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

Forecasting VMT without a TDF Model

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

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

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

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:

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:

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

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

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:

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

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.