Office of Planning, Environment, & Realty (HEP)
Planning · Environment · Real Estate
It is frequently of interest to assess how the range of benefits and impacts of transportation alternatives are distributed across population groups. The distribution of impacts can take various forms and can be measured in different ways. The Civil Rights Act of 1964 provides a legal imperative to avoid discrimination based on race, income, or national origin. Impacts may also be compared across other population groupings, for example, by geography, age, or mobility limitation. An analysis of the distribution of transportation impacts may also compare overall benefits to overall costs and other impacts by population group.
In transportation planning, the distribution of impacts is often a concern for three major types of impacts:
Transportation benefits, including mobility and accessibility;
Transportation costs, including who pays for the services (through user fees, taxes, etc.), and how do the costs paid compare to the benefits received; and
Externalities, including air pollution, noise, and neighborhood disruption.
The development of acceptable and agreed-upon measures of distribution is not always simple. Some complicating factors include:
Because of the aggregate nature of common data sources on population characteristics (such as the census), neighborhood-level population characteristics must generally be used as a proxy for specific groups being examined. For example, if concern is expressed over impacts on minority populations, the impacts are measured for neighborhoods that exceed a certain percentage minority population, rather than for specific minority persons or households.
The definition of unacceptable inequities or "disproportionate" impacts is not always clear. For example, if in evaluating a transportation alternative a low-income group is made better off in absolute terms, but worse off relative to other income groups, is the alternative favorable or unfavorable from a distributional standpoint?
Some factors that affect impact distribution are difficult to forecast, complicating the evaluation of future distributional implications. For example, it is difficult to forecast the geographic location of population according to race, income, etc., characteristics. An additional complicating factor is that locational decisions may be affected by transportation investments. For example, positive externalities (i.e., good transit or highway access) can lead to higher property values and a migration of higher-income people to the area served.
A consumer welfare model is used to estimate per-trip user benefits by income group under alternative scenarios.
San Francisco Bay Area, California
Accessibility of residents to employment is compared for "disadvantaged" versus "non-disadvantaged" neighborhoods for the Regional Transportation Plan versus the no-plan alternative. Statistical tests are applied to measure the significance of differences.
Transportation-related emissions and noise data are disaggregated within a GIS raster (grid cell) environment and their coincidence with population is measured. Exposure is measured by socioeconomic group to develop indicators of equity.
Accessibility is compared by mode (automobile, rail transit, and bus) and across five income groups to analyze the equity impacts of the Tren Urbano rail transit project.
GIS is used in conjunction with emissions and noise models to plot emissions and noise contours around transportation facilities. These are then overlaid on minority and low-income population data to develop measures of environmental justice.
Methods for discussing impacts such as accessibility or environmental impacts are discussed under the relevant impact sections. The following section identifies three techniques that can be used to disaggregate these impacts among socioeconomic groups or geographic areas.
This method compares the distribution of impacts among spatial units such as traffic analysis zones (TAZs) or census tracts, which can be classified by characteristic (low-income, predominantly minority, etc.). A general procedure is as follows:
Classify the spatial units according to the characteristic(s) across which the impacts are to be compared. For example, identify TAZs corresponding to census tracts with greater than X percent population in poverty or racial minority population.
Identify the magnitude of transportation project impacts for each spatial unit. For example, measure the change in accessibility, total emissions, or the concentration of emissions for each TAZ in the analysis area.
Compare the magnitude of impacts among the population groups of interest. For example, compare the average change in accessibility as a result of the regional transportation plan for TAZs with a higher racial minority population with the average change for TAZs with a lower minority population.
If appropriate, apply statistical tests to determine whether differences between alternatives and/or population groups are statistically significant.
Examples of this type of approach include:
San Francisco Bay Area accessibility analysis (see case study): The accessibility of residents to employment was compared for "disadvantaged" and "non-disadvantaged" neighborhoods, under the Regional Transportation Plan versus the no-plan alternative. Accessibility was measured based on travel times from the regional travel model highway and transit networks and on forecast population and employment by TAZ. Statistical tests were applied to measure the significance of differences.
Tren Urbano analysis (see case study): Accessibility is compared by mode (automobile, rail transit, and bus) and across five income groups to help analyze the impacts of the Tren Urbano rail transit project. TAZs are grouped according to average income level. For each group, the average accessibility of residents to employment is calculated based on transportation network travel times.
The approach described above can be facilitated or enhanced through the use of a GIS. For example, emissions and noise contours can be developed from the locations and characteristics of roads and traffic, and these contours can be overlaid on spatial units such as census block groups or tracks. Then, impacts can be compared according to the characteristics of the spatial units. For an example, see the Waterloo case study.
The Surface Transportation Equity Assessment Model (STEAM) is currently being modified by the Federal Highway Administration to assess benefits by zones. STEAM is a post-processor model that utilizes the output of four-step travel demand models and calculates various user benefits and externalities. Benefits and impacts will be assigned to TAZs or user-defined groups of TAZs to allow an analysis of the distribution of changes in impacts.
This approach is similar to Method 1, except that a GIS raster module is used to disaggregate socioeconomic data and impact data to grid cells. This allows impacts calculated for different types of spatial units to be more precisely overlaid on population data. For example, emissions from a transportation network can be assigned to the grid cells corresponding to the network, and then overlaid with population data that is assigned from the census tract level to each grid cell.
Examples of the use of spatial disaggregation to identify the incidence of air quality impacts are provided in the SPARTACUS case study and the Envision Utah case study. The SPARTACUS case study also illustrates the distribution of noise impacts by population and compares the overall incidence of negative impacts across three socioeconomic groups.
Microsimulation travel modeling techniques forecast travel by modeling a set of actual or synthetic individuals or households that represent the population. (A "synthetic" sample is composed of a hypothetical set of people or households with characteristics that as a whole match the overall population.) Full microsimulation of a population is yet to be commonly implemented in practice due to computational requirements. A variant known as "sample enumeration" has been applied successfully in a number of areas, however. Sample enumeration relies on the modeling of behavior for a representative sample of the population.
The benefit of this modeling approach for analyzing the distribution of impacts is that travel patterns, and therefore the travel benefits of transportation improvements, can be tracked across any population characteristic that is included in the sample of persons modeled. Historically, this has been done by income level, since income is commonly used to predict travel behavior. The characteristics of the sample can also be broadened to include race or other characteristics.
Some examples of the microsimulation approach include:
The STEP model, a sample enumeration-based travel forecasting approach. STEP has been applied in the San Francisco Bay Area in a number of studies, and has also been adopted for use in Los Angeles, Sacramento, Chicago, and Seattle. It has been used to analyze travel impacts of pricing scenarios by income group (EPA 1998), as well as for other purposes.
A travel model is currently being developed for the City and County of San Francisco using a sample enumeration approach. The model is being developed specifically with the intent of tracking travel and benefits by race, income, and other characteristics (Cambridge Systematics, Inc., 2000).
The new generation of activity-based travel models (Engelke, 1997) also generally rely on sample enumeration, thus allowing benefits to be tracked by user characteristic. In an activity-based model, travel decisions become part of a broader activity scheduling process based on modeling the demand for activities rather than merely trips. An activity-based model was recently developed for the Portland, Oregon region and will be used in the future as their primary travel demand model (Bowman et al., 1998).
Civil Rights Act of 1964, 42 U.S.C. 2000d-2000d-4 Pub. L. 88-352 - Nondiscrimination in Federally Assisted Programs.
Executive Order 12898 -Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations, February 11, 1994; and Executive Order 12948, Amendment to Executive Order No. 12898, January 30, 1995.
FHWA has issued policy guidance on addressing environmental justice in transportation planning. (Policy Guidance Concerning Application of Title VI of the Civil Rights Act of 1964 to Metropolitan and Statewide Planning, Federal Register, May 19, 2000.) The FHWA Office of Environment and Planning web site contains other recent FHWA memoranda on addressing environmental justice.
