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A Sampling of Emissions Analysis Techniques for Transportation Control Measures

Forecasting Approaches

Emissions Analysis Techniques for TCMs

Introduction

Selecting a Method

Descriptions of Available Methods

Key Inputs and Outputs for Each Method

References

List of Acronyms

This section provides a basic overview of travel and emissions forecasting approaches. The overview is intended to assist the user in understanding the various analytical approaches that underlie the methods described in the document. The section first reviews the effects of CMAQ strategies that lead to changes in emissions. Next, travel and emissions models commonly used in metropolitan transportation planning are discussed. These models represent the state of practice in travel and emissions forecasting at a regional level, and often serve as a source of data for other forecasting methods. Alternative analytical approaches to forecasting travel and emissions impacts are then described. The methods documented typically utilize one or more of these analytical approaches.

How CMAQ Projects Affect Emissions

The implementation of a CMAQ project (or any other transportation-related project) has a series of effects, as shown in Figure 1. The effects depend upon the nature of the CMAQ project. A CMAQ project may be primarily: 1) a travel demand management (TDM) project affecting travel behavior (e.g., ridesharing, transit incentives); 2) a transportation systems management (TSM) project affecting traffic flow (e.g., traffic signal timing, freeway ramp metering); or 3) a strategy affecting vehicle or fuel technology (e.g., alternative-fuel vehicles).

For a TDM project, the effects can be described as follows:

  1. The CMAQ project creates changes in trip patterns (e.g., total person-trips, origins and destinations, mode share, time of day of travel);
  2. Changes in trip patterns lead to changes in vehicle activity (e.g., total vehicle-trips, vehicle-miles of travel (VMT));
  3. Changes in vehicle activity affect traffic flow characteristics, including travel speeds and acceleration characteristics; and
  4. Changes in vehicle activity and traffic flow characteristics lead to changes in overall emissions.

For a TSM project, the resulting effects are as follows:

  1. The CMAQ project affects traffic flow characteristics, such as speed and acceleration; and
  2. The resulting changes in vehicle speed and acceleration in turn affect the emission rates of vehicles.

For example, a signal coordination project can smooth vehicular flow, thus reducing acceleration and deceleration, which in turn reduces emissions.

Vehicle and fuel technology-based CMAQ strategies do not affect travel demand or traffic flow, but instead affect overall emission levels by changing the emissions characteristics of vehicles.

Some of the methods documented in this report use various assumptions to simplify the analysis procedure, such as estimating vehicle trip and VMT changes directly from CMAQ project implementation, or ignoring any changes in traffic flow characteristics that may result from demand management strategies. Other methods have a limited focus in that they only estimate travel impacts or only convert travel into emissions impacts. These methods are documented because they provide a valid analytical approach that can readily be applied in conjunction with other approaches. For example, the FHWA TDM Evaluation model provides a sound methodology for estimating VMT changes from TDM projects, but does not estimate emissions changes. To estimate emissions changes, the user would apply trip and/or VMT-based emission factors to the model output.

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Standard Travel and Emissions Forecasting Models

The "benchmark" for travel forecasting in regional transportation planning is an analysis tool known as the "four-step model" or "regional travel model." Most metropolitan planning organizations (MPOs) have a travel model that is specifically developed for their region. The basis for the regional travel model is the division of the urban area into traffic analysis zones (TAZs), and the definition of a network of transportation facilities connecting the zones. The network is described by the time and cost of travel, for each mode, between each pair of zones. Inputs include proposed future transportation networks and forecast population and employment characteristics by zone. Travel survey data and mathematical models are then used to predict the number of trips generated in each zone, the distribution of these trips (origin and destination zones), modal shares, and the routes taken for trips.

Travel models are typically used in developing an area's long-range transportation plan to predict future traffic volumes, based on changes in development and travel patterns, and to compare forecast volumes to roadway capacities to identify deficiencies and needs. They are good for predicting the results of major projects such as new or expanded facilities, but are not well-suited for analyzing small-scale operational projects such as intersection improvements.

Range of Emission Reductions from CMAQ Projects

The reported benefits from past CMAQ projects can illustrate the range of emission reductions that may be expected. Figure 2 shows the frequency distribution of VOC reductions for CMAQ projects funded in 1997. (These are self-reported benefits; also, some projects did not report quantitative benefits and are not reflected in this figure.) The majority of projects resulted in VOC reductions of less than 10 kilograms per day (kg/day), with most of the remainder falling between 10 and 100 kg/day. The median benefit was 5.2 kg/day. Figure 3 and Figure 4 illustrate the range of benefits for CO and NOx. The median reported benefits were 37 and 4.0 kg/day, respectively. CO benefits are typically higher because CO emission rates from vehicles are higher than for VOC or NOx.

For a metropolitan area with a population of one million, total daily VOC emissions from mobile sources might be on the order of 25,000 to 30,000 kg. The regional impact of an individual "median" CMAQ project would therefore be on the order of 0.02 percent. This is not to diminish the benefit of the CMAQ project, but rather to place it in a regional perspective. Multiple projects, or projects with a broader scope, would of course result in a larger percentage reduction in regional emissions.

CMAQ strategies that can be translated into changes in the number of vehicle-trips by TAZ (i.e., an area-wide employer trip reduction strategy) or changes in transportation network characteristics (i.e., new transit service between two points) can be analyzed using the MPO's standard regional travel model. This requires working with the MPO travel forecasting staff and also may require a significant level of effort to develop appropriate inputs and run the model. The advantage of this approach is that changes in vehicle miles of travel and speeds are identified across the entire transportation network.

Once outputs of the travel model are obtained, emission factors (expressed in grams per mile or grams per trip) can be applied to VMT and/or vehicle-trips by vehicle type and speed. The standard model used to develop emission factors is EPA's MOBILE model. (A related EPA model, PART5, is used to develop emission factors for particulate matter. In California, the Air Resources Board's EMFAC model is used in place of MOBILE.) Emission factors are typically developed by the State DOT, MPO, and/or air quality agency using locality-specific data describing the mix of vehicles, fuel characteristics, inspection and maintenance (I/M) program, and other factors that influence emissions. MOBILE factors can be applied to travel model output using a spreadsheet; software programs have also been written to automate the processing of travel model outputs and emission factors.1

Even if they are not used directly in assessing CMAQ strategy benefits, data and methodologies from regional travel and emission models play an important role in CMAQ evaluation strategies. For example, the Metropolitan Washington Council of Governments has used its mode choice model independently of the four-step model to analyze a range of TCM strategies (FHWA, 1995). Elasticities are sometimes obtained from model coefficients. Trip tables and network data from the regional travel model can be used to obtain average trip lengths for a particular area.

CMAQ evaluation methods that calculate emissions should be able to incorporate locality-specific emission factors as developed using MOBILE or EMFAC. These locality-specific emission factors generally can be obtained from the MPO, state DOT, or state environmental agency.

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Forecasting Travel Impacts

Forecasting travel behavior impacts is typically the most difficult part of CMAQ project analysis. A TDM strategy in particular can have a wide range of effectiveness based on the details of the strategy and its implementation context. The analyst should carefully consider the underlying data and analytical approaches utilized in the chosen method(s).

Typical approaches to estimating travel behavior and demand impacts include:

  • Surveys that assess the likely or actual impacts of a CMAQ project. For example, a survey of employees at a suburban employment site may be performed to determine how many people expect to use or have actually used a rideshare matching service. Surveys that provide adequate data for forecasting purposes are difficult to conduct. On the other hand, pre- and post-implementation surveys of actual travel behavior are often both feasible and necessary if the effectiveness of a CMAQ project is to be evaluated retrospectively.
  • Experience from other areas. For example, a study may have found that five percent of office workers will telecommute one day a week if provided the opportunity. This is the simplest methodology to apply and can be used to assess programs that cannot be described in a quantitative manner (i.e., travel time and cost changes). It involves considerable risks, however, in assuming that results can be transferred from one situation to another.
  • Elasticities. An elasticity says that an X percent change of an input variable (e.g., the cost of parking) produces a Y percent change of an output variable (e.g., drive-alone mode share). Elasticities may be developed from direct observation or from coefficients of a model such as a mode choice model. While they can account for different levels of the input variable, they are not necessarily valid outside the range for which they were developed. For example, an increase in the cost of parking from $0.00 to $1.00 is an infinite percentage change, producing meaningless results. Also, elasticities developed in one setting cannot necessarily be assumed accurate in another setting.
  • Logit or pivot-point model. The logit model is a mathematical equation that predicts a particular choice (i.e., auto versus transit mode) as a function of differences in time, cost, or other quantifiable variables affecting travel. Logit mode choice models are typically developed from travel survey data as part of the four-step travel forecasting process. The pivot-point model is a derivative of the logit model with simpler input data requirements. The pivot-point model has the advantage that it requires only a knowledge of baseline mode shares and changes in travel time and cost. (Baseline travel time and cost information is not required because this information is fully reflected in the baseline mode share.) Logit-based models can account for the interaction of multiple factors, i.e., changing the time and cost of travel simultaneously.

Beyond the direct travel impacts of a CMAQ strategy, induced and offsetting travel may also be of interest to the analyst. Under some circumstances, the total travel and emission reductions resulting from CMAQ strategies may be less than the nominal reductions based on the specific vehicle-trips eliminated. Offsetting travel increases may come from two primary sources. The first is additional vehicle-travel by the same traveler or other members of the household. For example, a telecommuter may make an additional trip from home during the day to run an errand they would normally have run in conjunction with a work trip. The second is a "general" induced demand effect, in which reductions in network travel times (as a result of reduced vehicle-trips or improved traffic flow) are partially offset by additional travel resulting from the improved network performance. Induced travel may therefore occur as a result of TSM as well as TDM measures. A few of the models in this guidebook include factors to account for various types of induced and offsetting travel. Appropriate data on these factors, however, can be difficult to develop.

Another important consideration is interaction among strategies. Some strategies may complement each other (e.g., parking management and ridesharing incentives), leading to cumulative effects greater than the sum of the effects of the strategies if applied individually. Conversely, in some cases the effects of multiple programs may be smaller than the sum of their individual effects. For example, some TDM projects may compete for the same market of travelers, thus leading to diminishing returns as more projects are implemented. Except where noted, the methods described in this report typically do not account for the interactive effects of multiple strategies.

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Forecasting Emissions Impacts

To translate travel changes into emissions changes, emission factors (i.e., grams per mile or grams per trip) are typically applied to changes in VMT and vehicle-trips. In some methods, emission factors may vary according to vehicle speed and/or vehicle type.

A number of important analytical issues arise in translating travel impacts into emissions impacts, or in forecasting the emissions impacts of traffic flow improvements. Some of the most noteworthy include:

  • Trip-end versus VMT (distance)-based emission factors. Emissions at the beginning of a vehicle-trip are typically much higher than emissions later in the trip when the vehicle has warmed up. Therefore, some emission forecasting approaches apply separate trip-based and VMT (distance)-based emission factors. EPA's MOBILE5 model - the current source of emission factors in most areas - does not calculate trip and VMT-based emission factors separately; instead, cold-start and hot-start emissions are embodied in the VMT-based factor. As a result, the use of standard MOBILE emission factors will overestimate the benefits of strategies that do not reduce trips, but only affect trip lengths (i.e., park-and-ride), and conversely, will underestimate the benefits of strategies that reduce short trips (i.e., bicycle/pedestrian facilities). CARB's EMFAC model produces both trip-based and VMT-based emission factors, as will EPA's upcoming MOBILE6 model. Procedures have also been developed for identifying separate trip and VMT-based factors from MOBILE5. A few methodologies also consider emissions from work and non-work trips separately, under the assumption that hot versus cold start percentages differ between the two types of trips.
  • Traffic speed/flow impacts on emissions. CMAQ strategies such as signal timing and ramp metering affect the speeds at which vehicles travel on different facilities or affect other characteristics of traffic flow such as idle times and acceleration rates. The way in which a vehicle is operated can have a significant effect on emissions. Vehicles typically emit pollution at higher rates (in grams per mile) at extremely low or high speeds or under hard acceleration.

Speed changes are typically assessed through equations relating traffic speed to volumes and facility characteristics such as capacity. Such equations are embodied within the regional travel model (see Standard Travel and Emissions Forecasting Models), and may also be applied independently for facility-level analysis. As an alternative, some sketch-planning approaches apply elasticities of average travel speed versus total area-wide VMT. Standard emission factor models such as MOBILE and EMFAC produce emission rates as a function of vehicle speed.2 By applying speed-based emission factors, the emission impacts of strategies affecting traffic flow can be estimated to some extent.

The speed-based approach, however, does not account for changes in acceleration or idle characteristics, as might be expected from (for example) a signal timing project. To model traffic flow and emission impacts with greater precision, traffic simulation models are typically used. These models, which simulate actual traffic flows, incorporate emission factors that are based on both speed and acceleration rates.

  • Time-of-day shifting. Some TDM strategies, notably shifted work hours and telecommuting, may affect the time during which trips are taken as well as the total number of vehicle-trips. Shifting trips from the peak to off-peak periods can affect total emissions by changing the speeds at which these trips are taken. Modeling of the emissions impacts of strategies that affect the time of travel requires a knowledge of the speed characteristics of vehicular travel during the peak and off-peak periods (as discussed above).
  • Vehicle type. In addition to fleet-average factors, MOBILE and EMFAC produce emission factors for different types of vehicles, such as passenger cars, light trucks, and heavy trucks. Emission factors are typically higher for heavier classes of vehicles. While some CMAQ strategies, such as traffic flow improvements, will affect all types of vehicles, others, including most travel demand management strategies, will primarily affect passenger car and light truck travel. Emission factors used in the CMAQ analysis should reflect the general composition of the vehicle fleet affected by the strategy.

Example: California Air Resources Board Project Evaluation

In 1995, the California Air Resources Board (CARB) evaluated a number of emission reduction projects funded by state motor vehicle registration fees. The projects analyzed are similar to the types of projects commonly implemented under the CMAQ program. TDM projects were evaluated using transit or workplace-based survey data, in conjunction with the "California standardized methodology" described in this report. TSM projects were evaluated based on before and after travel speeds. Alternative fuel vehicle projects were evaluated based on various assumptions about emission rates and vehicle utilization.

The estimated VOC emission reductions for various types of strategies are shown in Table 1. Average benefits for the 18 projects are roughly 6.0 kg/day for reactive organic gases (ROG)3 and 9.0 kg/day for NOx. The evaluation report illustrates that a range of emission benefits might be expected, even for similar types of projects, depending upon the specific details of the project. While these results illustrate the order of magnitude of emission reductions that might be expected, it should be noted that overall effectiveness may not correspond directly to cost-effectiveness. For example, a project with low total emission reductions could be cost-effective if total costs were also low.

Table 1. Sample Emission Benefits of CMAQ-Type Projects in California

    Average Benefits (kg/day)
Project Type Number of Projects ROG NOx
Alternative Fuel Buses 3 1 36
Bicycle Facilities 2 1 0
Electric Vehicles 2 0 2
Employer Trip Reduction 3 16 14
Telecommunications 3 1 1
Traffic Signal Timing 2 13 2

Transit (Shuttle)

3 6 2
All Projects 18 6 9

Source: California Air Resources Board. "Evaluation of Selected Projects Funded by Motor Vehicle Registration Fees." Sacramento, CA (1995). Annual benefits from this report were converted to daily benefits using an annualization factor of 260, since most projects are worksite-based.

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