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