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

Table of Contents

  1. Executive Summary
    1. Background and Purpose
    2. VMT Estimation and Forecasting Approaches
    3. Speed Estimation and Forecasting Approaches
    4. Other Factors
    5. Selecting an Appropriate Approach
  2. 1 Introduction
    1. 1.1 Background and Purpose
    2. 1.2 How to Use this Document
  3. 2 VMT Estimation and Forecasting Examples
    1. 2.1 Background - Importance of VMT Estimates
    2. 2.2 MOBILE6 Requirements for VMT
    3. 2.3 Methodologies for Estimating Local Road VMT
    4. 2.4 Methodologies for Forecasting VMT without a TDF Model
    5. 2.5 Methodologies for Forecasting VMT with a TDF Model
  4. 3 Speed Estimation and Forecasting Examples
    1. 3.1 Background - Importance of Speed Estimates
    2. 3.2 MOBILE6 Requirements for Speed
    3. 3.3 Methodologies for Estimating Speed in Areas without a TDF Model
    4. 3.4 Methodologies for Estimating Speed in Areas with a TDF Model
  5. 4 Other Factors: Sample Techniques to Improve Upon MOBILE Defaults
    1. 4.1 Background
    2. 4.2 VMT Mix by Vehicle Type
    3. 4.3 Vehicle Age Distribution
    4. 4.4 Percent of VMT on Freeway Ramps
    5. 4.5 I/M Participation
  6. 5 Summary
  7. 6 Resources

Executive Summary

Background and Purpose

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:

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:

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:

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:

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:

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

Speed Estimation and Forecasting Approaches

Speed estimates are important since emissions rates for VOC, CO, and NOX 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:

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:

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:

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:

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:

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:

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.

Updated: 07/06/2011
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