond pro- grams, a typical PPP involves up-front private capital subsi- dizing current and near-term future users, and subsidies of those users by future users on a long-term basis.

35 WEIGHT FEES AND OTHER SPECIAL FEES Unfortunately, many states charge weight fees and other spe- cial fees with little or no attention to cost responsibility of the vehicles involved. Often these fees are based only on the administrative cost of issuing permits or registering vehicles in special classes. As a result of this issue, a Special Vehicle Analysis Work- book was developed and refined in studies conducted for several states (California, Idaho, Oregon, and Vermont), and was incorporated in FHWA’s State HCAS Model. The workbook provides estimates of cost responsibility and rev- enue generated for a user-specified vehicle based on the results of the state’s HCAS. The workbook can be used to answer many types of “what if” questions for any selected vehicle. A typical question might be: “What permit fees should be charged for a particular truck configuration oper- ating at x miles and y weight in order for it to fully cover its cost responsibility?” Another example might be: “How much should the registration fee (or any other fee) be in- creased (or decreased) in order to have a truck at x registered gross weight cover at least 95% of its cost responsibility?” In the workbook, the user can select any type of vehicle from a list and modify any of the characteristics associated with the selected vehicle as desired. Unless the user specifies different values, the special vehicle characteristics are deter- mined using default values based on the characteristics of typ- ical vehicles operating in the state. The user can override any or all of these default values. At a minimum, the user must specify the levels of government for the analysis, the vehicle configuration, RGW, and fuel type. The workbook will then provide default values for all other vehicle characteristics. REGIONAL ISSUES AND POSSIBLE REGIONAL APPROACHES TO HIGHWAY COST ALLOCATION STUDIES Experience has shown that state legislators, particularly in geographically smaller eastern states, give major attention to the tax rates and fees in surrounding states. This is especially true for taxes and fees applied to heavier trucks, because of pressure to standardize taxes and fees on a regional basis. Very large proportions of heavy trucks operate on an inter- state basis and can easily change their base state to states with lower flat fees (as distinct from fees based on mileage oper- ated in each state). This suggests that some type of regional approach to the evaluation of tax structures might be useful. Examples of similar efforts in the past in related highway issues include the periodic regional conferences organized by AASHTO and its regional affiliates and the series of regional confer- ences and studies organized by states with the financial support of FHWA to establish mechanisms for regional co- operation in the administration of services to heavy interstate truck operators. This latter category included efforts to develop “one-stop shopping” services at specific locations or through on-line service, both for individual states as well as on a multi-state regional basis. Regional cooperation in this field could lead to the actual conduct of regional HCASs in which most or all of the analy- ses described in the guidelines would be done on a regional basis, including evaluation of options for improvement of the equity of highway taxes and fees. DEVELOPMENT AND USE OF SIMPLIFIED HIGHWAY COST ALLOCATION STUDY PROCEDURES Unfortunately, relatively little has been done to develop and refine simplified approaches to HCASs with the exception of the work performed by Arizona as described in chapter three and summarized in Table 6. The comparison of equity ratios in that table shows that the simplified model produced equity ratios that were in close agreement with the comprehensive HCAS for autos and buses, and although not shown in that table, were also close for the entire heavy-truck class. How- ever, because the results produced a much higher equity ratio for single-unit trucks (1.41 versus 0.90) and substantially lower ratio for combination trucks (0.81 versus 0.93) sug- gests that the simplified model might be improved by using different sets of allocation factors for these two broad classes of trucks. The overall approach would appear to lend itself to easy refinements along these lines. The Special Vehicle Analysis Workbook contained in the FHWA State HCAS Model described previously employs a different approach that also could be applied relatively easily to each vehicle class (as distinct from its application to special vehicles applying for permits or other special fee classes), and has the advantage of producing more accurate equity ratios because that model utilizes all of the important results of a recent comprehensive HCAS in its internal cal- culations of both cost responsibility and revenue payments. The other experience of note is the sensitivity analysis performed recently by Vermont in completing and refining its 2006 HCAS using the FHWA State HCAS Model. VTrans used the model to explore how sensitive the equity results were to a variety of input factors. VTrans suggests that this approach might be used in a more rigorously orga- nized manner to develop a simplified model. ALLOCATION OF EXTERNAL COSTS The term “internal costs” includes all costs of highway-related programs and use of highways that result in public expendi- tures. This is to distinguish such costs from “external” or “so- cial costs.” External costs considered in the HCAS literature (e.g., congestion, crash costs, air and noise pollution) are

36 somewhat mistakenly thought of as costs that are entirely external to user payments and, therefore, are borne by non- highway users (the larger society). In reality, the costs that are usually thought of as external or social are mixed—partially external and partially internal. For example, congestion results in wasted fuel, which increases highway-user costs. Air pollu- tion is an example of an external cost that is borne by society rather than the highway user, although in some highly polluted areas such as most of Southern California air pollution control costs are significant public expenditures. These costs should be included in every HCAS to the extent that they can be identi- fied in state, regional, and local agency budgets. The costs associated with congestion in large urban areas have grown significantly in recent years. In 2003, congestion resulted in 3.7 billion hours of travel delay and 2.3 billion gallons of wasted fuel at a cost of more than $63 billion (Schrank and Lomax 2005). Most but not all congestion costs are borne by urban highway users through fuel costs, wasted time, and vehicle maintenance costs. Highway users also impose the costs associated with ve- hicle crashes on society. How significant are these crash costs? The Economic Cost of Motor Vehicle Crashes report constitutes one of the major sources of crash cost information in the United States. The report estimated the economic cost of all motor vehicle crashes in the United States in 2000 at $230.6 billion (Blincoe et al. 2002). This study monetized the costs associated with 41,821 fatalities, 5.3 million non-fatal injuries, and 28 million damaged vehicles. The study also included a number of cost elements: • Productivity losses, • Property damage, • Medical costs, • Rehabilitation costs, • Travel delay, • Legal and court costs, • Emergency services, • Insurance administration costs, and • Costs to employers. The costs included those associated with both police-reported and unreported crashes. The crash costs are stratified by severity according to the Abbreviated Injury Scale. This study examined crash costs associated with all vehicles, including both automobiles and heavy trucks. The average crash cost when all vehicles are included is $14,102 (2002 dollars) per crash. Although significant, crash costs are partly internal because some of them are paid for by users or public agen- cies (e.g., insurance costs, police and highway patrol expen- ditures, and state and local government emergency response organizations). However, the external costs are usually much larger than these internal costs. The largest of these in mag- nitude is the cost of loss of life, loss of productive life owing to injuries, and property costs not covered by insurance. The internal or external costs are often omitted entirely from HCASs, except for some studies where the small portion that shows up in highway patrol or other state agency budgets is included. Pollution costs vary widely depending on local environ- mental and congestion conditions. In most areas, only a rela- tively small proportion of total external costs are pollution costs; however, they are a relatively high proportion in the Los Angeles basin and in several of the largest urban areas. Most pollution costs are true social costs. Extra fuel costs cover only a very small portion of these costs. Noise costs are localized and are largely internal rather than external costs. Some highway noise does negatively af- fect local communities, although its impact has been greatly reduced by noise walls and is nearly entirely internalized now for most new construction. Allocating the external costs associated with congestion, air pollution, noise, and vehicle crashes would add to the breadth and completeness of HCASs, but these costs have not been historically included in federal and state studies. Arguments offered against the allocation of these social or external costs have included that they are much more difficult to quantify than direct costs and that states do not have in place a set of user charges to cover these costs (Stowers et al. 1998). In an addendum to the 1997 Federal HCAS, the U.S.DOT estimated the costs associated with air pollution, crash costs, congestion, and noise (Table 12). The economic costs asso- ciated with air pollution are tied to the mortality, chronic bronchitis, and other heart and respiratory diseases resulting from the inhalation of particulate matter, ozone, nitrogen dioxide, carbon monoxide, and ozone in vehicle emissions. Air pollution costs were estimated based on EPA models used to estimate the economic benefits of the Clean Air Act and on other studies of the air pollution costs tied to vehicle emissions (McCubbin and Delucchi 1998). When applying this methodology to vehicle emissions, the authors per- formed sensitivity analysis with respect to the costs associ- ated with premature death. As shown, when including the time, fuel, and maintenance costs associated with congestion Low Congestion $16,352 Crash Costs $120,580 Air Pollution $30,300 Noise $1,214 Total High $181,635 $839,463 $349,100 $11,446 $1,533,344 Mid-Range $61,761 $339,886 $40,443 $4,336 $446,319 $170,246 Source: US DOT 2000. TABLE 12 ESTIMATES FOR SOCIAL COSTS OF MOTOR VEHICLE USE ($ MILLIONS)

37 and mortality, property damage, personal injury, and other costs associated with vehicle crashes, the total social costs of motor vehicle use in 2000 were estimated at between $170 billion and $1.5 trillion (U.S.DOT 2000). If highway-user fees were designed to capture the full costs of highway use, the resulting revenue could be used to make investments (e.g., additional noise walls, improved clean fuel development, better air pollution control pro- grams, development of new technologies, and better crash response teams) that could mitigate major portions of these external costs over time. Although some of the costs associ- ated with these external cost categories are already internal- ized into highway agency budgets (e.g., emergency response costs and variable message signs), most social externalities are not being addressed through public expenditures. Be- cause the remediation of external costs does not generally fall on a state’s DOT, these costs are not allocated under the tra- ditional expenditure-based HCAS approach in many states. Substantial uncertainties exist in the estimation of exter- nal costs, underscoring the need for caution in identifying the implications of including them when setting highway-user charges. However, much can be learned from analyses of non-agency costs of highway use. The analysis of external costs is based on principles of economic efficiency. Ulti- mately, if highway users are required to pay highway-user charges equal to the costs they impose on others, then trips that are valued less than these costs will not be made and overall societal benefits will be maximized. INCLUSION OF INTERNAL COSTS NOT INCLUDED IN AGENCY EXPENDITURES We use the term “internal costs” to include all costs of high- way-related programs and use of highways that show up in public expenditures during any time period. This is to distin- guish such costs from “external” or “social” costs, which are discussed in the previous section. Internal costs can be divided into at least four categories, each of which could be considered in comprehensive HCASs. The most obvious category of internal highway costs is current highway agency budgets and programmed expendi- tures, such as construction, maintenance, operations, and re- lated administrative costs. These are almost always included in HCASs, except that federal expenditures on federal lands and similar expenditures on streets and highways that are not the responsibility of the state highway agency are often excluded from state HCASs. Although these may not be the direct responsibility of state highway agencies, excluding them could result in a less than complete analysis of highway expenditures in the state. The next most closely related category of internal highway costs is state expenditures for highway-related programs that are not the responsibility of the state highway agency, such as the MVA, highway patrol, public transportation operations on streets and highways, crash response, traffic-related court costs, and highway-user fee tax collection and enforcement. Sometimes these expenditures are incurred by agencies in other parts of state DOTs, sometimes in other state agencies, and sometimes in agencies at other levels of government. Nevertheless, their exclusion results in less of a comprehen- sive analysis of expenditures on streets and highways. The third category of internal highway costs are those that can be expected to occur in the future but are not already pro- grammed, such as the costs of deferred maintenance. These are important costs because they are usually going to be sig- nificantly greater than the cost savings from cutting current maintenance program recommendations, so they could be in- cluded in HCASs even if they are not in adopted programs or budgets. In a typical HCAS, approved capital programs cover five to 10 years in the future, but maintenance pro- grams, or at least routine maintenance, are often excluded from anything beyond current budget years. Often in the process of developing proposed future capital programs states will forecast future maintenance program requirements based on projections of future factors such as future lane- miles of highways and future maintenance costs per lane- mile. If deferred maintenance costs are likely to result in a significant increase in future maintenance costs per mile of highway, these costs should be included in an HCAS. The fourth and final category of internal highway costs are those associated with potential expansions of highway sys- tems beyond those included in all of the previous categories. Traditionally, these have been identified in “highway needs studies,” which have typically included such potential future expenditures as upgrading portions of the highway system to include new routes, bypasses, and conversion of older routes to freeway standards. Such potential future expenditures have traditionally never been included in state HCASs, except dur- ing the early years of the development of the Interstate high- way system. If a state wishes to give serious consideration to such a program, HCASs could include them when sufficient planning work has been done to provide both cost estimates and user forecasts. ISSUES IN DEVELOPING RECOMMENDATIONS FOR CHANGES IN STATE TAX STRUCTURE Experience demonstrates that state HCASs seldom if ever re- sult in major changes in the tax structure owing to the impor- tance of changes in tax burden to the stakehoders. As noted in the final paragraph of chapter four: “One issue in planning HCASs that often affect the likelihood of implementation is the stated set of conditions for studies.” Examples were cited of ways in which HCASs have eased the pain of recom- mended changes in tax structure by either ensuring that no major changes in tax rates will occur or that any significant increase in taxes or fees will be done in a less painful way by seeking to reach agreement on quid pro quos.

38 A related approach that is sometimes done to ease pain in making changes in tax structure in related situations is to either stage the changes over a several year period or to just propose incremental changes at the completion of a study and defer further changes until the results of the next study are available. Another way of incrementally improving the equity of the tax structure is to introduce some graduation of fees based on annual mileage in a state as distinct from establishing a tax that varies directly with mileage. Examples include having high- mileage vehicles pay higher weight fees or higher registration fees. A different approach used by a few states is setting a higher diesel tax rate for high-mileage vehicles. A final way of coping with these important practical limi- tations is to propose small changes in tax structure targeted at the most seriously inequitable parts of the tax structure, such as by gradually reforming weight fees for very overweight permit vehicles or vehicles that should be subject to special weight fees (see the section in this chapter covering weight fees and other special fees). This was the approach taken in developing recommendations in the 1990 Vermont HCAS and in a follow-up analysis of special vehicles in 1991.