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

Forecasting VMT with a TDF Model: Adjustments to Model Output

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

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:

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

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

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/air-quality-noise/air-quaility-noise.php

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

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.

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

Method 1: Assume Percent of Modeled VMT

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

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

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:

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.

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

Method 1: Subtract Modeled VMT from Projection of Countywide VMT

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

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:

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

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.