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Sample Methodologies for Regional Emissions Analysis in Small Urban and Rural Areas

2 VMT Estimation and Forecasting Examples

2.1 Background - Importance of VMT Estimates

The basic process for calculating emissions involves multiplying VMT by a per-mile emission factor. Thus, accurate VMT forecasts are extremely important for developing emissions estimates in the conformity analysis. This section focuses on the methodologies and approaches used for estimating baseline VMT and forecasting future VMT.

Clearly, any methodology to forecast future VMT requires an accurate estimate of current VMT (and often historic VMT and socio-economic factors as well). Data from the Highway Performance Monitoring System (HPMS) are typically used in small urban and rural areas to estimate VMT for the current year.[4] However, the accuracy of HPMS-based estimates may be limited in small urban and rural areas and for local roadways in particular (as opposed to arterials and other higher functional classifications), given the sparse sample sizes at the county level. As a result, some areas have developed detailed inventories of local road mileage and supplemented the HPMS sample with additional traffic counts, and some have developed detailed traffic monitoring systems in order to develop more accurate estimates of VMT at the county level.

A basic process for estimating VMT using a sample of traffic count data for use in emissions analysis is as follows:[5]

  1. Calculate the sum of counts in each facility type
  2. Determine the sample size in each facility type (i.e., the number of count sites)
  3. Determine the average volume for a facility type by dividing total count by sample size
  4. Obtain total centerline miles of each facility type in the modeling domain
  5. Multiply average volume by the number of centerline miles for each facility type to estimate total VMT for each facility type.

VMT estimates are used together with per mile emissions factors developed using the EPA MOBILE6 model (or EMFAC in California), which in turn, are dependent on speed estimates. As a result, the level of detail in the estimation of VMT will influence the level of detail that can be used in the estimation of speeds, and will ultimately affect the regional emissions estimates.

VMT estimates are typically developed on a daily basis, for multi-hour time periods, or by hour:

1) Average daily VMT - The simplest approach is to develop estimates of average daily VMT by functional roadway classification. Areas with TDF models use modeled volumes by roadway segment. Areas that do not have a TDF model usually rely on HPMS estimates of VMT by functional roadway classification.

2) VMT for different periods of the day - This approach involves developing estimates of VMT for different periods of the day (e.g., AM peak, PM peak, off-peak). This approach is commonly used in areas where there is significant traffic congestion during peak hours. In areas with a TDF model, the model usually can be run to estimate VMT for the morning and evening peak periods, and for total average daily traffic, in which case, the peak periods can be subtracted from the daily total in order to estimate off-peak VMT. Otherwise, average daily traffic estimates can be disaggregated to time periods using locally-developed factors. In areas without a TDF model, average daily traffic is often distributed between peak periods and off-peak periods based on local factors developed from traffic counts.

3) VMT by hour of day - The most detailed breakdown of VMT can be developed by subdividing estimates of daily VMT or VMT by time period to generate hourly VMT. This hourly breakdown is often based on estimates of hourly volumes from a sample of roadways and average roadway speeds each hour. These sample results provide an estimate of VMT for each of 24 hours in the day. This step allows for a more detailed speed analysis in MOBILE6, which allows VMT to be distributed by hour of the day and by speed category or "bin."

In many small urban and rural areas, a simple analysis of average daily traffic will suffice for the regional emissions analysis. However, it is important to recognize cases where a more detailed breakdown is useful to reflect local conditions that could significantly influence emissions.

Moreover, although many areas use annual average daily VMT (based on estimates of annual average daily traffic, or AADT, on roadways), a seasonal adjustment is sometimes applied so the resulting VMT used in the conformity analysis reflects either an average summer or winter weekday, depending on the pollutant of concern (summer for ozone, winter for CO). This seasonal adjustment is most important in areas with large seasonal variations in traffic patterns, and is more often applied in areas that have regional TDF models.

2.2 MOBILE6 Requirements for VMT

MOBILE6 differs from previous versions of the MOBILE emissions model in that it produces different emission factors for different roadway facility types. The four facility types are:

Using the VMT BY FACILITY command, the user can input the fraction of VMT that occurs on each facility type. The user can run the model assuming 100 percent for a specific facility type in order to develop facility-specific emission factors, or can input a fractional value for each facility in order to develop a composite emissions factor across all road types.

As noted above, MOBILE6 allows users to input VMT information at different levels of detail, depending on the availability of local data. MOBILE6 allows users to specify the distribution of VMT by hour of the day, by speed, and by vehicle type.[6] Using the VMT BY HOUR command in MOBILE6, the user can input the fraction of VMT that occurs at each hour of the day (24 fractional values). The 24 fractions should sum to 1. If this command or other MOBILE6 commands that allow specification of VMT by hour are not used, then MOBILE6 will use national default data for the distribution of VMT by hour.

2.3 Methodologies for Estimating Local Road VMT

These methodologies can be used both in areas with or without a TDF model, since TDF models generally do not include road links for local roadways.

For purposes of emissions modeling, note that the assignment of VMT as "local road" VMT may not match with the standard highway classifications of urban local roads and rural local roads. In MOBILE6, local roads are defined as facilities having extremely low average speeds and frequent stops at intersections. They generally represent roads in residential areas and are characterized by having no traffic lights, no more than one lane in each direction, vehicle parking on the street, and traffic control handled via stop/yield signs. MOBILE6 assumes an average speed of 12.9 miles per hour for local roads, which cannot be changed by the user. As a result, roadways that fit within FHWA's "rural local roadways" and "urban local roadways" functional classifications with higher average speeds should be considered arterials/ collectors in MOBILE6. Rural local roadways as defined by FHWA will generally not fit the MOBILE6 definition of local roadways, and many urban local roads will also not fit the definition. Given these differences in definitions, it is important for the analyst to develop an accurate estimate of total VMT in the nonattainment or maintenance area, and to pay special attention to classifying the VMT appropriately into the MOBILE6 road classifications.[7]

Estimating Local Road VMT

Method 1: Use Statewide Estimates to Calculate Proportion of Local Road VMT to Collector VMT

Scale of 1-5(lowest to highest) - Availability of Data:5 ; Ease of Application:5 ; Technical Robustness:2 ; Policy Sensitivity:1

Description
State-level data on VMT on local roads and collectors is used in order to develop a ratio of local road VMT to collector VMT on urban and rural functional road classifications. This ratio is then applied to county-level estimates of VMT on collectors.
Method Applicability
This method is most appropriate when the region being examined is expected to have relatively similar patterns as the State as a whole. This method is broadly applicable to virtually all small urban and rural areas with limited data on local roadway VMT.
Data Sources and Procedures

VMT Estimation and Forecasting

VMT estimates for local roadways in a particular county are developed by multiplying the HPMS estimates of VMT on collectors in the county by the ratio of local roadway VMT to collector VMT developed using HPMS data at the statewide level (see equation below).

CountyVMT<sub>local</sub> = CountyVMT<sub>collector</sub> x StateVMT<sub>local</sub>/StateVMT<sub>collector</sub>

The equation is applied separately for rural local roads, using the proportion of statewide VMT on rural local roads to rural collectors, and for urban local roads, using the proportion of statewide VMT on urban local roads to urban collectors. The estimates of VMT are believed to be more accurate at the statewide level than at the county level, given the limited HPMS sample size at the county level. The same procedure can also be applied using ratios developed at a smaller geographic level than the state (for example, a set of counties within a large state), if sufficient data are available.

This procedure may be used to develop a base year estimate of VMT on local roadways or to forecast VMT on local roadways given a forecast of VMT on collectors.

Advantages
  • Simplicity of the approach.
  • Resource requirements are very small.
  • Rationale and data sources are generally accepted.
Limitations
  • Methodology may not reflect differences in patterns between the county under consideration and the state (for example, if there is a much smaller local road network proportional to collectors).
Example Location

This approach had been used in Kentucky for rural areas and small urban areas (however, this approach is not currently used). In its original approach, the Kentucky Transportation Cabinet (KYTC) examined the statewide ratio of VMT on local roads and collectors, and used the following ratios to predict local road travel in each county: 0.33 (rural local/rural collector), 0.28 (urban local/urban collector in urbanized counties), 0.12 (urban local/urban collector in non-urbanized counties).

Website: http://transportation.ky.gov/Multimodal/Air_Quality.asp

References:

M.L. Barrett, R.C. Graves, D.L. Allen, J.G. Pigman, G. Abu-Lebdeh, L. Aultman-Hall, S.T. Bowling, Analysis of Traffic Growth Rates, University of Kentucky Transportation Center, August 2001.

Bostrom, Rob and Jesse Mayes, "Highway Speed Estimation for MOBILE6 in Kentucky," Kentucky Transportation Cabinet, 2002.

Excel Spreadsheet tables, "2001-2030 VMT Tables," supplied by Jesse Mayes, Kentucky Transportation Cabinet.

Estimating Local Road VMT

Method 2: Develop Statistical Relationship between Local Road VMT and Collector VMT

Scale of 1-5(lowest to highest) - Availability of Data:2 ; Ease of Application:3 ; Technical Robustness:3 ; Policy Sensitivity:1

This methodology is similar to Method #1, but uses several data points in order to develop a formula relating local road VMT to collector VMT. It relies on several samples of local and collector VMT (e.g., county-level estimates, for various years).

Method Applicability
This method is applicable to virtually all small urban and rural areas with limited data on local roadway VMT for the nonattainment or maintenance area. It requires, however, sufficient data on local and collector VMT from several samples of counties in order to conduct the statistical analysis.
Data Sources and Procedures

VMT Estimation and Forecasting

An analysis is conducted using available state-level HPMS estimates or county-level samples in order to relate VMT for local roads with VMT for collectors. The analysis can be conducted by testing various equations to describe the relationship between local road and collector road volumes and selecting the best fitting equation. For example, a typical spreadsheet package can test the following:

Simple linear equation[8]: LocalADT = a x CollectorADT = b

Logarithmic equation: LocalADT = a x ln(CollectorADT) + b

Exponential equation:nbsp; LocalADT = a x e<sup>b x CollectorADT</sup>

Power equation:nbsp; LocalADT = a x CollectorADT<sup>b</sup>

In all cases, a and b are constants that are determined based on the relationship of existing local and collector VMT data.

The formula that is developed from this analysis can then be used to develop an estimate of county-level VMT on local roads given an estimate of county-level VMT on collectors. The formula can be used both for the baseline analysis and projections.

Advantages
  • Relative simplicity of the approach.
  • Rationale and data sources are generally accepted.
Limitations
  • Requires several estimates of local and collector VMT at a county level, drawn from more detailed sampling and traffic counts on roadways.
  • Methodology may not reflect conditions that are particular to the county under consideration that may make the relationship between different functional roadway class traffic volumes different from other parts of the state (for example, if there is a much smaller local road network proportional to collectors).
  • Methodology may not substantially improve upon estimates using a simple ratio of local road VMT to collector VMT.
Example Location

The approach has been used by the Kentucky Transportation Cabinet (KYTC) for small urban and rural areas in order to improve estimates of local road VMT previously developed using Method #1. KYTC used GPS technology to develop accurate mileage data fro local roadways statewide. Reasonably good ADT data at the county level were available from HPMS down to the collector functional class.

Research conducted by the University of Kentucky Transportation Center (see first reference below) found that a simple ratio of local road to collector VMT was inadequate to predict local road VMT. Researchers graphed local ADT against collector ADT to develop the best fitting relationship between these two measures. For Kentucky, they arrived at the following equation:

Local ADT = 3.3439 x (Collector ADT)0.6248

KYTC selected to use this equation rather than a simple ratio since they found that local roads carry much less traffic relative to collectors in locations where collectors have higher VMT. Therefore, if a simple ratio had been used as in method #1, then local road VMT would have been overestimated where collector VMT was relatively high.

Website: http://transportation.ky.gov/Multimodal/Air_Quality.asp

References:

M.L. Barrett, R.C. Graves, D.L. Allen, J.G. Pigman, G. Abu-Lebdeh, L. Aultman-Hall, S.T. Bowling, Analysis of Traffic Growth Rates, University of Kentucky Transportation Center, August 2001.

Bostrom, Rob and Jesse Mayes, "Highway Speed Estimation for MOBILE6 in Kentucky," Kentucky Transportation Cabinet, 2002.

Excel Spreadsheet tables, "2001-2030 VMT Tables," supplied by Jesse Mayes, Kentucky Transportation Cabinet.

Estimating Local Road VMT

Method 3: Estimate Average Daily Traffic on Inventory of Local Roadways

Not Applicable (only rated for forecasting methodologies)

Scale of 1-5(lowest to highest) - Availability of Data:1.5 ; Ease of Application:4 ; Technical Robustness:4 ; Policy Sensitivity:n/a

* Note: This method is used solely to develop an estimate of baseline VMT for local roadways, with or without a TDF model. Projections can be made using any of the methods described in Sections 2.4 or 2.5, depending on whether a TDF model is available; a very simple approach is to estimate and apply a growth factor.

Description
This approach involves developing an inventory of all local roadways in the county of interest. Data on average daily traffic (ADT) on individual road links, based on traffic counts, are then applied to the roadway mileage. For links where there are no data, the ADT from other roadways or a default ADT assumption can be applied.
Method Applicability
This method is broadly applicable to virtually all small urban and rural areas. It is most appropriate when the region being examined is expected to have different patterns from the state as a whole and if local roadways make up a disproportionately large share of total traffic.
Data Sources and Procedures

VMT Estimation

A detailed inventory of total road mileage on local roadways is required. Traffic counts are conducted in order to develop estimates of average daily traffic (ADT) on a sample of local roadways in the area of interest. The ADT estimates are then applied to the appropriate road links or assumed to apply to other nearby links. Alternatively, an overall average ADT can be applied across all road mileage. VMT is estimated by multiplying ADT by the link length.

Advantages
  • Accounts for actual road mileage of local roadways in the area of interest.
  • Use of traffic counts provides better indication of actual traffic levels in the county of interest as opposed to using statewide data.
  • Level of data collection can vary, and should depend on whether there is great variation in traffic volumes on local roadways.
Limitations
  • Methodology requires additional data collection compared to those that rely on statewide data.
  • Assumptions of ADT may not be accurate for all roadways.
  • Additional resources and complexity are introduced in the analysis if local road links are examined at a detailed level.
Example Location

North Carolina DOT (NCDOT) applied this methodology in conducting analyses of regional emissions in donut areas outside of MPO boundaries. NCDOT records AADT for all roads in all functional classifications. Since less than 74 percent of local road mileage was covered by actual counts, for local road links without traffic counts, NCDOT assumed 400 ADT. This is the maximum amount of traffic expected on low-volume local roads.[9]

The Rogue Valley MPO in Medford-Ashland (Klamath County), Oregon, used an assumption of 20 ADT on unpaved roads in the base year as part of its conformity analysis for the 2004-2007 Transportation Improvement Program (TIP). The ADT average was developed by the Oregon Department of Transportation's Transportation Planning and Analysis Unit. The MPO assumed a growth rate of 1.2 percent per year for future projections.

References:

North Carolina DOT, Davidson County, and the North Carolina Department of Environment, "Emissions Analysis Report for the Transportation Plan for the Rural Portion of Davidson County." September 27, 2002.

Rogue Valley MPO, "2004-2007 Transportation Improvement Program and

Air Quality Conformity Determination," August 26, 2003.

2.4 Methodologies for Forecasting VMT without a TDF Model

It should be noted that although these methodologies all rate relatively low in terms of policy sensitivity (their ability to respond to changes in highway investments, transit investments, or other policies), separate analyses can be conducted in order to evaluate the effects of new transportation investments, including highway facilities, transit services, park and ride lots, and other transportation control measures. Small urban and rural areas often conduct special analyses of these types of investments to assess the effect to which they might be expected to bring in additional "through" traffic, change the routes that drivers take, or shift drivers from motor vehicles to transit or higher occupancy modes. The effects of these investments on VMT is then added to or subtracted from the totals resulting from the general VMT projection methods.

Forecasting VMT without a TDF Model

Method 1: Linear Projection of VMT based on Estimated Growth Factor

Scale of 1-5(lowest to highest) - Availability of Data:5 ; Ease of Application:5 ; Technical Robustness:1 ; Policy Sensitivity:1

Description
Total VMT is projected to the future based on an estimated growth rate developed by planners. This growth rate may reflect historical growth, expectations for future growth using demographic or economic projections, or other factors as appropriate.
Method Applicability
This method is broadly applicable to virtually all areas without TDF models. It is most appropriate when there are extremely limited resources for forecasting VMT, or when future growth rates are not expected to follow historical patterns.
Data Sources and Procedures

VMT Projection

VMT projections are developed by applying an estimated VMT growth rate to a base-year estimate of VMT, developed from traffic counts and data on roadway extent. Regional planners develop the VMT growth rate based on historical information or expectations for future growth using demographic or other projections. Projected VMT is then apportioned to the functional classes in the same ratio as the most recent year of VMT data.

Advantages
  • Simplicity of the approach.
  • Resource requirements are very small.
Limitations
  • Methodology may not reflect future changes in factors that will influence VMT growth, such as population growth, economic growth, land use changes, and major new developments.
  • Methodology does not reflect potential differences in travel growth rates on different types of roadways.
  • Methodology is not sensitive to expected changes in transportation investments or policies. Any additional traffic growth associated with new facilities will need to be analyzed separately. Upgrades of facilities to higher classifications will not be reflected.
Example Location

Colorado DOT used this approach in one conformity analysis conducted in Aspen, a rural nonattainment area for PM-10. The VMT forecast was based on a baseline estimate of VMT for 1990 from vehicle counts, projected to future years based on an estimated growth rate of 2 percent per year. This overall growth rate was estimated by City of Aspen planners based on experience with recent trends and anticipated growth patterns.

This approach can also be applied for a particular type of roadway (i.e., local roadways, unpaved roads) when a model does not address the roadway class. For instance, the Rogue Valley MPO in Medford-Ashland (Klamath County), Oregon, assumed a growth rate of 1.2 percent per year for future projections of VMT on unpaved roads as part of its conformity analysis for the 2004-2007 Transportation Improvement Program (TIP).

Website:

http://www.dot.state.co.us/environmental/CulturalResources/AirQuality.asp

References:

Colorado DOT, "Colorado State Implementation Plan for PM-10, Aspen Element." Revised September 22, 1994.

Colorado DOT, "Air Quality Analysis, A Technical Report to the State Highway 82 Entrance to Aspen Environmental Impact Statement," July 7, 1995.

Rogue Valley MPO, "2004-2007 Transportation Improvement Program and

Air Quality Conformity Determination," August 26, 2003.

Forecasting VMT without a TDF Model

Method 2: Linear Projection of Total VMT, based on Regression Analysis, Apportioned by Functional Class

Scale of 1-5(lowest to highest) - Availability of Data:4.5 ; Ease of Application:5 ; Technical Robustness:1 ; Policy Sensitivity:1

Description
This methodology uses a simple linear regression in order to forecast future total VMT for a jurisdiction, and then apportions the VMT to functional classes in the same ratio as the most recent year of VMT data. It is a simple method to project VMT using manual calculation procedures.
Method Applicability
This method is applicable to any area without a TDF model. It is most appropriate for an area that is expected to maintain a stable rate of growth in population, economic activity, and vehicle travel. It may be useful (at least initially) for a new nonattainment area that has limited experience with regional emissions analysis.
Data Sources and Procedures

VMT Projection

VMT projections are developed on a county basis based on the historical trend line (an ordinary least squares linear regression extrapolation of the latest ten years of data). The statistical analysis uses total VMT in order to avoid issues associated with reclassification of VMT over time due to the expansion of urbanized boundaries and other functional class shifts. Projected VMT is then apportioned to the functional classes in the same ratio as the most recent year of VMT data.

Advantages
  • Relative simplicity of the approach.
  • Resource requirements likely to be small.
  • Rationale and data sources are generally accepted.
Limitations
  • Methodology does not reflect factors that will influence future VMT growth, such as population growth, economic growth, land use changes, and major new developments. However, such items could be included in the regression analysis as an improvement to the existing methodology. As described above, this method will not be very accurate for an area that is expecting a change in growth rate (either more rapid or slower) from the historical rate.
  • Methodology is not sensitive to expected changes in transportation investments or policies. Any additional traffic growth associated with new facilities will need to be analyzed separately. Upgrades of facilities to higher classifications will not be reflected.
  • If applied in a donut area, methodology may not be consistent with the VMT estimation and projection techniques used in the metropolitan portion of the nonattainment and maintenance area. As a result, coordination with the MPO to insure consistency would be needed.
Example Location

The approach has been used by the North Carolina DOT for the donut areas in North Carolina where a metropolitan area's travel demand model includes the metropolitan planning area only and not the balance of the nonattainment or maintenance area.

Web sites:

http://www.ptcog.org/emissions.pdf

http://tocfs2.ci.high-point.nc.us/HPMPO/plans/LRTP04/Appendix_B_-_Conformity_Analysis_Report_Executive_Summary/High_Point_2030_LRTP_Conformity_Analysis_Report.pdf

References:

North Carolina DOT, Davidson County, and the North Carolina Department of Environment, "Emissions Analysis Report for the Transportation Plan for Rural Portion of Davidson County," September 27, 2002.

Forecasting VMT without a TDF Model

Method 3: Linear Projections of VMT by Functional Class, with Adjustments to Correct for Changes in Functional Class Categories

Scale of 1-5(lowest to highest) - Availability of Data:4 ; Ease of Application:4 ; Technical Robustness:2.5 ; Policy Sensitivity:1

Description
This methodology uses separate simple linear regressions in order to forecast future VMT for each roadway functional classification. In order to account for changes in road classifications over time, minor changes are "smoothed" by adjusting the VMT on a particular functional class for each year in proportion to any changes made in functional class mileage.
Method Applicability
This method is applicable to any area without a TDF model. It is most appropriate for an area that is expected to maintain a stable rate of growth in population, economic activity, and vehicle travel. It may be useful (at least initially) for a new nonattainment area that has limited experience with regional emissions analysis.
Data Sources and Procedures

VMT Projection

VMT forecasts are developed based on a linear regression for each functional class of roadway. However, in order to use historic data to conduct a linear regression by functional class, adjustments need to be made to correct for minor changes in functional class categories (associated with changes due to system upgrades).

Minor changes are "smoothed" by adjusting the historic annual VMT for a particular functional class in proportion to any subsequent changes made in functional class mileage (do to roadway upgrades). Per the equation below, this is done by multiplying VMT for each year by the ratio of mileage in the functional class in the current year to mileage in the VMT estimate year (for example, if current mileage on urban arterials is 105% of mileage in a historic year, due to system upgrades, VMT on urban arterials in the historic year will be multiplied by 1.05 in order to get an adjusted VMT estimate).

For each roadway functional class, for each historic year:

VMT adjusted-historic-year = VMT historic-year x FunctionalClassMiles current-year over FunctionalClassMiles historic-year

This effectively adjusts the older VMT for a given functional class to account for roadways that have since been shifted into that functional class. Per the equation below, functional class VMT totals must then be adjusted to ensure that total VMT for each year does not change as a result of these adjustments. The VMT sum for each year is calculated, and the ratio of the original VMT sum to the new VMT sum is multiplied by the adjusted VMT value for each functional class.

For each roadway functional class, for each historic year:

VMT corrected-historic-year  = VMT adjusted-historic-year x VMT(All - Roadways) unadjusted-historic-year/VMT(All - Roadways) adjusted-historic-year

To avoid problems caused by larger discontinuities in historic trends by functional class (for example, due to changes in the definition of a functional classification at a particular time), linear regressions are conducted in a manner so they do not span such discontinuities. In other words, if a major jump takes place in 1990, the regression may disregard all data prior to 1990.

The procedures discussed above may be conducted at the statewide level in order to develop projected growth rates for each functional class that can then be applied at the local level.

Advantages
  • Accounts for differences in growth rates on different types of roadways.
  • Accounts for historical changes in road network, and can adjust for concerns about local links with sparse data.
  • Rationale and data sources are generally accepted.
Limitations
  • Methodology does not explicitly account for factors that will influence future VMT growth, such as population growth, economic growth, land use changes, and major new developments. As a result, it will not be very accurate for an area that is expecting a significant change in growth rate (either more rapid or slower) from the historical rate.
  • Methodology is not sensitive to expected changes in transportation investments or policies. Any additional traffic growth associated with new facilities will need to be analyzed separately. Future upgrades of facilities to higher classifications will not be reflected.
  • If a donut area, may not be consistent with the VMT estimation and projection techniques used in the metropolitan portion of the nonattainment or maintenance area. This work is done by the MPO and coordination on consistency would be important.
Example Location

Ohio Department of Transportation (DOT) uses TDF models for many small urban areas. Where there is no TDF model, Ohio DOT has used this procedure to forecast VMT. In this case, VMT estimates were only believed to be accurate at the statewide level, not the local level. As a result, the procedures for estimating future VMT growth rates were conducted at the statewide level for all functional classes in order to maintain consistency. The statewide growth rates were then applied to estimates of VMT by functional class for the area being analyzed.

Website: http://www.dot.state.oh.us/urban/index.htm

(See VMT forecasting procedures described under "documents" section:

http://www.dot.state.oh.us/urban/data/vmt.doc)

Forecasting VMT without a TDF Model

Method 4: Linear Projection of Interstate VMT and Population-based Forecast of Non-Interstate VMT

Scale of 1-5(lowest to highest) - Availability of Data:3 ; Ease of Application:4 ; Technical Robustness:3 ; Policy Sensitivity:2

Description
This methodology separates out non-Interstate and Interstate VMT, since non-Interstate VMT typically relates closely to population, while the Interstate traffic in rural and small urban areas involves predominantly through-traffic and is not closely correlated with local population growth. Interstate VMT is estimated based on historical trend line, while non-Interstate VMT is estimated based on a regression to predict non-Interstate VMT per capita, which is applied to projected population.
Method Applicability
This method is applicable to small urban and rural areas, where Interstate highways make up a substantial proportion of VMT, and where population growth patterns may not reflect historical trends.
Data Sources and Procedures

VMT Projection

Interstate VMT is projected using linear regression based on historic traffic volumes.

Non-Interstate VMT is calculated by multiplying projected population by projected non-Interstate VMT per capita. Projected population can be taken from the MPO or state agency responsible for population projections. Non-Interstate VMT per capita is forecast based on a linear regression using historic estimates of VMT per capita for non-Interstate travel at the county level. This forecast recognizes that the amount of daily travel per person has increased historically and is likely to continue to increase. The resulting estimate of non-Interstate VMT is then apportioned to the functional classes in the same ratio as in the most recent year of data (also see Method #5 below for an alternative methodology for estimating non-interstate VMT).

Advantages
  • Relatively simple approach yet accounts for most important roadway classification issues.
  • Use of per capita VMT provides better sensitivity to key factors that affect non-Interstate travel then methods that simply use historical VMT as the independent variable.
  • Resource requirements likely to be small.
  • Rationale and data sources are generally accepted.
Limitations
  • Methodology does not fully reflect factors that will influence future non-Interstate VMT growth.
  • Methodology is not sensitive to factors affecting Interstate VMT growth rate.
  • Methodology is only somewhat sensitive to expected changes in transportation investments or policies.
  • For the local links without traffic counts, assumptions about traffic levels need to be made, and these assumptions should be documented and reasonableness reviewed each time a new conformity determination is made.
Example Location

The approach has been used by the South Carolina Department of Transportation in Cherokee County.

References:

Gardner, John, "Vehicle Miles of Travel Projections and Speed Estimates for Rural Nonattainment and Maintenance Areas," South Carolina Department of Transportation.

Forecasting VMT without a TDF Model

Method 5: Corridor-based Analysis of Interstate VMT, Population-based Forecast for Non-Interstate VMT

Scale of 1-5(lowest to highest) - Availability of Data:4 ; Ease of Application:3 ; Technical Robustness:3 ; Policy Sensitivity:2

Description
This method is similar to methodology #4, but uses professional judgment and a corridor-by-corridor analysis of historic growth and anticipated growth in each corridor in order to estimate the growth rate for Interstate VMT rather than relying solely on linear projection of historic data. It also utilizes a slightly different approach for estimating non-Interstate VMT, relying heavily on statewide VMT data rather than county-level data.
Method Applicability
This method is applicable to small urban and rural areas, where Interstate highways make up a substantial proportion of VMT, and where historical growth in Interstate VMT may overestimate future growth (this may be the case if historic growth has been especially rapid, and limited highway capacity constrains the level of future growth). It also is most applicable in locations where population growth patterns may not reflect historical trends.
Data Sources and Procedures

VMT Projection

Interstate VMT is projected using data on historic trends, but is assumed to decline somewhat based on limitation in highway capacity or other factors. A corridor-by-corridor analysis is conducted of historic VMT growth in order to develop an initial annual growth rate. An adjusted annual growth rate is then developed for purpose of projections based on professional understanding of the anticipated pace of traffic growth in each corridor.

An estimate of non-Interstate VMT is developed using an approach similar to the one described in methodology #4, which relies on projections of population and per capita VMT. However, in this case, statewide growth in non-Interstate VMT is estimated using statewide VMT data from HPMS and state population estimates. First, a linear regression is developed to predict statewide non-Interstate VMT as a function of population, based on historic data, as follows:

NonInterstateVMT = a x Population + b

The base year non-Interstate VMT is then subtracted from the future year projection to calculate the projected growth in non-Interstate VMT. This statewide VMT growth is then allocated to the counties based on a combination of county population change and a projected increase in per capita VMT, as described below:

  1. First, projected VMT per person is calculated for the analysis year by dividing the non-Interstate VMT calculated in the regression equation by the population projection in the analysis year.
  2. The resulting estimate of VMT per capita is then multiplied by the base-year county population estimate in order to estimate future-year VMT associated with the existing population for the analysis year.
  3. The county-level VMT estimates are then summed for the state to obtain the estimated statewide VMT associated with the existing population.
  4. The difference between the resulting statewide VMT total (representing VMT associated with the base year population) and the forecasted total (from the regression analysis) is then calculated to obtain the estimated VMT due to population growth.
  5. The VMT associated with population growth is then allocated to the county level based on each county's proportion of statewide population change between the base year and the forecast year. For example, if a county is responsible for 5% of the estimated population growth, then 5% of the VMT associated with population growth would be allocated to the county.

The resulting estimate of county-level non-Interstate VMT is then allocated to each functional class in the same proportion as in the HPMS baseline year.

Advantages
  • Relatively simple approach yet accounts for most important roadway classification issues.
  • Use of per capita VMT provides better sensitivity to key factors that affect non-Interstate travel then methods that simply use historical VMT as the independent variable.
  • Use of statewide data helps to avoid potential inaccuracies associated with county-level VMT estimates.
  • Resource requirements are moderate.
Limitations
  • Methodology does not fully reflect factors that will influence future non-Interstate VMT growth.
  • Use of estimated or assumed growth rates can introduce bias.
  • Methodology is only somewhat sensitive to expected changes in transportation investments or policies.
Example Location

The approach has been used by the Kentucky Transportation Cabinet (KYTC) in locations where there is not a TDF model.

References:

M.L. Barrett, R.C. Graves, D.L. Allen, J.G. Pigman, G. Abu-Lebdeh, L. Aultman-Hall, S.T. Bowling, Analysis of Traffic Growth Rates, University of Kentucky Transportation Center, August 2001.

Forecasting VMT without a TDF Model

Method 6: Separate Forecasts by Functional Class based on VMT, Population, and Employment, with Growth Factor employing a Decay Function

Scale of 1-5(lowest to highest) - Availability of Data:2 ; Ease of Application:2 ; Technical Robustness:3.5 ; Policy Sensitivity:2.5

Description
This methodology involves developing separate forecasts of VMT by functional class. A unique aspect of this method is that it takes into account employment as a factor that influences VMT, and does not use a linear regression function. It employs a decay factor based on an assumption that future traffic growth will slow in the future compared to historic rates of growth. Estimates are adjusted to reflect current year HPMS VMT estimates.
Method Applicability
This method is applicable to small urban and rural areas without a TDF model. It is particularly useful where there are significant differences between travel characteristics by road classification, and when there is empirical evidence of a declining trend in VMT growth.
Data Sources and Procedures

VMT Projection

VMT forecasts for each county and functional class are based on traffic data and growth factors that reflect historic correlations between VMT and population and employment for each county and functional class. The growth factor employs a decay function assuming that VMT growth will taper off. Estimates are adjusted to reflect current year HPMS VMT estimates.

Advantages
  • Methodology accounts for additional factors that influence VMT growth.
  • Approach accounts for differences in VMT growth rates on different roadway functional classifications.
Limitations
  • Use of estimated or assumed growth rates (decay function) can introduce bias.
  • Resource and data requirements are the highest among the alternatives examined for areas without a TDF model.
Example Location

The approach has been used by the Pennsylvania Department of Transportation for all areas where there is not a TDF model.

Reference:

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

2.5 Methodologies for Forecasting VMT with a TDF Model

Areas that maintain a TDF model generally use the model outputs to estimate VMT. There are a variety of commercially available TDF model software packages in use, including TranPlan, TransCAD, TP+, Viper, MINUTP, EMME/2, and QRS2. Software is often supplemented by custom sub-routines not integrated into the package. The scope of these models also differs - some areas have urban area models, some county-level models, and a few have statewide models (which may not use commercial software) that provide county-level data. At least one State reported using both urban area models (for the MPO area) and a statewide model (for the donut areas outside of MPO and urban model boundaries).

TDF models offer greater sensitivity to changes in transportation investments or policies, compared to most manual calculation procedures. New facilities and improvements to existing facilities can be coded into the network. In estimating future VMT, the TDF model takes into account all of these improvements at once, predicting the most likely distribution of traffic on the future network. In contrast, most off-model calculation procedures cannot consider how all improvements together would affect traffic distribution across the network.

Adjustments to TDF model outputs are often required in order to make the results suitable for conformity analysis. Adjustments to the model outputs generally fall into three categories:

  1. Adjustments to TDF model outputs to ensure that VMT results are appropriate for use in comparison with the emissions budget in a SIP (see section 2.5.1);
  2. Methods to account for lower functional classification roadways (i.e., local roads) that are within the model area but not included within the model network (see section 2.5.2); and
  3. Methods to estimate VMT for donut areas not covered by the TDF model (see section 2.5.3).

2.5.1 Adjustments to Model Output to Ensure Appropriateness for Emissions Analysis

In cases where a TDF model is available, the model itself is generally used to estimate future VMT by functional class. However, some adjustments may be made to the estimates so they can be used for the regional emissions analysis. The adjustments are typically made either to improve the reliability of the estimates or to ensure that the resulting estimates are consistent with estimates from HPMS that were used in developing the emissions budget in the SIP.

Samples of adjustments include:

Forecasting VMT with a TDF Model: Adjustments to Model Output

Adjustment 1: Adjustment Factor to Scale Modeled VMT Estimate to HPMS VMT Estimate

Scale of 1-5(lowest to highest) - Availability of Data:5 ; Ease of Application:5 ; Technical Robustness:3.5 ; Policy Sensitivity:1

Description
VMT estimates from the urbanized area TDF model are compared to the urbanized area VMT estimate from HPMS for each urban functional class. Adjustment factors are calculated for each roadway functional class to fit the modeled VMT estimates to the HPMS estimates. The adjustment factors are then applied to all forecast years to scale the forecasts.
Method Applicability
This method is applicable to areas with a TDF model where the model does not include all roadway links, or does not represent an estimate of total regional VMT. This adjustment is required under the transportation conformity rule.
Data Sources and Procedures

VMT Estimation

When a travel demand model is used to estimate VMT, those estimates must be checked against HPMS VMT estimates and adjusted if needed. The goal is to ensure, as best possible, that the travel demand model is forecasting VMT consistently with the VMT reported through the HPMS system.

VMT estimates from the TDF model are compared to the VMT estimate from HPMS for each functional class in the base year. Adjustment factors are calculated for each roadway functional class to fit the modeled VMT estimates to the HPMS estimates, as follows:

AdjustmentFactor = VMT<sub>HMPS</sub>/VMT<sub>TDFmodel</sub>

VMT Projection

VMT projections are made using the TDF model in a standard fashion. The adjustment factors for each functional class developed in the base year comparison are then applied to all forecast years to scale the forecasts.

Advantages
  • Conceptually simple approach.
  • Data are readily available.
  • Required under the transportation conformity rule.
Limitations
  • Relies on the accuracy of HPMS data.
  • Completely static with regard to effects of projects, other factors, etc. across functional classes for forecasts.
Example Location

This adjustment is required under the transportation conformity rule, and always should be applied in areas with TDF models to come up with accurate forecasts. Michigan DOT, for instance, uses adjustment factors to scale the results of the urban area models that it maintains for all small urban areas. In Yuma County, Arizona, VMT figures on local roadways were scaled up since the model local roadway mileage was 136 miles, whereas the actual local roadway mileage was approximately 780 miles.

References:

Michigan DOT, Travel Demand Analysis Section. "Technical Documentation of the Procedures Used to Develop VMT and Speed Estimates for Michigan Non-Attainment Counties Containing a Modeled Urban Area." 1995.

Lima & Associates "Vehicle Particulate Emissions Analysis" prepared for ADOT, and Yuma MPO, May 2002.

Forecasting VMT with a TDF Model: Adjustments to Model Output

Adjustment 2: Adjustment to Account for Trip Lengths that do not Cover the Entire Link Length

Scale of 1-5(lowest to highest) - Availability of Data:3 ; Ease of Application:3 ; Technical Robustness:3 ; Policy Sensitivity:2

Description
The standard calculation of link VMT (link volume x link length) assumes that vehicle trips travel the entire length of the link. This is not always the case, particularly for local roads in rural areas. As a result, an adjustment is made to scale VMT down for selected segments or classifications in order to better reflect actual travel activity.
Method Applicability
This method is most applicable to areas in which the TDF model contains long road links and where substantial activity is likely to occur away from the endpoints of the links.
Data Sources and Procedures

VMT Estimation and Projection

Baseline VMT is estimated for each link using a TDF model. Based on professional expertise, knowledge of a given location, or review of travel activity data, selected road links or classifications are subject to a downward adjustment to represent trips traveling a limited distance along the links. The adjustment factor is then applied to future forecasts on those links or functional classes.

Advantages
  • Relatively simple approach.
  • Better reflects regional VMT.
Limitations
  • Requires GIS capabilities and comprehensive road network data.
  • Must be accounted for in TDF model calibration.
Example Location

The approach has been used in Yuma County, Arizona, where final VMT was adjusted down in rural areas by a certain percentage for local paved roads and by another percentage for local unpaved roads.

Reference: Lima & Associates "Vehicle Particulate Emissions Analysis" prepared for ADOT, and Yuma MPO, May 2002.

Forecasting VMT with a TDF Model: Adjustments to Model Output

Adjustment 3: Detailed Approach to Incorporating External Trips into TDF Model

Scale of 1-5(lowest to highest) - Availability of Data:2 ; Ease of Application:3 ; Technical Robustness:3 ; Policy Sensitivity:3

Description
TDF models account for internal trip generators and attractors (i.e., located within the model area) as part of trip generation, distribution, and mode split. They also account for external trips at the network assignment stage. This adjustment involves a detailed analysis of external trips associated with tourism in order to develop projections of VMT, which are added into the model's VMT projections. Growth in external trips is estimated based on professional judgment using analysis of applicable variables.
Method Applicability
This method is most applicable to areas where external trips make up a significant portion of total regional VMT, particularly small communities that are tourist destinations or have a great deal of freight activity utilizing a port or trucking facility. The level of detail in the approach can relate to the level of importance of the factor.
Data Sources and Procedures

VMT Estimation and Projection

An off-model, customized procedure is developed to account for external trip purposes that may represent significant VMT and be sensitive to predictable factors. These may include external-internal trips and external-external (through) trips. Although these trips are typically accounted for in traffic assignment based on traffic counts, this procedure includes a detailed analysis and projection methodology to better predict potential changes in the rate of external trips. For tourist trips, projected population increases in states that supply the largest number of visitors and anticipated growth in service employment can be used to estimate the number of external trips and VMT generated.

Advantages
  • Required to account for all components of regional VMT.
  • Addresses an important factor for certain rural and small urban areas.
  • Flexibility in degree of precision vs. level of effort.
  • Sensitive to factors that influence external trips, which may be different from internal trips.
Limitations
  • Custom off-model procedures may require additional resources and technical expertise.
  • External trip adjustments often rely on professional judgment and thereby open to potential bias or error.
  • May be data intensive if external trip estimates are based on vehicle surveys or economic data.
Example Location

The approach has been used by the Maine DOT in its statewide TDF model. The statewide model relies on population demographics, employment, and economic activity in order to forecast VMT. A REMI model[10] was used to establish base year and forecast year population and employment for nine regions in Maine. By using a REMI model for population and employment estimates Maine's statewide TDF model accounted for vehicle travel that may be specifically associated with large transportation investments.

A separate category of external trips was developed for tourist travel into Maine. Maine DOT reviewed population increases in states that supply the largest number of visitors to Maine (Massachusetts, Connecticut, Rhode Island, New York, and New Jersey) and projected growth in service employment in order to come up with an estimated increase in external trips.

Website: http://www.maine.gov/mdot/aqn/

Reference: Maine Department of Transportation (Bureau of Planning), "The 2002 - 2004 STIP Conformity Analysis for Maine's Nonattainment and Maintenance Areas," August 2001.

Forecasting VMT with a TDF Model: Adjustments to Model Output

Adjustment 4: Use of Seasonal Adjustment Factor

Scale of 1-5(lowest to highest) - Availability of Data:4 ; Ease of Application:4 ; Technical Robustness:4 ; Policy Sensitivity:2

Description
An adjustment is made to scale average annual daily VMT to reflect a seasonal estimate of average daily VMT, either summer or winter, depending on the pollutant of concern. The seasonal adjustment is made to ensure that the resulting VMT estimate is consistent with assumptions used in the SIP emissions budget. The methodology can be used with or without a TDF model.
Method Applicability
This method is most applicable to areas where there are significant seasonal variations in travel activity (e.g., due to tourism) or where the SIP budget was developed with a seasonal adjustment.
Data Sources and Procedures

VMT Estimation and Projection

A scaling factor is developed to scale the annual average daily VMT estimates to reflect a summer or winter season. The scaling factor is developed by dividing average daily traffic in the season of interest by average annual daily traffic (AADT). The data come from traffic surveys conducted at various points in the year.

Advantages
  • Better reflects actual travel activity for period of concern.
  • Simple methodology with limited resource requirements.
Limitations
  • Requires enough data on travel at different times of the year for all road types in order to ensure accuracy.
Example Location

Pennsylvania DOT (Penn DOT) developed an automated software package called PPSUITE, which takes the daily volumes from its Roadway Management System (RMS) that represent AADT, and seasonally adjusts the volumes to reflect an average weekday in July. The Pennsylvania DOT developed the adjustment factors for each functional class of roadway based on the ratio of weekday July traffic counts to the RMS's data on annual average volumes.

Reference:

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

2.5.2 Methods to Estimate VMT for Local Roads not Covered by TDF Model

Many TDF models only include the higher functional classifications of roadways, not roadway functional classes with low traffic volumes such as local roads. Accounting for future local road links in TDF models is often problematic since the construction of local streets is dependent upon private residential development and is not included in regional transportation plans, and therefore, it is difficult to determine where and how many local roads will be built in future years. Moreover, some areas may not have an accurate inventory of all local roads. However, in order to estimate regional emissions, estimates of VMT on the entire road network are required.

As a result, areas with TDF models typically use off-model procedures to forecast VMT on local roadways. Several methods can be used to estimate VMT for local roads that are not covered by a regional TDF. Some of the methods described earlier in Section 2.3 (i.e., Method 1: Use statewide HPMS data to calculate the proportion of local road VMT to collector VMT; or Method 2: Use county-level HPMS estimates to develop a statistical relationship between local road VMT and collector VMT) can also be applied in areas with TDF models.

The methods described in this section rely on information from the TDF model. The two sample methods are:

Forecasting VMT with a TDF Model: Estimating VMT for Local Roads

Method 1: Assume Percent of Modeled VMT

Scale of 1-5(lowest to highest) - Availability of Data:5 ; Ease of Application:5 ; Technical Robustness:1 ; Policy Sensitivity:1

Description
Many TDF models do not produce VMT estimates for roadway functional classes with low traffic volumes such as local roads. Under this method, local road VMT is assumed to be a percentage of modeled VMT.
Method Applicability
This method is applicable to all areas where a roadway classification (i.e., local roads) in the modeled area is not represented in the model. It is most applicable to an area where the road network is expected to remain relatively unchanged (i.e., the area is not planning to add a new major freeway or arterial facility).
Data Sources and Procedures

VMT Estimation and Projection

VMT on local roads is assumed to be a consistent percentage of modeled VMT. For example, if local road VMT is assumed to be 10% of the modeled VMT, and the model produces an estimate of 100,000 daily vehicle miles, then local road VMT would be estimated as 10,000 vehicle miles, for a regional total of 110,000 vehicle miles. The percentage may be determined based on available data sources, such as HPMS figures for the county or state by functional class.

Advantages
  • Very simple approach and straightforward to explain.
  • Data are readily available.
  • Resource requirements are small
Limitations
  • Using a constant share of local road VMT to non-local road VMT may not be appropriate for projections if a major new highway facility is planned that could change the balance between local road traffic and total traffic. A constant percentage also would not be accurate if different growth rates are expected for interstate (through) traffic associated with external trips and traffic associated with local population.
  • Percentage selected may be highly uncertain. If based on statewide HPMS data, the county under consideration may not reflect state patterns. If based on county-level HPMS data, there are major uncertainties in these estimates.
Example Location

This methodology was used Medford-Ashland (Klamath County), Oregon, by the Rogue Valley MPO in its air quality conformity determination for the 2004-2007 TIP. The Rogue Valley MPO has a TDF model that estimates average daily VMT within the MPO area but does not include local streets. In this case, VMT on local streets in the MPO area was assumed to be 10 percent of the modeled VMT.

References:

Rogue Valley MPO, "2004-2007 Transportation Improvement Program and

Air Quality Conformity Determination," August 26, 2003.

Forecasting VMT with a TDF Model: Estimating VMT for Local Roads

Method 2: Use HPMS Estimate and VMT Growth Rate on Analogous Functional Classes from Model

Scale of 1-5(lowest to highest) - Availability of Data:5 ; Ease of Application:4.5 ; Technical Robustness:2 ; Policy Sensitivity:1

Description
This methodology is similar to Method #1, but relates local road VMT to analogous functional classes. HPMS data are used to estimate VMT on these lower volume functional classes for the base year. Growth in VMT for functional roadway classes not included in the TDF model is assumed to be parallel to VMT growth of functional classes that are represented in the model (e.g., local roads are assumed to have the same growth rate as collectors).
Method Applicability
This method is applicable to all areas where a roadway classification (i.e., local roads) in the modeled area is not represented in the model. It is easiest to apply when this discrepancy is congruous with functional classes.
Data Sources and Procedures

VMT Estimation

VMT estimates are taken directly from the TDF model for functional roadway classes included in the model. For those classes of roads not included in the model, the VMT estimate is taken directly from HPMS.

VMT Projection

VMT growth rates for non-represented functional classes are assumed to be parallel to those of a functional class that is represented in the model (e.g., local roads are assumed to have the same growth rate as collectors). For example, if the model forecasts that VMT on rural collectors will increase 15% between the base year and the forecast year, then VMT on rural local roads would be assumed to increase by 15%.

Advantages
  • Simple and straightforward approach
  • Data are readily available.
Limitations
  • HPMS VMT estimates for local roads generally depend on a small sample of roads within a given county and may therefore be unreliable.
  • VMT on functional classes not included in the model (i.e., local roads) may not experience the same growth rate as classes included in the model.
Example Location

The approach has been used by Michigan DOT in the portion of Allegan County that is outside of the area covered by the model used for the MPO area by the Macatawa Area Coordinating Council (MACC).

References:

"Technical Documentation of the Procedures Used to Develop VMT and Speed Estimates for Michigan Non-Attainment Counties Containing a Modeled Urban Area," Travel Demand Analysis Section, Michigan DOT, 1995.

Forecasting VMT with a TDF Model: Estimating VMT for Local Roads

Method 3: Off-Model GIS Analysis Using TAZ-Level Trip Data and Number of Dwelling Units

Scale of 1-5(lowest to highest) - Availability of Data:2.5 ; Ease of Application:1 ; Technical Robustness:4 ; Policy Sensitivity:4

Description
For low traffic volume road links not represented in the model network (usually local roads), VMT estimates are developed using a GIS application. Baseline VMT is estimated for each local roadway link in a traffic analysis zone (TAZ) based on the link's length and the number of vehicle trip-ends generated within the TAZ. Future year VMT is estimated based on projected increases in the number of dwelling units within the TAZ and an estimate of future VMT per dwelling unit developed based on regression analysis of historical data.
Method Applicability
This method is applicable to all areas where not all road links in the modeled area are represented in the model.
Data Sources and Procedures

VMT Estimation

Baseline VMT is estimated for each local link in a traffic analysis zone (TAZ), based on the link length (derived using a GIS application) and the number of vehicle trip-ends generated within the TAZ. These two factors may be statistically evaluated against those local roads for which data are available, and a relationship thus developed.

VMT Projection

Future year VMT on local roads is estimated as base year VMT plus additional VMT associated with new development. Since the number of lane miles of new local roads is unknown, the incremental VMT is estimated based on the projected increase in the number of dwelling units in the TAZ and an estimate of daily VMT on local roads per dwelling unit. Local road VMT per dwelling unit is estimated based on a linear regression of historical values from travel surveys.

Advantages
  • Relatively robust and technically appropriate.
  • Sensitive to changes in population and development patterns.
Limitations
  • Requires GIS capabilities and comprehensive road network data.
  • Requires additional data (such as number of dwelling units and VMT per dwelling unit).
  • Method for estimation requires cross-checks to insure VMT is consistent with empirical data.
Example Location

The approach has been used in Yuma County, Arizona, where local roads in the regional transportation network were not represented in the TransCAD model.

An inventory was performed on all local streets in the region to obtain relevant information, such as their location and surface type. In this case, link VMT for local roads in the base year was calculated using the equation:

Link VMT = (Trip Ends in TAZ/Sum of lengths of links in TAZ in miles) x (Length of links in miles)<sup>2</sup>

The VMT for future off-network links could not be estimated by the foregoing expression, since it is difficult to estimate the future construction of local roads. However, a simple linear regression analysis revealed that a relationship exists between the VMT and the number of dwelling units in a TAZ.

The analysis found that, on average, daily VMT on local roads for a TAZ increased by 1.22 mile for every increase in one dwelling unit. The increase in VMT on local roads for a specific TAZ was thus estimated as 1.22 times the number of dwelling units added to the TAZ between the base year and the future year.

References:

Lima & Associates "Vehicle Particulate Emissions Analysis" prepared for ADOT, and Yuma MPO, May 2002.

2.5.3 Methods to Estimate VMT in Donut Areas not Covered by TDF Model

In many cases, the geographic area covered by an MPO TDF model is inconsistent with the boundaries of the non-attainment or maintenance area that must be examined for the regional emissions analysis. In cases where the non-attainment or maintenance area is larger than the MPO planning area covered by the TDF model, the total VMT for the entire area is usually estimated through a two part process: 1) the MPO's TDF model is used to estimate VMT in the MPO area (along with any necessary adjustments, as discussed in sections 2.5.1 and 2.5.2), and 2) separate off-model approaches are used to estimate VMT in the portion of the nonattainment or maintenance area outside of the coverage of the TDF model (i.e., the donut area).

A range of off-model approaches can be used to estimate VMT for the donut area. This section identifies three methods that utilize outputs of the TDF model:

In addition, the methods discussed in section 2.4 for forecasting VMT without a TDF model typically can be applied in donut areas.

Forecasting VMT with a TDF Model: Estimating VMT for Donut Areas

Method 1: Subtract Modeled VMT from Projection of Countywide VMT

Scale of 1-5(lowest to highest) - Availability of Data:5 ; Ease of Application:5 ; Technical Robustness:1 ; Policy Sensitivity:1

Description
Forecast county-level VMT is determined by linear projections of HPMS or supplemental data for each functional class. The TDF model provides forecasts for the modeled area. The modeled area VMT is then subtracted from the countywide VMT forecast to obtain an estimate of the donut area VMT by functional class.
Method Applicability
This method is applicable for any nonattainment or maintenance area where only a portion of the area is covered by a TDF model. It is most appropriate for an area that is expected to maintain a stable rate of growth in population, economic activity, and vehicle travel.
Data Sources and Procedures

VMT Estimation

Countywide VMT estimates are based on estimates of Annual Average Daily Traffic (AADT), drawn from the best available data sources. For many areas, the annual HPMS VMT estimates reported to FHWA are the best available data. Some states also collect additional traffic counts and may have better estimates of traffic at the county or MPO level. For local road links without counts, assumptions of AADT can be made. VMT values for the modeled area are subtracted from the county-wide values by functional class to get the base year VMT by functional class for the donut area.

VMT Projection

VMT projections are developed on a county basis based on the historical trend line (e.g., an ordinary least squares linear regression extrapolation of the latest ten years of data). The statistical analysis can use total VMT in order to avoid issues associated with reclassification of VMT by functional class over time due to the expansion of urbanized boundaries and other functional class shifts. Projected VMT is then apportioned to the functional classes in the same ratio as the most recent year of VMT data.

The modeled area VMT forecast is then subtracted from the countywide VMT forecast for each functional class to obtain estimates of the donut area VMT by functional class.

Advantages
  • Relative simplicity of the approach.
  • Resource requirements likely to be small.
  • Rationale and data sources are generally accepted.
Limitations
  • For the non-modeled area, this methodology does not reflect factors that will influence future VMT growth, such as population growth, economic growth, land use changes, and major new developments. As a result, it will not be very accurate for an area that is expecting a change in growth rate (either more rapid or slower) from the historical rate or a growth rate very different from the modeled area.
  • For the non-modeled area, the methodology is not sensitive to changes in transportation investments or policies. Any additional traffic growth associated with upgrades of existing facilities or new facilities needs to be analyzed separately.
  • The countywide projections may not be consistent with the VMT projections developed for the modeled portion of the nonattainment or maintenance area.
  • Any uncertainty regarding the countywide data (e.g., data limitations in HPMS) will be reflected and possibly magnified in the non-modeled area, as the subtraction of the modeled area VMT means all the county's data variance will be attributed to a sub-area of the county. Moreover, the methodology does not directly relate the rate of growth in the modeled area with the donut area, although they presumably should be somewhat related due to their proximity.
Example Location

The approach has been used for the donut area of Sheboygan County, Wisconsin, where The Bay-Lake Regional Planning Commission conducts the conformity analysis for the Sheboygan County maintenance area using a regional travel demand model for the area within the MPO boundary, and simpler HPMS-based forecasting methodology for the rural donut portion of the county.

References:

Bay-Lake Regional Planning Commission, Wisconsin DOT, and Wisconsin Department of Natural Resources, Assessment of Conformity of the Year 2025 Sheboygan Area Transportation and the 2004-2007 Sheboygan Metropolitan Planning Area Transportation Improvement Program (TIP) with Respect to the State of Wisconsin Air Quality Implementation Plan, Fall 2003.

Forecasting VMT with a TDF Model: Estimating VMT for Donut Areas

Method 2: Develop Independent Projections for High-ADT Roadways, and Proportions from Model Area for Other Functional Classes

Scale of 1-5(lowest to highest) - Availability of Data:4 ; Ease of Application:3 ; Technical Robustness:2 ; Policy Sensitivity:2

Description
This method involves a combination of other methodologies. For high-ADT roads (freeways and major arterials), VMT are estimated from traffic data and estimated traffic growth rates are applied. For low-ADT roads (minor arterials, collectors, and local roads), the ratio of VMT on high- to low-ADT roads from the modeled area is assumed to apply for the non-modeled area.
Method Applicability
This method is applicable for the non-modeled portion of a nonattainment or maintenance area or for any county in a nonattainment or maintenance area where only a portion of the area is covered by a TDF model.
Data Sources and Procedures

VMT Estimation and Projection

An estimate of baseline VMT on high-ADT roads is developed by multiplying ADT on these road links by the link length. The ADT figures come from traffic counts collected along freeways and major arterials. To forecast future VMT, an estimated annual traffic growth rate is applied to the baseline estimate. The traffic growth rate is estimated based on historical data and/or information on factors that may affect future traffic growth.

VMT on low-ADT roads is then estimated using the ratio of VMT on low- to high-ADT roads from the modeled area, as follows:

Low ADT Road VMT<sub>donutarea</sub> = High ADT Road VMT<sub>donutarea</sub> x (Low ADT Road VMT<sub>model</sub>/High ADT Road VMT<sub>model</sub>)

For example, if low-ADT roads contribute 30% of the VMT of high-ADT roads in the modeled area, VMT on low-ADT roads in the donut area is assumed to be 0.3 times VMT on high-ADT roads in the donut area.

Advantages
  • Flexibility of the approach.
  • Resource requirements likely to be small - uses existing data.
  • Rationale and data sources are generally accepted.
Limitations
  • Ratio of low- to high-ADT road VMT in the modeled area may not reflect ratio in the nonmodeled area if the characteristics of the roadway network differ significantly (for example, if the nonmodeled area contains very few homes and a higher proportion of through traffic than the modeled area).
  • High degree of discretion makes method more open to introduction of bias and opinion.
Example Location

The approach has been used in Medford-Ashland (Klamath County), Oregon, by the Rogue Valley MPO area for the conformity analysis of the 2004-2007 TIP. The Rogue Valley MPO has a TDF model that estimates average daily VMT within the MPO. An "off-model" calculation was conducted for roadways outside the MPO area. VMT on arterials and interstates in non-MPO areas was estimated based on traffic counts and estimated traffic growth rates developed by the Oregon Department of Transportation; VMT on collectors and local roads in non-MPO areas was estimated based on the same ratio of VMT on these roads to arterials and interstates as inside the MPO area.

References:

Rogue Valley MPO, "2004-2007 Transportation Improvement Program and

Air Quality Conformity Determination," August 26, 2003.

Forecasting VMT with a TDF Model: Estimating VMT for Donut Areas

Method 3: Use of Statewide Model for Non-MPO TDF Model Area

Scale of 1-5(lowest to highest) - Availability of Data:1.5 ; Ease of Application:1.5 ; Technical Robustness:4 ; Policy Sensitivity:4

Description
An MPO's TDF model is used for the MPO planning area and a statewide TDF model is used for portions of the nonattainment or maintenance area outside of the MPO boundary. Both models rely largely on HPMS VMT data. For the donut area, the estimate of VMT from the MPO model is subtracted from the total countywide VMT estimate from the statewide model to determine VMT in the donut area.
Method Applicability
This method is applicable for a nonattainment or maintenance area where only a portion of the area is covered by an MPO TDF model and where a statewide model is available.
Data Sources and Procedures

VMT Estimation and Projection

Base year and future year estimates of VMT for the MPO planning area are calculated using the MPO's TDF model. Base year and future year estimates of countywide VMT are developed using the statewide TDF model (Since statewide models do not include all roadway links, expansion factors are developed for each functional class by taking the HPMS county-level VMT estimate and dividing by the modeled VMT estimate for each functional class; the expansion factors by functional class are then applied to all future year VMT forecasts).

Estimates of VMT from the MPO's TDF model are then subtracted from the total countywide VMT estimates from the statewide TDF model to determine VMT in the portion of the county not covered by the MPO's TDF model.

Local roads are not incorporated into statewide models, so county-level HPMS figures are used for the base year. VMT growth for those local roads is assumed to parallel growth on collectors, and future year VMT figures are calculated accordingly.

Advantages
  • Rationale and data sources are well accepted.
  • Use of statewide TDF model provides greater robustness and more sensitivity to changes in the highway network than off-model methods.
Limitations
  • The need for a separate statewide model limits the applicability of this method; implementing one solely for this purpose is unlikely to be an efficient use of resources.
  • For the donut area (and potentially the MPO area), local road links not represented in the models need to be estimated based on HPMS-estimates that are less robust.
Example Location

The approach has been used by Michigan DOT for donut areas outside of MPO boundaries in small urban areas, such as Allegan County. Michigan DOT maintains a statewide TDF model, which is used in these analyses.

References:

Michigan DOT, Travel Demand Analysis Section. "Technical Documentation of the Procedures Used to Develop VMT and Speed Estimates for Michigan Non-Attainment Counties Containing a Modeled Urban Area." 1995.


[4] The HPMS provides data that reflects the extent, condition, performance, use, and operating characteristics of the nation's highways. For more information on background, scope, major uses of the HPMS, and reporting requirements, consult FHWA's HPMS Field Manual at http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/.

[5] Adapted from Guidance for the Development of Facility Type VMT and Speed Distributions, U.S. EPA, Report Number M6.SPD.004, February 1999.

[6] Methods for estimating VMT by speed bin are discussed in Section 3. Methods for estimating VMT mix by vehicle type are discussed in Section 4.2.

[7] The MOBILE6 emissions model functions differently than MOBILE5, which simply developed emission factors based on average speeds. As a result, methodologies for conformity analysis using MOBILE5 did not need to make this distinction between the different definitions of local roadways, and commonly developed emissions estimates based on average speed by functional roadway classification.

[8] This formula is nearly equivalent to method 1, except that it allows for a certain baseline VMT level on local roads that is independent of the volume on collector roads.

[9] American Association of State Highway and Transportation Officials, A Policy on the Design of Highways and Streets [2001 Greenbook], 2001.

[10] A REMI model (Regional Economic Models, Inc.) is a commonly used economic-forecasting and policy-analysis model;

Updated: 11/02/2011
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