Estimating VMT Mix by Vehicle Type

Method 1: Use MOBILE6 Model Defaults

Description

The MOBILE model requires estimates of a distribution of registered vehicles by age and vehicle category for current and future years. For MOBILE6 new national level vehicle registration data by age and class were developed for July 1, 1996. EPA developed a methodology to convert the July 1, 1996 registration profile into a general registration distribution by age and by vehicle category for some 16 composite (gasoline and diesel) vehicle types. To project future changes EPA evaluated general sales growth and vehicle scrappage trends for the total light-duty vehicle in-use fleet and the total heavy-duty vehicle in-use fleet, and made minor adjustments, where possible, to reflect some of the differences between vehicle categories.

Method Applicability

This method is most applicable in a nonattainment or maintenance area where it is anticipated that the vehicle fleet mix is similar to the national default. This is most applicable in areas that parallel the national socioeconomic statistics. This assessment should include all on-road vehicles in the area including those outside the nonattainment or maintenance area if a considerable portion of vehicles in the on-road fleet come from outside the area.

Data Sources and Procedures

This approach involves using the national default registration distribution that comes with the MOBILE6 model. A review of the national registration data should be made in order to verify the appropriateness of the national default data. This review could look at the most important class of emissions light-duty vehicles and heavy-heavy duty vehicles. Also, an assessment should be made as to the projected trends in sales growth and scrappage trends to determine if the default trends used in MOBILE6 are appropriate.

Advantages

• Uses a readily available, nationally recognized source of data that requires little effort for the user to apply.
• Use of the national average facilities comparisons to other regions using the national averages for the fleet mix distribution.
• The approach is simple to operationalize.

Limitations

• The area's VMT fleet mix may vary significantly from the national default. Thus, the approach may not provide a valid representation of the actual fleet mix.
• The approach does not include local adjustments for changes in local scrappage or sales rates. Localized shifts in these trends may have substantial impact on emissions.

Example Location

This methodology has been applied in Portneuf Valley, Bannock County, Idaho. It was suspected that the higher proportion of SUVs would be found in this county than the national default. A local vehicle count was conducted in the area, which verified that the national defaults were in the appropriate range for this category.

Resources:

Bannock Planning Organization, "FY2004 Draft Transportation Improvement Program Conformity Determination," August 15, 2003.

Estimating VMT Mix by Vehicle Type

Method 2: Use Available Local Data and Maintain Constant Mix for Future Years

Description

The MOBILE model requires estimates of a distribution of registered vehicles by age and vehicle category for current and future years. In this case, local registration and/or local traffic data are used to characterize the vehicle mix for the 16 composite MOBILE6 vehicle classes (or potentially the full 28 MOBILE6 categories), and this mix is assumed to hold constant over future years.

Method Applicability

This approach is most applicable in areas where there are significant differences in the local vehicle fleet mix relative to the national average vehicle fleet mix, and where changes are not anticipated in the future. It is most applicable where the local traffic and/or registration data can be assembled and are representative of the nonattainment or maintenance area. Both traffic survey and registration data at the local level can be used to estimate vehicle fleet mix. However, if only local registration data are used, adjustments may be needed if a significant portion of on-road motor vehicles come from outside the nonattainment or maintenance area.

Estimating VMT Mix by Vehicle Type

Method 2a: Use Local Vehicle Registration Data

Data Sources and Procedures

This approach involves using local vehicle registration data. This is typically available at the county level, but may be possible to acquire at city or smaller scale from the state motor vehicle registrar office. The fleet mix should be representative of the vehicle mix over the small urban or rural area under question. If the pollutants of concern are ozone precursors then the data should reflect the July 1^st date. For CO, the January 1^st date should be used.

Also, an assessment should be made as to the projected trends in sales growth and scrappage trends to determine if the default trends used in MOBILE6 are appropriate when using this local vehicle registration data for baseline fleet composition. The extent to which the growth and scrappage trends diverge from the baseline is an important factor that will affect estimates of future year emission estimates.

Advantages

• Uses locally specific registration data that is likely more representative of the local area than the national default.
• Requires minimal additional resources, particularly if data is readily available at the county or local level from the State department of motor vehicle registration.
• Recommended by EPA and is generally accepted as a viable approach.

Limitations

• Registration data may include vehicles owned, but not operated in the local area.
• Registration data does not differentiate between seasonal usage differences in vehicles. For example, in some locations, light-duty trucks (LDTs) are operated more frequently in the winter months with the need for better traction in winter driving conditions; conversely light-duty vehicles (LDVs, or passenger cars) are used in summer months when driving conditions are less demanding.
• Does not include local adjustments for changes in local scrappage or sales rates. Localized shifts in these trends may have substantial impact on emissions.

Example Location

This methodology has been applied in a number of counties in Pennsylvania. The distributions were developed for July 1^st and reflect the development of the fleet mix for the group of 16 composite MOBILE6 vehicle types. However, Pennsylvania elected not to use the heavy-duty vehicles registration data as they were limited and because much of Pennsylvania's HHDDV traffic is through traffic. Pennsylvania used the MOBILE6 defaults for HHDDV. This approach was also used in Missoula County, Montana with the same mix in future years.

References:

"The 2002 Pennsylvania Statewide Inventory, Using MOBILE6, An Explanation of Methodology," Michael Baker, Jr., Inc., November 2003.

Estimating VMT Mix by Vehicle Type

Method 2b: Use of Traffic Data for Each Vehicle Class

Data Sources and Procedures

This approach involves using county traffic count data by vehicle class. This requires data collection on a representative set of facilities over the small urban or rural area under question. The data collection requires measuring as many of the 28 MOBILE6 vehicle classes as possible. At a minimum the counts should be able to separate out LDGV, LDGT, and HDDV. If the pollutants of concern are ozone precursors then the data should reflect the July 1^st date. For CO, the January 1^st date should be used.

Also, an assessment should be made as to the projected trends in sales growth and scrappage to determine if the default trends used in MOBILE6 are appropriate when using this county traffic data for baseline fleet composition. The extent to which the growth and scrappage trends diverge from the baseline is an important factor that will affect estimates of future year emission estimates.

Advantages

• Uses county traffic count data, which are more representative of the local area than the national default.
• Requires minimal additional resources, particularly if traffic count data by vehicle class are readily available from the State DOT.

Limitations

• The county traffic count data by vehicle type require a moderate level of increased resources to complete.
• It may be difficult to gather more than a handful of vehicle classification data from the county traffic count data.
• The traffic count data should reflected the climate season of concern; fleet mix may change significantly in some locations.
• The approach does not include local adjustments for changes in local scrappage or sales rates. Localized shifts in these trends may have substantial impact on emissions.

Example Location

This methodology was used in Bannock County, Idaho to verify the percentage of LDGT1 and LDGT2 trucks had been properly developed for their region. The results showed that the national defaults were very similar to the local fleet fractions for LDGT1 and LDGT2 vehicles.

References:

Bannock Planning Organization, "FY2004 Draft Transportation Improvement Program Conformity Determination," August 15, 2003.

Estimating VMT Mix by Vehicle Type

Method 2c: Use of a Combination of Traffic Data and Vehicle Registration Data

Data Sources and Procedures

This approach involves using traffic count data by vehicle class in combination with vehicle registration data. This requires data collection on a representative set of facilities over the small urban or rural area under question and ideally the local vehicle registration. The traffic data collection count requires collecting information on vehicle type by roadway functional class. The vehicle registration data are then used to determine the type of fuel use by vehicle type. The vehicle registration data are typically available at the county level, but may be possible to acquire at city or smaller scale from the state motor vehicle registration office. The product of the registration data and traffic count are used to determine the MOBILE6 fleet mix over the small urban or rural area under question. If the pollutants of concern are ozone precursors then the data should reflect the July 1^st date. For CO, the January 1^st date should be used.

Also, an assessment should be made as to the projected trends in sales growth and scrappage to determine if the default trends used in MOBILE6 are appropriate when using this baseline fleet composition. The extent to which the growth and scrappage trends diverge from the baseline is an important factor that will affect estimates of future year emission estimates.

Advantages

• Uses traffic count data, which are likely more representative of the local area than the national default.
• Uses local registration data, which is likely more representative of the local area than the national default.
• Offers an approach to develop an estimate for the full 28 MOBILE6 vehicle classification categories.
• Appealing in estimating fleet mix in the near future as the alternative fueled and new technology (hybrid vehicles - gasoline/electric and diesel/electric) begin to enter the fleet.

Limitations

• The traffic count data by functional class require a moderate level of increased resources to complete.
• The need to acquire the vehicle registration data and compute the product with the traffic count data entails a modest amount of additional resources.
• The traffic count data should reflect the climate season of concern; fleet mix may change significantly in some locations.
• The approach does not include local adjustments for changes in local scrappage or sales rates. Localized shifts in these trends may have substantial impact on emissions

Example Location

This methodology was used in Cheshire County, New Hampshire. VMT mix was estimated by using a combination of vehicle registration data and traffic count data were collected by roadway function class. County registration data was used to estimate fuel use (gasoline, diesel) by vehicle type and the cross product used to estimate the sixteen MOBILE6 vehicle mix categories. Development of a local fleet mix was identified as an important factor in helping the region meet conformity.

References:

New Hampshire Department of Transportation, "Procedure to Determine VMT Percentages by Vehicle Type in New Hampshire", August 2, 2002.

Estimating VMT Mix by Vehicle Type

Method 3: Use Available Local Data for Base Year Fleet Mix and Iteratively Adjust To Reflect Expected Changes in Mix

Description

The MOBILE model requires estimates of a distribution of registered vehicles by age and vehicle category for current and future years. In this case, local registration and/or local traffic data are used to characterize the vehicle mix for the 16 composite MOBILE6 vehicle classes (or potentially the full 28 MOBILE6 categories). The estimates are then iteratively adjusted for each analysis year in proportion to changes assumed in the MOBILE default values.

Method Applicability

This approach is most applicable in areas where important differences are known relative to the national average vehicle fleet mix used in MOBILE6. It is applicable where the local traffic data in conjunction with vehicle type from the HPMS reporting system can be assembled and is representative of the nonattainment or maintenance area under study. Ideally, traffic survey count information classified by vehicle type at the local level can be used to estimate on-road vehicle fleet mix for the MOBILE6 model. Caution is advised in mapping the HPMS vehicle type information to the MOBILE6 model as the two classification schemes are distinctly different.

Data Sources and Procedures

This approach involves using local traffic count data by vehicle class. This requires data collection on a representative set of facilities over the small urban or rural area under question. The data collection requires using historical HPMS data for the six or more vehicle classification counts and then translating to the 16 MOBILE6 composite vehicle classes. These vehicle classification counts from HPMS are used in conjunction with the default MOBILE6 vehicle mix by iteratively adjusting the distributions so that the final fleet mix reflect the change in vehicle mix for each year. At a minimum the vehicle classification counts should be able to separate out LDGV, LDGT and HDDV. If the pollutants of concern are ozone precursors then the data should reflect the July 1^st date. For CO, the January 1^st date should be used.

Also, an assessment should be made as to the projected trends in sales growth and scrappage trends to determine if the default trends used in MOBILE6 for future years are appropriate when using this local traffic data for baseline fleet composition.

Advantages

• Uses traffic data classification counts, which are likely more representative of the area than the national default.
• Uses only a modest additional resource requirement by using historical HPMS data; particularly if representative traffic data vehicle classification counts are readily available from the State DOT.

Limitations

• The traffic count data by vehicle type require a moderate level of additional resources to complete.
• It may be difficult to gather more than a handful of vehicle classification data from the HPMS traffic classification count data.
• The traffic count data should reflect the climate season of concern; fleet mix may change significantly in some locations.
• The approach does not include local adjustments for changes in local scrappage or sales rates projected for future years. Localized shifts in these trends may have substantial impact on emissions.

Example Location

This methodology was used across North Carolina for six urban and six rural road types. It was used primarily for adjusting the vehicle classification mix to reflect the change in fleet mix for higher light-duty truck fraction than the national average using recent historical HPMS data.

Reference:

Phone conversation with Behshad Norowzi, North Carolina DOT, bnorowzi@dot.state.nc.us), February 17, 2004.