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

The regional emissions analysis is the key analytic component of the transportation conformity process. It is conducted to demonstrate that regional emissions from on-road sources do not exceed levels that could cause or contribute to violations of the health-based air quality standards, and to ensure that transportation plans, programs, and projects are consistent with the State Implementation Plan (SIP) for air quality.

Many small urban and isolated rural nonattainment and maintenance areas face challenges in conducting a regional emissions analysis. Small urban and rural areas typically have limited data on vehicle miles traveled (VMT) and speeds required for emissions analysis. In addition, they often lack network-based travel demand forecasting (TDF) models that predict future travel inputs for emissions analysis. As a result, many small urban and rural areas have faced questions about appropriate methods for conducting a regional emissions analysis.

This document is intended to help small urban and rural areas gain a better understanding of several options for conducting regional emissions analysis. It provides information on sample methodologies and adjustment techniques that have been used for regional emissions analysis in a number of small urban and isolated rural nonattainment and maintenance areas. For each method, the report includes a general description, data sources and procedures, advantages and limitations, and circumstances for which the approach is most appropriate. Although the methodologies profiled in this document are not comprehensive, they provide information that should be helpful to areas in considering potential approaches.

The document is divided into three sections, reflecting key inputs to emissions analysis:

VMT estimation and forecasting examples (Section 2);
Speed estimation and forecasting examples (Section 3); and
Sample techniques for other factors (such as VMT mix by vehicle type, vehicle age distribution, etc.) (Section 4)

This document describes methodologies that have been used in locations without TDF models, as well as techniques that have been used in areas with TDF models. It shows that a wide range of approaches are available to estimate and forecast VMT, speeds, and other factors for emissions analysis. These approaches range from simple to relatively complex methodologies. For example, to predict VMT in an area without a TDF model, identified approaches range from use of a simple linear trend line of historical data to use of more complex regression analyses that employ non-linear functions and take into account factors such as projected population and employment. To estimate speeds, identified approaches range from use of observed speeds to use of speed formulas that are applied to estimate speeds along individual road links. Statewide data are sometimes used (e.g., to develop relationships between VMT on different road types, or to estimate speeds by road type) when data specific to the small urban or isolated rural area are unavailable.

VMT Estimation and Forecasting Approaches

Section 2 of this report presents several approaches for estimating baseline VMT and for forecasting future VMT, as described below:

Methodologies for Estimating Local Road VMT

Although estimates of VMT are available from the Highway Performance Monitoring System (HPMS), the sample of segments for a small urban or rural area may not be sufficient to provide accurate estimates of VMT by functional roadway classification for an area, particularly for local roadways. Three methods were identified to develop baseline estimates of VMT on local roads given limited data; these methods can be applied in areas with or without a TDF model (since TDF models often do not include lower functional class roadways), and can also be used in forecasts. These methods are as follows:

Use statewide estimates to calculate the proportion of local road VMT to collector VMT; apply the resulting ratio (Method 1)
Use available county-level estimates to develop a statistical relationship between local road VMT and collector VMT; apply the resulting formula (Method 2)
Develop a detailed inventory of local roads, and estimate average daily traffic on local roadways (Method 3)

Methodologies for Forecasting VMT without a TDF Model

Areas without TDF models generally rely on calculations that involve spreadsheets to forecast future VMT. VMT forecasting methodologies range from very simple linear trend lines to more complex non-linear regression analyses. Sample methods include:

Linear projection of VMT based on estimated growth factor (Method 1)
Linear projection of total VMT, based on regression analysis of historic VMT data, apportioned by functional roadway class (Method 2)
Linear projections of VMT by functional roadway class, based on historic VMT data, with adjustments to correct for changes in functional class categories (Method 3)
Linear projection of interstate VMT based on historic VMT data, and separate population-based forecast for non-interstate VMT (Method 4)
Analysis of anticipated VMT growth in each interstate corridor, and population-based forecast for non-interstate VMT (Method 5)
Separate regression forecasts by functional roadway class, based on VMT, population, and employment, with growth factor employing a decay function (Method 6)

Methodologies for Forecasting VMT with a TDF Model

Areas that maintain a TDF model generally use the model outputs to estimate VMT. TDF models offer greater sensitivity to changes in transportation investments or policies, compared to most manual calculation procedures. In estimating future VMT, the TDF model takes into account all transportation improvements at once, predicting the most likely distribution of traffic on the future network. However, adjustments to TDF model outputs are often required in order to make the results suitable for conformity analysis. Adjustments and additions made to the model outputs fall into three categories:

1) Adjustments to TDF model outputs to ensure that VMT results are consistent with estimates used to develop the emissions budget in the SIP. Samples of these adjustments include:

Adjustment factor to scale modeled VMT estimate to HPMS VMT estimate (Adjustment 1)
Adjustment to account for trip lengths that do not cover the entire link length in the model (Adjustment 2)
Detailed approach to incorporating external trips into a statewide TDF model (Adjustment 3)
Use of seasonal adjustment factor (Adjustment 4)

2) Methods to account for local road links that are within the model area but not included within the model network. Samples of these methods include:

Assume percent of modeled VMT (Method 1)
Use HPMS estimate of local road VMT and apply VMT growth rate on analogous function class(es) from the model (Method 2)
Off-model GIS analysis using traffic analysis zone (TAZ)-level trip data and number of dwelling units (Method 3)

3) Methods to estimate VMT for donut areas not covered by model. Samples of these methods include:

Develop projection of countywide VMT and subtract modeled VMT estimate (Method 1)
Use traffic counts and other projections for higher-classification roadways, and apply ratio from model to estimate VMT on lower-classification roadways (Method 2)
Use a statewide model and subtract estimates from urban area model (Method 3)

Speed Estimation and Forecasting Approaches

Speed estimates are important since emissions rates for VOC, CO, and NO_X can vary widely with speed. Section 3 of the report presents several approaches for estimating speeds without and with a TDF model, as described below:

Methodologies for Estimating Speeds without a TDF Model

Areas without a TDF model generally lack detailed information on the roadway network and associated traffic volumes, and therefore, may not have the option of estimating speed on enough roadway segments to determine the distribution of VMT by speed. In this case, they typically estimate average speed by functional roadway classification. Samples of methodologies used in areas without a TDF model and for donut areas outside of a modeled area include:

Use observed speeds and/or speed limits (Method 1)
Use HERS Model at a statewide level (Method 2)
Use BPR formula or variation (Method 3)
Use TTI method (Method 4)

Methodologies for Estimating Speeds in Areas with a TDF Model

Estimating Speeds in the Area covered by the TDF Model

A TDF model estimates traffic speed on each link as part of the network assignment process. However, TDF models are typically calibrated so they closely match observed traffic volumes, not traffic speeds. As a result, the speeds may or may not be accurate for a given area. To account for such inaccuracies, adjustments are sometimes made to TDF model speeds for the purpose of developing emission factors. Samples of methods used include:

Use TDF model outputs (Method 1)
Use TDF model outputs with adjustments where model values are inconsistent with observed data (Method 2)
Use formula and/or lookup tables to estimate speed based on modeled V/C ratio (Method 3)

Estimating Speed in Donut Areas not covered by the TDF Model

In nonattainment or maintenance areas that contain donut areas not covered by a TDF model, the same methods that were presented for areas without a TDF model can be applied to estimate speeds in the donut area. In addition, two other techniques were identified that rely on modeled data:

Use speeds from modeled area by functional class (Method 1)
Use speeds from statewide model (Method 2)

Other Factors

The MOBILE6 model takes into account a number of factors in estimating emissions rates, including the mix of vehicles that contribute to VMT, the age distribution of the vehicle fleet, the mix of VMT by functional roadway classification, and the existence of inspection and maintenance (I/M) programs. While the MOBILE model contains default values for many of these factors, the defaults may not reflect local conditions, and small urban and rural areas may want to use approaches to improve upon defaults. For these factors, Section 4 of the report describes several methods for using local data instead of defaults, and compares these approaches with approaches that rely on default values.

VMT Mix by Vehicle Type

The VMT fleet mix determines how the VMT are assigned to each vehicle type or class. Emission factors across vehicle classes may vary widely. As a result of this variation, small changes in fleet mix have the potential for large changes in emission totals. Sample approaches for estimating VMT mix by vehicle type include:

Use MOBILE6 model default, which is based on national-level vehicle registration data and projected future changes in registrations (Method 1)
Use available local data (vehicle registration data, traffic data, or combination) and assume constant mix (Method 2)
Use available local data for base year fleet mix and iteratively adjust to reflect expected changes in mix (Method 3)

Vehicle Age Distribution

The vehicle age distribution determines the fraction of vehicles operating within each emission control requirement standard and the deterioration of the emission control technology. Emission rates vary significantly with vehicle age, and thus, small changes in fleet age may result in large changes in emission totals. Sample approaches to vehicle age distribution include:

Use MOBILE6 model default, which is based on national-level vehicle registration data (Method 1)
Use local vehicle registration data for in-use fleet (Method 2)

Percent of VMT on Freeway Ramps

The MOBILE6 model develops emissions factors for four sets of driving cycles: freeway (excluding ramps), arterial/collector, local roadway, and freeway ramp. Most transportation agencies do not collect estimates of VMT on freeway ramps, and so the MOBILE model includes a national default of 8 percent of freeway VMT occurring on freeway ramps. Although EPA generally recommends using the default, this national average may not be appropriate for rural areas with a limited number of interchanges and some small urban areas. The report describes one method that involves a local traffic survey to collect data on the percentage of freeway VMT on ramps.

Inspection and Maintenance Program Implementation

I/M programs reduce average emissions rates, and the type of I/M program may have potentially significant impacts on emission totals. The standard way to address I/M programs in MOBILE6 is to specify the I/M programs in place in the nonattainment or maintenance area. However, this approach may not be accurate in small urban or rural areas that are not subject to I/M requirements but have a sufficient amount of through traffic from areas that are subject to I/M requirements. These "through vehicles" can significantly affect on-road emission rates. Sample approaches for addressing I/M programs include:

Apply type of I/M program to area of analysis (standard approach) (Method 1)
Use local data sources to estimate proportion of traffic subject to I/M (Method 2)

Selecting an Appropriate Approach

In selecting an appropriate approach, there are often tradeoffs to be made. Simple methods tend to have advantages in terms of data availability and ease of application, but may not be as technically robust. In contrast, more complex methods tend to have advantages in terms of being able to produce robust results for different circumstances and being sensitive to changes in transportation investments and other policies, but may be more time-consuming to apply and require greater investments in data collection.

The advantages and limitations of each approach need to be weighed in terms of the availability of data and local understanding of conditions that influence the accuracy of an approach. Although complex methods may be more robust overall, simple methods may be most appropriate in cases where results are expected to be similar to those of a more complex method with less data collection and cost.

The unique circumstances of the nonattainment or maintenance area should determine what techniques or approaches are most appropriate. For example, a linear projection of VMT may be appropriate if historical population trends are expected to continue and the road network is expected to remain largely the same; however, it would not be as appropriate if the area is expecting much more rapid or slow growth than in the past, or if a major new highway facility is planned, which could bring in more through traffic. If MOBILE defaults are being considered, it is important to examine whether the defaults reflect patterns for the area of analysis or whether the defaults reflect areas that are different in character. Similarly, if state-level data are being considered when local data are unavailable (for example, to estimate the proportion of VMT on local roads to collectors, or to estimate average speeds by roadway type), it is important to consider whether the area of analysis exhibits characteristics typical of the state as a whole.

There is no "one-size fits all" approach for conducting an appropriate regional emissions analysis. Methods should be selected based on data availability and local conditions, and should be determined through the interagency consultation process. This report seeks to support current and newly designated areas subject to conformity in considering potential options for regional emissions analysis.

1 Introduction

1.1 Background and Purpose

The regional emissions analysis is the key analytic component of the transportation conformity process, and is conducted to demonstrate that transportation plans, Transportation Improvement Programs (TIPs), and projects are in conformity with the State Implementation Plan (SIP) for air quality. A critical factor that will influence what methods can be used for the regional emissions analysis is whether or not the region has a travel demand forecasting (TDF) model. The transportation conformity rule specifically requires that serious and above ozone and carbon monoxide nonattainment areas with urban population more than 200,000 use network-based travel models for the regional emissions analysis. For small urban and rural areas, and others not meeting these criteria, the conformity rule allows areas flexibility to conduct regional emissions analysis by either continuing the existing modeling practices of the MPO or by using "any appropriate methods" that account for vehicle miles traveled (VMT) growth and future transportation policies.

Many small urban and rural nonattainment and maintenance areas face challenges in conducting the regional emissions analysis. These areas often do not have TDF models to generate travel outputs required for use in emissions analysis. They also often have very limited data on VMT and speeds required for emissions analysis. As a result, existing small urban and rural areas have faced questions about what are appropriate methods for conducting a regional emissions analysis given limited data and tools.

The purpose of this document is to provide information on methodologies and adjustment techniques that have been used for regional emissions analysis in several small urban and isolated rural nonattainment and maintenance areas. The methodologies described in this document were identified through a research effort that involved a literature review and contacts with staff from over twenty State Departments of Transportation (DOTs) and metropolitan planning organizations (MPOs) that conduct conformity analysis. This document describes and assesses methodologies, and is intended to help small urban and rural areas gain a better understanding of some of the procedures that have been used for conducting regional emissions analysis.

1.2 How to Use this Document

This document is designed as a "menu" of methodologies and adjustment techniques that can be used in small urban and rural areas for regional emissions analysis. An important theme behind this document is that a variety of methods are available and appropriate for different circumstances. This document is not intended to direct a specific methodology to be used in a particular location. The methodology that is ultimately used should be determined through the interagency consultation process, and should reflect considerations appropriate for the nonattainment or maintenance area. The methodologies profiled in this document are not necessarily comprehensive; other methods may be feasible or appropriate.

The document is organized as follows:

Sections for Key Inputs Required for Emissions Analysis

The document is divided into three main sections, which relate to specific inputs required for conducting the regional emissions analysis:

VMT Estimation and Forecasting Examples (Section 2)- VMT estimates (by functional roadway classification and speed are a necessary input for estimating regional emissions, and are required for each year being analyzed. VMT estimates are used together with emissions factors developed from EPA's MOBILE model (or EMFAC in California) to estimate emissions.^[1] This section describes methods for developing estimates of VMT for use in the emissions analysis.
Speed Estimation and Forecasting Examples (Section 3)- Speed is a key input required in order to estimate emissions factors in EPA's MOBILE Model (and EMFAC). This section describes methods for developing estimates of average speeds, or speed distributions, for use in the emissions analysis.
Other Factors: Sample Techniques (Section 4) - The emissions rates generated by MOBILE (and EMFAC) depend on a number of factors, including the VMT mix by vehicle type, vehicle age distribution, and the participation of vehicles in inspection and maintenance (I/M) programs. The MOBILE model provides default values for these factors, which are often used in small urban and rural areas. This section summarizes methods that can be used to generate these factors using local data in place of defaults.

A typical user of the document should review methods within each of these three sections since each addresses a necessary component of the emissions analysis process. Moreover, how VMT is treated will have impacts on how speeds must be treated.

Section 5 of the report contains a summary, and Section 6 provides resources for additional information on regional emissions analysis and the conformity process.

Areas with Travel Demand Forecasting Models

Although not required to use TDF models, many small urban areas do have TDF models, and use them to conduct conformity analysis. A number of States have also developed statewide TDF models (e.g., Maine, Michigan, Oregon), which are used to estimate VMT for higher-order roadway classifications. The methodologies that can be used in these areas to forecast VMT and speeds will differ considerably from those in areas without models. As such VMT and speed methodologies are addressed separately for areas with and without TDF models. In cases where a TDF model is available, techniques are often used to adjust the outputs of the models to reflect local conditions and to ensure that the results can be used appropriately for emissions analysis. In addition, methods are sometimes needed to address "donut" areas^[2] or specific functional roadway classifications that are not addressed by the TDF model.

The typical user of this document can skip to the appropriate subsections of the document depending on whether or not a TDF model is available.

Methodology Descriptions

Each methodology is presented in a standard format for easy reference. For each methodology, the document briefly describes the method, discusses where the method is most applicable, and provides information on advantages and limitations of the approach, based on ICF Consulting's assessment. It also identifies a sample location where the methodology has been applied.^[3]

Each method is assessed on a qualitative scale (from low to high) across four criteria, in order to help the user determine the applicability of each:

Availability of Data - How readily available are the data that are required in order to use the method? Methodologies that require limited amounts of readily available data will score "high", while those that require a large amount of data that may be difficult to obtain will score "low".
Ease of Application - How simple or complex is the method to actually apply? Methodologies that are relatively easy to implement and have relatively simple procedures or calculations will score "high", while those that require a great deal of time, effort, and resources to apply will score "low".
Technical Robustness - How reasonable are the results of the methodology believed to be for a variety of different circumstances? Methodologies that take into account a full range of factors that might affect emissions will score "high", while those that use a lot of simplifying assumptions and whose results do not vary in different circumstances will score "low".
Policy Sensitivity - How sensitive are the results of the methodology to changes in highway investments, transit investments, or other policies? Methodologies that take into account the effect of transportation decisions will score "high" (e.g., a VMT methodology that predicts lower VMT based on increased transit investments and associated transit service improvements would exhibit high policy sensitivity). In contrast, methods that predict the same results regardless of relevant policy changes will score "low" (e.g., speed tables that predict the same speed for traffic regardless of a large investment in a major corridor signal coordination project would be exhibit low policy sensitivity).

It is important to point out that simply because a methodology scores "low" on technical robustness or policy sensitivity does not mean that the methodology is inferior or should be avoided. In some cases, a relatively simple methodology may be the most appropriate, and it may not be worth the additional effort or cost to use a more complex methodology if the results are not expected to be substantially different. The specific circumstances in the region will determine which approaches are most appropriate.

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

Table of Contents

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