National Academies Press: OpenBook

Effective Methods for Environmental Justice Assessment (2004)

Chapter: Chapter 7 - Transportation User Effects

« Previous: Chapter 6 - Safety
Page 162
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 162
Page 163
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 163
Page 164
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 164
Page 165
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 165
Page 166
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 166
Page 167
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 167
Page 168
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 168
Page 169
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 169
Page 170
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 170
Page 171
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 171
Page 172
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 172
Page 173
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 173
Page 174
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 174
Page 175
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 175
Page 176
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 176
Page 177
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 177
Page 178
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 178
Page 179
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 179
Page 180
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 180
Page 181
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 181
Page 182
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 182
Page 183
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 183
Page 184
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 184
Page 185
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 185
Page 186
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 186
Page 187
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 187
Page 188
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 188
Page 189
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 189
Page 190
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 190
Page 191
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 191
Page 192
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 192
Page 193
Suggested Citation:"Chapter 7 - Transportation User Effects." National Academies of Sciences, Engineering, and Medicine. 2004. Effective Methods for Environmental Justice Assessment. Washington, DC: The National Academies Press. doi: 10.17226/13694.
×
Page 193

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

167 CHAPTER 7. TRANSPORTATION USER EFFECTS OVERVIEW Transportation system changes generally benefit users by reducing travel time, improving safety, and lowering vehicle-operating costs. A transportation system change may also improve the choices available to travelers by offering them different routes or modes of travel at different times of the day. A change can also increase the number of accessible destinations. In terms of environmental justice, the point of interest is the extent to which minority populations or low- income populations would experience these benefits. To understand the distributive effects that would result from a potential transportation project, it is first necessary to examine the performance of the existing transportation service, including how this service varies between members of protected populations and others. Then, a reasonable comparison can be made between the existing service and the new service that would result from a system change. In general, system performance may be measured by the volume-to- capacity (V/C) ratio and by the accessibility of destinations that the affected populations consider important. Thus, the methods presented in this chapter focus on changes in accessibility and changes in transportation choice. Geographic information systems (GIS) are capable of combining and analyzing layers of data about a location and thus are well suited for analyzing distributive effects. A detailed account of applying GIS mapping as part of an assessment is provided in Appendix C. GIS will also be the major method used to assess changes in transportation choice. Accessibility Accessibility is the ability to reach desired destinations. It is related to, but different from, mobility, which is the ability to move. If a population group has limited mobility (e.g., people with low incomes may be less likely to own automobiles), achieving accessibility will require a residential location that is near places where essential activities are conducted, such as work, school, shopping, worship, child care, social services, and recreation. In general, accessibility has two main components: (1) the physical ability to reach a desired destination and (2) the degree of difficulty in reaching it. If a destination can be reached, travel time is the measure most often used to assess the difficulty or ease of reaching it. Travel time is greatly affected by the level of congestion on road segments; by how directly the road system connects trip origins and destinations; and by the standard and condition of applicable road segments. In our analysis of accessibility, we treat vehicle operating costs as a function of travel time, even though a more engineering-oriented approach would take into account pavement surface quality and related variables when evaluating road segment performance. Our primary focus is on travel demand analysis that is specific enough to assess differential effects on protected populations versus travelers in general. We are aware, however, that the process of developing more refined and accurate measures of system performance continues. More comprehensive evaluation

168 models are currently being developed, and some will be operational soon. Thus, we also provide a brief overview of future generation, activity-based techniques for assessing road system performance and accessibility. Transportation choice Closely related to accessibility is transportation choice, which refers to the quantity and quality of transportation options available to residents of an area. Most communities have transportation systems that are strongly auto oriented. Very few options are available for those who either prefer an alternative mode or are not able to travel by auto. Because public transportation planning is beyond the scope of this guidebook, we focus on pedestrian travel as well as non- motorized transportation, particularly bicycling. It is not unusual for a road project to affect, either positively or negatively, the ability of people to use other transportation modes. More specifically, there are at least four reasons why individuals and communities may value having choices among transportation modes: • To help achieve equity goals. A lack of transportation choice limits the personal and economic opportunities available to people who are physically, economically, or socially disadvantaged. Often, such individuals have less access (or less reliable access) to an auto, and so may face barriers to mobility in auto-dependent communities. • To serve as a back-up option for those who can drive. People who do not habitually use an alternative mode may value its availability at some point in the future or in the case of an emergency. Many people can expect to go through periods when they must rely on alternative modes of transportation due to age, physical disability, financial constraints, vehicle failures, or major disasters that limit automobile use. • To increase transportation system efficiency. Use of alternative modes can help achieve certain transportation demand management (TDM) objectives, including reduced traffic congestion, facility cost savings, and environmental quality. • To increase livability. Many people enjoy using alternative modes, such as walking and bicycling or riding the bus, and they value living in or visiting a community where these activities are safe, pleasant, and readily available. Some alternative modes are more prevalent than others, and not every analysis need consider every alternative mode. Public participation and dialogue with local officials can help in the selection of modes that need to be examined. A key element in environmental justice is to ensure that protected populations have mobility that is comparable to that of other populations; this often means that transportation modes other than the auto must be available. New or upgraded transportation facilities may affect the viability of alternate transportation modes in three major ways: • Upgrading roads can increase vehicular traffic. Heavily traveled roads are more likely to be dangerous, difficult to traverse, and unpleasant for those traveling via something other than a motor vehicle. As traffic increases, so does the risk to bicyclists and

169 pedestrians, and some who might have chosen to walk or ride a bicycle before the increase in traffic may no longer be willing to do so. • Street widening can create barriers. Several aspects of road design can affect the quality of nonmotorized transportation choices. Widening road facilities may be a boon to motorists, but for bicyclists and pedestrians (especially for those with disabilities), wider roads can be difficult and dangerous to traverse. • Transportation projects can displace or disrupt facilities. Bicycle trails, sidewalks, and transit stops may have to be moved to make way for other facilities. If so, it is likely that the nonmotorized facilities will be less accessible to at least part of the neighboring community. Even though relocating facilities to areas accessible to more people in total may be a wise thing to do, it can create accessibility problems for people who purposefully chose to live near the original location of the facility. STATE OF THE PRACTICE—ACCESSIBILITY Travel demand modeling is the primary tool for assessing the ability of people living within a particular area of a community to travel to desired destinations. This mode of analysis has been dominated over the years by trip-based models that use a four-step procedure for analysis consisting of (1) trip generation, (2) trip distribution, (3) mode split, and (4) traffic assignment. These models often do a good job of replicating aggregate travel patterns. However, they are limited in their ability to account for the attitudes, values, and constraints that determine travel patterns by the general population, much less specific groups such as minorities or those with low incomes. Activity-based approaches attempt to take into account the interdependences in trip decisions made by groups of individuals. These approaches generally are flexible enough to consider the way household members allocate and share resources and tasks among themselves, and jointly share activities that are dispersed in time and space. In other words, activity-based approaches can be more realistic for the purposes of analyses related to environmental justice (see RDC, Inc. 1995). Nevertheless, four-step travel demand modeling is a very useful tool for gauging road system performance—an essential part of assessing transportation user effects. Thus trip-based models can serve an important role in providing a preliminary analysis of the likely impact a proposed transportation change would have on accessibility by low-income populations and minority populations. Trip-based models The trip-based approach is founded on several assumptions: • The number of trips generated by a household is a function of household size (number of members) and the number of vehicles available.

170 • Individuals always make optimal decisions with respect to their travel arrangements; in other words, an individual can identify and pursue the travel option that would take the least time on any given occasion. • Destinations attract trips on the basis of distance from the trip origin and attributes such as size and attractiveness. • Changes in travel costs to the traveler, such as parking fees and effects of congestion on travel demand, are not usually taken into account. The latter means that these models are not typically sensitive to travel time analysis; literally the assumption is made that trip demand is inelastic with respect to higher costs arising from congestion. Trip-based models present two concerns: (1) nonmotorized transport is not usually taken into account and (2) the models lack sensitivity to chained trips. Chained trips are those that involve multiple stops en route to a destination. These trips are of particular significance when considering project impacts on low-income and some minority populations who may rely on networking more than others for purposes such as child care. By treating trip segments independently, trip-based models fail to reflect that trip decisions made by individuals often are interrelated. On the other hand, the advantages of trip-based models stem from their simplifying assumptions, which allow for the development of standard analysis packages, such as TransCAD, TRANPLAN, and the Urban Transportation Planning System (UTPS) and which make the forecasting procedure affordable to most metropolitan planning organizations (MPOs). The data requirements of trip-based models are less than for activity-based models. Indeed, the simplicity and lower data requirements of these models can be appreciated when making a preliminary assessment of the impact of a transportation project on travel time or congestion levels. Activity-based models In general, activity-based models are still in the developmental stage, but it is likely that they will see increased application in the near future. Testing of early versions of these models has revealed that travel demand forecasts can be developed to treat daily travel patterns in their entirety without breaking them down into individual trips. This is important because attempting to reduce travel to individual trips tends to compromise the interdependencies and continuities that exist across the series of trips made by a given traveler. The testing to date also indicates that activity-based models will be able to predict travel behavior along a continuous time axis and to evaluate specific transportation system changes, such as the impacts of daycare facilities at work, extended transit service hours, or changes in transit lines on travel patterns and demand. Travel Model Improvement Program (TMIP) is developing the Transportation Analysis and Simulation System (TRANSIMS), an integrated system of travel forecasting models that includes a population synthesizer, activity generator, route planner, and traffic microsimulator. The system seeks to create a virtual metropolitan region with a completely disaggregated representation of the population. TRANSIMS simulates the movement of individuals and

171 vehicles across the transportation network using multiple modes. The system can forecast how changes in transportation policy or infrastructure might affect individual trips by time of day. In addition, the model is capable of evaluating impacts on different subpopulations, such as minorities and low-income groups, because it simulates individual travelers, taking into account their demographic characteristics. Highway economic requirements system (HERS) model The Highway Economic Requirements Systems (HERS) model allows you to examine the issue of accessibility from a different perspective than that afforded by the exclusive use of travel demand modeling systems. It gives you the opportunity to assess environmental justice concerns based on the actual and forecasted performance of the road segments used most frequently by protected populations. Performance can be measured in terms of average vehicle speed. The recent innovation of making an interface between HERS-ST (State) and TransCAD makes it possible to analyze the travel experience of members of protected populations as they move between traffic analysis zones (TAZs), particularly for trips to work, school, child care facilities, and other social services, and recreation. The focus here is on the use of HERS at the state level because many MPOs will be using it increasingly for routine analysis of travel behavior. METHODS FOR STUDYING ACCESSIBILITY Table 7-1 summarizes the methods for studying accessibility that we present in this chapter. Before conducting an in-depth analysis of how a transportation project might affect accessibility for protected populations, it makes good sense to conduct a preliminary assessment. This assessment should be simple and should use an off-the-shelf method of analysis. The most efficient approach would be to apply the travel demand model already in use within the agency. Method 1. Unmodified transportation demand models As discussed earlier, transportation planning agencies commonly use four-step travel demand (TD) models, which are capable of measuring travel time between TAZs under varying traffic conditions. By comparing travel time estimates before and after modeling a project’s characteristics, changes in travel time can be assessed. This method is a useful indicator of a project’s impact on trip costs, level of accessibility, and transportation choice. When to use. Standard TD models can be used to obtain a preliminary assessment of changes in travel time or V/C ratios affecting TAZs with relative concentrations of protected populations. This assessment allows you to determine the likelihood that a project would improve or worsen environmental justice within the community by changing the relative accessibility of areas within the activity space of protected populations. Analysis. The starting point for determining the existence of an environmental justice problem lies in identifying those TAZs in which a high proportion of members of protected populations reside. In the analysis, these TAZs will be considered as the origin of travel. Likewise, TAZs that are the common or primary destinations for these special populations are identified, including job

172 and shopping centers, social service agencies and providers (including daycare centers), and schools. Table 7-1. Summary of methods for studying accessibility Method Assessment level Appropriate uses Use when Data needs Expertise required 1. Unmodified transportation demand models Screening Estimate travel demand (TD) between TAZs The project will impact travel demand patterns Medium Standard travel demand modeling; census data analysis 2. Adapted transportation demand models Detailed Estimate travel demand (TD) between census tracts The project will impact travel demand patterns and protected population distribution is uniform within census tracts Medium/ High Standard travel demand modeling; census data analysis 3. Advanced adapted transportation models Detailed Estimate travel demand (TD) between census blocks The project will impact travel demand patterns and protected population distribution is not uniform within census tracts High Standard travel demand modeling; census data analysis 4. HERS-ST model Screening/ detailed Estimate traffic congestion and/or travel cost The project will impact travel cost for protected populations Medium HERS-ST application; TransCAD 5. Activity- based travel simulation Detailed Estimate traffic congestion and/or travel cost Detailed, dynamic analysis of traffic patterns is required or for large or high-impact projects High Advanced modeling tools and techniques 6. Transportation analysis and simulation system (TRANSIMS) Detailed Estimate traffic congestion and/or travel cost Detailed, dynamic analysis of traffic patterns is required or for large or high-impact projects High Advanced modeling tools and techniques The transportation demand model is first run with the data that characterize the current transportation system. The results, either travel time or V/C ratios of road links between TAZs, are recorded. The next phase of analysis involves running the model again, but this time with the data that embody the intended transportation project. The focus, as before, is on the times or V/C ratios for travel between principal origin-destination (O-D) pairs by protected and other groups. Data needs, assumptions, and limitations. The data requirements for analyzing differences and changes in travel time and V/C ratios consist of demographic data such as: • Household size; • Number of persons in household of working age; • Household income and availability of vehicles;

173 • Nonresidential land use data that include number of employees, floor area, and retail sales; • Zone data such as population density and distance from central business district or other business centers; and • Data about designed highway capacity. Departments of transportation collect most of these data in the course of building TD models. As mentioned previously, TD models are based on simplifying assumptions that do not accurately depict factors such as trip-chaining, and so are limited in their ability to account for the relationships at work in human travel behavior patterns. Moreover, the results may be significantly skewed by estimates of economic activity, land use, and people’s propensity to travel, all of which are approximated in the model. Results and their presentation. Whether using travel time or V/C ratios, a comparison is made of the results obtained on trips between origin and destination TAZs for protected and other populations under existing conditions of the network. If the comparison reveals that travel times or V/C ratios related to protected populations are typically greater than for other groups, it may be concluded that low-income and minority groups are most likely carrying a disproportionate burden of transportation-related costs; and therefore an environmental justice problem may exist. Of course, if there is no significant difference, there is probably no environmental justice problem. One note of caution in presenting the results: because of the aggregate level at which the analysis is conducted and the difficulty of definitively knowing which road segment(s) were used, you would only be able to say, for example, “About 95 percent of trips from zone 1 to zone 2, representing protected population groups, experienced a reduced/increased travel time or V/C ratio.” This percentage is based on the proportion of the population in zone 1 who are members of protected groups. Assessment. Caution should always be used in drawing conclusions from the results of this type of analysis because of the underlying simplifying assumptions with respect to the factors that influence the choice of mode, the impact of various public policies on people’s travel patterns, and the relationship between land use and mode choice, among others. In short, the results should be viewed as crude and should be interpreted as indicating only the likelihood of an environmental justice problem even when the magnitude of the changes in travel time or V/C ratios is significant. In such a case, a more detailed analysis is required. Method 2. Adaptation of transportation demand models By making use of TAZs, TD models allow you to take advantage of the demographic data contained within them to enhance the analysis. TAZs typically are aggregations of census tracts and may be redefined based on the presence of protected populations within zones before the model is run. When to use. These models are appropriate when the preliminary analysis indicates that a more accurate method of estimating changes in travel-time costs is needed. Though more costly in terms of time necessary to redefine TAZs, this remains a relatively inexpensive method because

174 it does not require new computer software. This method is suitable for small- to medium-sized projects. Analysis. The first step in redefining TAZs is to identify which zones contain the residences of members of protected groups. The second step is to dissect TAZs into units that are smaller groups of census tracts based on the relative presence or absence of protected and other populations. TAZs should be configured so as to be as homogenous as possible in terms of income and/or race. The third step is to identify the destination TAZs to which households of protected populations are routinely attracted, such as job and shopping centers, social service agencies and providers (including daycare centers), and schools.1 The transportation demand model is first run with the data that characterize the current transportation system and the travel times on road links between origin and destination TAZs. The next phase of analysis involves running the model again, but this time with the data that characterize the intended transportation project. As before, a record is made of travel times between principal origins and destinations by the respective kinds of groups. Data needs, assumptions, and limitations. The data required for this analysis are the same as for other routine analyses using the TD model. This includes demographic, nonresidential land use, zone, and road data as mentioned in the description of the preliminary assessment method. Again, departments of transportation routinely collect most of these data in the course of building TD models. The limitations are the same as with the unmodified TD models; the simplifying assumptions that the models are based on leave out factors such as trip-chaining and so are limited in their ability to account for relationships among travel behavior patterns. As with unmodified models, results may be skewed by estimates of economic activity, land use, and people’s propensity to travel, all of which influence the model. Estimating travel-time savings is a challenge because of the significant issues involved in attaching economic value to travel time. Researchers have yet to agree on the following: • What fraction of the wage rate should be used for work-related travel. • What fraction of the work-related travel rate should be used for personal or nonwork- related travel. • What fraction of a driver’s hourly time value should be assigned to passengers in the vehicle. • Whether a lower time value should be used for commuting trips that are shorter than the common travel-time budget (i.e., the amount of time people are willing to spend journeying to and from work) and a higher value for the time increments that exceed this budget. 1 TIGER/Line data based on the 2000 census are currently available from the U.S. Census Bureau regarding the location of employment centers (including shopping and major retail centers; industrial buildings/parks; office complexes/parks; government centers; and major amusement centers), educational and religious institutions, and transportation terminals.

175 • Whether the same time value should be applied for very short periods of time saved (e.g., 30 seconds) as for longer periods (e.g., over 5 minutes). • How to take into account variation in time en route and hence unreliability of arrival time. • How to include changes in travel time for pedestrians, cyclists, and others using nonmotorized transportation modes. Completely overcoming these challenges is beyond the scope of this guidebook. The choice of method used to estimate travel-time savings is influenced primarily by how much detail is perceived to be necessary to make a decision about a project alternative. For many small projects, the preliminary assessment described earlier using travel demand models will prove adequate. For more extensive projects, or those where a reasonably strong likelihood exists that the benefits and costs of the project may raise questions of equity, a more accurate and detailed estimation of travel-time savings is needed. Results and their presentation. The travel times obtained on trips between origin and destination TAZs for protected populations are compared with those for other populations under existing conditions of the network. The analysis is then repeated with the transportation improvement in place. As in the preliminary assessment, if the comparison reveals that travel times of protected populations tend to be significantly higher than those of other groups and that the project would do little to reduce the disparity or even worsen it, the conclusion may be drawn that an environmental justice problem currently exists. As in the case of the preliminary assessment, the results should be presented in terms of trips originating in each applicable TAZ in comparison with all TAZs. Assessment. Although the results acquired using this method reflect a greater degree of accuracy than the preliminary analysis, it too is quite aggregate. As such, it can provide a general sense of the extent to which travel times to important destinations would improve or worsen for protected populations. It also can be used to compare such changes with those of travelers in general. If unfavorable results emerge relative to environmental justice, more detailed analyses will be required. Method 3. More advanced adaptation of transportation demand models This method is an advance on the previous one and disaggregates the applicable TAZs using census-block-group data instead of tract data. As before, the TAZs are redefined based on the presence of protected populations within zones before the model is used to determine travel times between analysis zones. When to use. This method is appropriate when a more accurate assessment of changes in travel time is needed than that afforded by the preliminary analysis or tract-level analysis. This method will be more costly because the tract data have to be replaced by block-group data. The method is suitable for small- to large-scale projects and is particularly useful for achieving relatively high accuracy in determining the probability of an environmental justice problem using TD models.

176 Analysis. To redefine and prepare the TAZs for use, the four steps outlined in the previous method are followed, but with block group data as the basis. Care must be taken to ensure that the TAZ geometry matches the network geometry. As before, the TD model is first run with the data that characterize the current transportation system and the travel times on road links between origin and destination TAZs, then run again with the data that characterize the intended transportation project. Data needs, assumptions, and limitations. The data required for this analysis are essentially the same as for other routine analyses using the TD model with the exception that the demographic and nonresidential land use data are at the census block group level instead of the census tract level. Census block group data, including those on population density, can be downloaded from the U.S. Census Bureau Web site; zone and road data are the same as mentioned in the description of the previous assessment method. Departments of transportation routinely collect data for modeling in the course of building TD models. The same limitations described previously affect the reliability of results for this approach. Results and their presentation. The travel times obtained for the trips between origin and destination TAZs for protected populations are compared with those from other populations under the existing network conditions. As in the preliminary assessment, if the comparison reveals that travel times of protected populations are consistently significantly higher than those for other groups, then low-income and minority groups are likely carrying a disproportionate burden of travel time costs; and therefore an environmental justice problem exists. Assessment. These results are about the most accurate that can be obtained using a traditional TD model. Nevertheless, they still represent a probability of occurrence, even though we may express it with greater confidence. This level of confidence may be adequate for most small- to medium-sized projects, but larger projects and highly sensitive projects may require the use of a of model that provides an even higher level of accuracy. Method 4. HERS-ST model Aggregate models, such as the HERS model, often are less expensive to use than more disaggregate models. Much of the necessary data for these models is routinely collected and updated by states and maintained by the FHWA in the Highway Performance Monitoring System (HPMS) database.2 The HPMS database does not, however, routinely include data on urban and rural local roads, nor on rural minor collectors, as indicated in Figure 7-1. These categories of roads are important in assessing environmental justice concerns because low-income populations and minority populations are likely to use them, and their performance would have an impact on travel costs for these protected populations. The data problem is partially resolved by HERS-ST, which treats the performance of these roads in terms of changes 2 The FHWA and the states, beginning in 1978, jointly developed and implemented a continuous data collection system called the Highway Performance Monitoring System (HPMS). Currently, the HPMS contains more than 110,000 sample sections, the most comprehensive nationwide data system available regarding the physical condition and usage of the nation's transportation infrastructure.

177 in vehicle speed or level of congestion in one of the submodels. Figure 7-2 is a graphic representation of the HERS model. FHWA uses HERS to project needs for these highways KEY Arterials Arterials Urban roads Rural roads Collectors Collectors Local roads Local roads FHWA uses non-HERS methods to project needs for these highways Urban interstates Rural interstates Rural major collectors Rural local roads Rural minor collectors Rural other principal arterials Rural minor arterials Urban collectors Urban local roads Urban freeways and expressways Urban other principal arterials Urban minor arterials Figure 7-1. FHWA’s road classification system Source: U.S. General Accounting Office 2000. It is important to note that an interface can be created between HERS-ST and TransCAD. This enables you to identify those segments of the road network, including the urban and rural roads that are most likely to be used by members of protected populations. These segments often represent the probable routes between the origin TAZs and destination TAZs for the most essential trips made by such populations. The TAZs may be defined using either census tract or block-group data, but using the latter provides more detailed and accurate information, as noted earlier. When to use. The HERS-ST model is best suited to estimating changes in average vehicle speed or levels of congestion, as it takes into account factors such as traffic volume, pavement condition, and lane width. When this capability is coupled with the GIS-based TransCAD, the model becomes a reasonably accurate measure of how the existing road network affects protected populations and how the intended improvements will alter that. Of course, additional costs are incurred with this increased proficiency and must be weighed against the size, cost allocation and social and political significance of the project.

178 Evaluate and select improvements for implementation Estimate costs (travel time, operating, safety, emissions, etc.) Forecast pavement condition Forecast vehicle speed Travel forecast Estimate improvement costs Figure 7-2. Simplified representation of the HERS model Source: U.S. General Accounting Office 2000. Analysis. In the data analysis of urban and rural local roads and rural minor collectors, HERS- ST clumps together lower rural classified roads with rural major collectors and lower classified urban roads with urban minor collectors. In this approach, the derived output information on these roads is separated from the rest prior to reporting. Another approach would be to analyze the lower classified roads separately from the rest of the system once the induced deficiency and cost data are appropriately adjusted prior to conducting the analysis. As in the methods using travel demand models, defining the TAZs at the block-group level is an option, but if the choice is made to redefine them, every effort must also be made to ensure that each TAZ’s geometry matches that of the road network. In addition, the interface with TransCAD provides data on the volume of traffic traveling on each road segment, which it tags with a unique identifier. This identifier is what makes it possible for TransCAD to interface with HERS-ST, and the traffic volumes relayed to the HERS model via this mechanism are incorporated into its computations. HERS-ST consists of a number of submodels with the output of one becoming the input for another (U.S. General Accounting Office 2000). The travel forecast submodel projects traffic growth, and it utilizes current traffic volume data, along with data related to the cost of travel—travel time, safety, and vehicle operating costs—and combines them with the state’s projection of traffic growth and with a measure of the price elasticity of travel demand. The

179 output of this sub-model is the input to the pavement condition submodel and, subsequently, the vehicle speed sub-model. Thus, the change in average vehicle speed is an important measure because it not only reflects changes in the volume of traffic and associated congestion, but also the quality of the road surface, which is subject to wear-and-tear effects. Furthermore, changes in average speed, which can be measured for each road segment, including those most frequently used by protected populations, are key to assessing whether an environmental justice concern exists. The model begins by assessing the current condition of the highway segments in the data sample. Average speeds under existing conditions may be first compared with those for segments used by members of other groups, followed by a similar comparison of forecasted values that reflect expected changes that would result from the intended project. Data needs, assumptions, and limitations. HERS-ST requires data on average annual daily traffic (AADT), highway capacity, pavement condition, and lane width. Most of these data are also readily available from the HPMS database. Moreover, HERS-ST provides the option to substitute more accurate local data, where feasible. Additional effort will be needed to gather more specific data on urban and rural local roads, as well as on rural minor collectors. If the decision is made to redefine the TAZs in TransCAD using data at the block group level, the applicable data must be obtained from census files. This model assumes that the forecast for each road segment represents the level of use that will occur if a constant level of service is maintained on the segment. There is also an implicit assumption that the model captures the net effect of all changes in the transportation network and the economy through its assumed price elasticity of travel demand. In addition, the model assumes that all roads face the same weather conditions. Some of these assumptions give rise to the model’s limitations, which are stated below (U.S. General Accounting Office 2001). • Because it analyzes each road segment independently rather than the entire network as a whole, it does not completely account for the interrelationships between all segments and different transportation modes (e.g., how traffic is redistributed as improvements are made). • It does not fully account for the uncertainties associated with its methods, data, and assumptions. For example, the model uses the price elasticity of demand for travel to incorporate information on how changes in vehicle user costs affect travel; there is thus an implicit assumption that the model captures the net effect of all cost changes in the transportation network. The overall economy is thus assumed to remain constant because its condition affects consumer choice. • The accuracy of estimates generated by HERS-ST is uncertain because the model uses data that vary in quality. For example, the state-supplied data on pavement roughness vary significantly in quality because different states use different devices and approaches to measure it. In addition, some data used in the model, such as pavement resurfacing costs, are usually outdated. Users may exercise the option of using more accurate, local construction data. • The model uses information to project the future condition of the road pavement, which does not take full account of environmental conditions that affect highways. For example,

180 the assumption is made that all road segments experience freezing and thawing conditions, while this is not the case in the warmest parts of the country. Results and their presentation. Changes in average speed on the various road segments are generated by the model and may be displayed in tabular form or reflected in maps generated by TransCAD, which receives the output of HERS-ST via a routing system and dynamic segmentation process. Maps can also be used to display the location of the intended improvements to in-house staff or to policymakers. Assessment. The HERS-ST model differs from the national-level HERS model in significant ways that can be an asset to the analyst. First, it allows the user to override some or all of the improvement decisions generated by the model. For example, users can specify the type of improvement they see fit for any segment of the highway in any funding period, whether or not the specified improvement is economical. FWHA contends that this capability gives users the opportunity to apply specific knowledge of a particular condition. Second, as implied earlier, the HERS-ST can analyze more classes of roads and provide a higher level of detail in its results with respect to every segment analyzed. Third, this model permits the substitution of more relevant state data for national-level data so that local conditions may be modeled more accurately. Finally, this model provides the user with the option of analyzing a statistical sampling of highways drawn from the HPMS database or analyzing all segments of the state’s road network. Method 5. Activity-based travel simulation A new set of travel forecasting and analysis procedures based on travelers’ daily activity patterns is being encouraged under the TMIP. TMIP is an attempt to satisfy the need for more accurate and sensitive travel forecasts and to facilitate better-informed decision making on transportation matters. Activity-based simulation models of human activity and travel behavior contain several modules. These modules enable the researcher to combine stated and revealed preference data along with baseline activity patterns, network and land-use data, and socio-economic and demographic data. Not only does this type of model check the network data for logical consistency and missing information, it also assesses whether a modified travel pattern is feasible, based on a human adaptation and learning module. Behavioral responses are captured by the statistics accumulator within the evaluation module, which provides descriptive and frequency statistics about vehicle miles traveled, number of trips by mode and time of day, number of stops by purpose, trip chains, vehicle occupancy, and travel times by trip purpose, among other classifications. Because this micro-simulation approach does not rely upon over-simplifying assumptions, it does not reduce the complexity and realism of the response and adaptation patterns of the travelers being modeled. As a result, the model is capable of providing highly accurate analysis of travel-time savings compared to most currently available models. As might be expected, this improved accuracy comes at a relatively higher cost. Further detail on this sort of model may be found in RDC, Inc. (1995). When to use. Activity-based simulation is most appropriate when the project to be implemented is costly. It also is suitable when a relatively high level of precision is needed to determine the

181 travel-time savings that would occur in various areas of the community if the project were implemented. Analysis. One significant advantage of this type of model is that it permits a dynamic, longitudinal analysis of travel behavior, as opposed to the static, cross-sectional analysis afforded by the traditional four-step demand models. This means not only that behavior is examined over a continuous time frame but also that impacts originating within and outside of the transportation system can all be evaluated together. As a consequence, people’s entire daily itinerary is the focus of analysis, rather than individual trips. In addition, whereas evaluation has traditionally been based on capacity and level of service, this approach evaluates the impacts of transportation policy measures and projects based on time-use utility, which is represented by the daily time- use patterns of the target population. Data needs, assumptions, and limitations. Because this type of model focuses on the entire daily itinerary of travelers, it requires considerably more data than traditional models, a factor that contributes significantly to its running costs. In addition, it uses response data that must be gathered by means of a survey. Thus, the magnitude of potential benefits from its use should be carefully weighed against the costs. Nonetheless, many of the data requirements are similar to those of four-step models and may be obtained from most MPOs. These include data on TAZs, including network system and travel time, mode choice, trip distribution, and land use inventory. Demographic and socio-economic data by TAZ, such as household size, vehicle ownership, income, and race (white and nonwhite categories) are also needed and may be obtained from the Census Bureau. Original data needed include information from trip diaries for the revealed preference analysis. If the project being evaluated requires a change in TDM strategies, the type and characteristics of these strategies can also be input. To do this, however, a survey must be designed to collect stated preference data in the form of potential responses to the anticipated impacts or policy changes. The same survey can also be used to gather information to complement that received from trip diaries, such as tradeoffs between parking costs and walking distance. Activity-based simulation models are based upon the assumption that travelers engage in “satisficing” behavior (making appropriate choices with limited information), as opposed to always making optimal decisions or decisions that always maximize their utility in the purest understanding of the concept, as is typically assumed in traditional models. This satisficing assumption more appropriately reflects the reality of day-to-day living in a world where individual travelers do not have perfect information of events and concerns that affect their decision making. In other words, most travelers often make decisions with the intention of “making do” with the current circumstance, and this will be reflected in random or stochastic travel behavior because factors and constraints will affect persons differently. Moreover, the model assumes that the marginal utilities of travel vary across people, modes, and environmental conditions encountered, and that route choice preferences vary according to socio-economic characteristics and perceptions of individuals. Both of the latter assumptions impact the individual’s valuation of time and allow for the differential analysis of travel-time savings across income and racial groups. The model’s limitations derive from the fact that it is still in development.

182 Results and their presentation. Activity-based models can be configured to generate descriptive statistics, and they are also capable of carrying out statistical tests and providing statistical analyses in the form of response distributions. They can also cross-classify these response distributions against socio-economic and demographic variables, which allows a level of disaggregation that facilitates the application and assessment of environmental justice criteria. Detailed results can be presented in tabular and graphical formats that are easy to comprehend. Assessment. This form of model has many capabilities and has the potential to provide accurate analysis of investment and demand management policies. The level of financial and technical support that the FHWA has devoted to this process reflects its commitment to fully developing activity-based simulation models and making them widely available. A major benefit of this form of model is its potential for giving relatively accurate valuations of travel time savings. A certain expertise will be required to design a survey instrument capable of eliciting the necessary information. The goal should be to make questions as simple as possible and yet clear enough to obtain the required data. One approach to such a survey design is that used in the Adaptive Stated Preference survey instrument (Richardson 2001, p. 13). Of course, all survey questions should be pilot-tested to ascertain whether the target population is able to comprehend them fully. Realistically, it is highly unlikely that an agency would develop so ambitious a model solely for assessing environmental justice implications of a project. Rather, this type of model is most likely to be developed to meet an agency’s general needs for travel demand analysis. In such a case, it is feasible to enhance the modeling effort to provide a first-rate capability to evaluate the environmental justice effects of almost any significant transportation project. Method 6. The Transportation Analysis and Simulation System The TRANSIMS is an activity-based travel demand model that functions as six integrated modules, along with a feedback selector/iteration database. The feedback mechanism is the primary modeling tool as it functions to achieve consistency among the various computational modules (Los Alamos National Laboratory and Price Waterhouse Coopers 2002, p.3). This mechanism is critical to simulating decision/choice responses of individuals to events such as accidents, closure of a segment of highway, or interruption of transit service that occur directly within the transportation system; it is also helpful in evaluating policy alternatives that affect the use of an entire transportation system, even though the policy may be targeting a particular segment of the system. TRANSIMS simulates the movement of individuals and vehicles across the transportation network and can also forecast how changes in transportation policy or infrastructure might affect individual trips by time of day. The results of the simulation are aggregated only after the activities have been set, the trips routed, and the entire set of individual trips simulated in the presence of all other travelers. Because of this capability, TRANSIMS promises a substantially expanded scope of analysis along with improved analytical ability, particularly when evaluating the impacts of potential transportation projects on different populations. One of the first requirements to make this model operational is the creation of a detailed network that represents the future transportation infrastructure. Infrastructure includes signs, signals, streets, highways, and transit information, along with information about where activities (e.g., residential, commercial, and recreational) will occur and where parking lots will be located. This

183 network supplies data to all the modules. Figure 7-3 depicts the primary modules in the center row; each is dependent on external data inflows, which are shown on the top line. The data produced by each module, indicated in the bottom row, becomes the input for other modules. The population synthesizer creates a synthetic population of households and individuals that are distributed both geographically and demographically according to the input data related to the metropolitan area under study. Vehicles are also assigned to households and individuals according to the input data. This synthetic population then interacts with the other modules. The first of these is the activity generator, where an activity list is constructed for each individual in a household by matching his or her demographics against information gathered from household travel and activity surveys. At this point, the synthetic population has places to go, and the means of going to those places are supplied by the route planner module. This module computes the fastest route to each activity by each individual based on the activity information and trip plans supplied from travel diaries and stated choices of transportation mode. Mode choice is also accomplished within the route planner module using external functions, such as logit and travel cost functions. In addition, shared rides, in which the passenger and the driver are from different households, are accounted for by this module as long as information related to the dependency is recorded with other household information in the population synthesizer module. Census In pu tf ile s In pu t & o u tp ut file s M od ul es Population forecast Synthetic population Population synthesizer Activity generator Route planner Traffic micro- simulator Emissions estimator Output visualizer Traveler plans Simulation output Emissions inventory MODELS3 database Traveler survey Transit Network Activity Vehicle Air quality surveys Arbitrary box data Figure 7-3. The TRANSIMS architecture from the perspective of data flow Source: Los Alamos National Laboratory and Price Waterhouse Coopers 2002. The traffic microsimulator module processes the output of the aforementioned modules, causing the synthetic individuals to interact with one another and realistic features of the traveling environment. This module simulates the movement of individuals throughout the network, including their use of private vehicles and public transportation, and the high level of realism in the simulation is directed by the selector/iteration database, which utilizes an iterative process and feedback mechanism. The next step in the process, calculating vehicle emissions, is not

184 pertinent to our analysis of transportation user effects; but the one following, the creation of a visual representation of the model’s output, is of particular interest. When to use. This model is most useful when the situation requires a high degree of accurate information regarding the impact of a proposed project on protected populations. Obtaining such accuracy is relatively more expensive compared to other methods, but because this type and standard of modeling is in demand due to federal requirements for other information and decision making, the use of TRANSIMS may become widespread in the not-too-distant future. Analysis. The special ability of this model to simulate the travel of an individual over an extended time period, beyond peak periods and for travel other than commutes to work and other basic activities, means that the potential for more thorough comparative analysis is greatly enhanced. For the purpose of assessing environmental justice, the most critical stage is gathering and inputting accurate demographic data in the population synthesizer. Such information determines how the individual is going to travel across the simulated transportation network and, hence, the quality of the model’s output. While current use of TRANSIMS does not require demographic data about race, this must be included to facilitate environmental justice assessment. Furthermore, the matter of trip-chaining, which often is an important part of low-income people’s travel itinerary, is dealt with explicitly by the model within the activity generation module—a discrete choice-based model that generates trip chains along with activity locations using the data related to the synthesized householder’s travel itinerary and domicile location. The operation of the traffic microsimulator module gives this model an important advantage over traditional demand models because it is capable of simulating multiple travelers per vehicle and multiple trips per traveler, both factors that are fairly common to low-income and minority travel routines. Another key feature that facilitates determination as to whether an environmental justice problem will exist is the output visualizer module. The module allows the user to select for display any data value of interest that can be drawn on any link of any size on a given network. Because TRANSIMS is a completely disaggregate system, much care is required in calibrating and applying mode choice. Data needs, assumptions, and limitations. Much detailed information related to individual travel is required by this model, so building the database can take considerable time. In addition, the data need to be location-specific for the model to be most useful, so considerable data have to be gathered at the local and regional level. The bulk of the data is keyed into the population synthesizer and consists of geographic and demographic information at both census tract and block-group levels. TIGER/Line layouts of census tracts and block groups make up the geographic data, while summary tables (STF-3A), and public use microdata area (PUMA) samples are obtained from the U.S. Census Bureau. TIGER/Line data are used to build the transportation network, which must be able to reflect the location of workplaces, shops, stores, schools, daycare and recreation centers, hospitals, and other areas identified from household activity surveys, along with parking lots. Other network data include number of lanes, streets, freeways, highways, ramps, turn pocket lanes, and intersections (with and without traffic signals). It is important to stress that in order for the model to provide a predictive output, a

185 forecast marginal demographic file consisting of race, household size, income and age data based on census tract and block-group data must first be generated before it is keyed into the synthesizer. The master area block level equivalency/geographic correspondence engine (MABLE/Geocorr)3 is also utilized to generate a link between the PUMA samples and census blocks. Because the population synthesizer assigns individuals to activity locations, household travel and activity surveys (including travel diaries) are important sources of information about the types of activities individuals engage in (e.g., work, school, and shopping) as well as the start, stop, and travel times associated with them. Trip-chaining activities, including stop and start times, must also be included for origin-destination travel because the traffic microsimulator chains together several legs to form a trip. In fact, data on network travel times and activity locations are essential elements that allow the model to select a likely location for each activity, and each location’s relative attractiveness is computed using criteria such as the number of retail employees or the amount of retail-store floor space. Unlike conventional travel demand models, TRANSIMS is not merely concerned about peak- hour activity, so travel diaries should cover an extended period of about a month to ensure that a reliable trend can be established. Information on whether individuals walk, use private vehicles or transit, or use any other mode of transportation is also gathered from the surveys. Transit data, such as route paths, terminals, and schedule of stops, are part of the required network data. This model assumes that the traveler always makes rational choices and so takes the route and uses the mode of transportation, including walking, that yields the shortest time between two points, while taking into account any situation or obstacle that may cause delay on any of the possible routes. For example, if the input information is that the traveler walks to and from work, the model will subsequently compute all work-related activity as accomplished by walking unless programmed otherwise. In other words, the travel cost function of the synthetic traveler is based on a predetermined, user-defined cost structure obtained from the survey data. On the other hand, if the information reflects that the individual drives his own vehicle and sometimes walks part of the distance, or takes transit, the model assumes that if the delay using motorized transportation would cause the time traveled between two points to be longer than it would take to walk, then that individual would, in fact, decide to walk. Though the model structure may not reflect all decisions made in reality, this should not be regarded as a serious limitation because it is applied without bias. Like other methods of forecasting, the assumption is made that once the characteristics of the surrounding infrastructure are recorded and there is no subsequent change, the forecast year behavior of the traveler is the same as that captured in the base year. Any change in infrastructure, such as changes to a roadway or to the level of transit service, and the area’s population are assumed to be reflected in the base-year calibration function. Changes in such things as travel time, transit fares, and parking costs are assumed to affect modal choice. The intensity of the impact on an individual traveler depends on the nature of the demographic data input obtained from the Census Bureau 3 MABLE/Geocorr supports data maintained by the Center for International Earth Science Information Network (CIESIN) at Columbia University. See http://plue/sedac.ciesin.org/plue/geocorr.

186 and from the surveys. There is, therefore, an implicit assumption that the cost functions in the model reflect the thought processes of the traveler. TRANSIMS is one of the new generation of behavioral travel models and as such is still under development. As its validation process continues, much optimism has been expressed about its capabilities. However, one of the limitations we observed is that it is not sensitive to certain geometric factors, such as lane width, and the length of both acceleration and deceleration lanes. In addition, the microsimulator has been found to be inaccurate in predicting the velocities of individual vehicles along weaving sections of highways.4 Results and their presentation. As long as a region is defined by a set of vertices, user-selected data can be drawn, and this capability facilitates the display of data aggregated into regional areas. Users can manipulate three-dimensional objects using the Output Visualizer’s graphical user interface. Getting the Output Visualizer to generate output is facilitated by an extensive, user-friendly system of menu options. Moreover, by setting a configuration file key, this module can be run remotely to produce images that may be incorporated into reports, presentations, or motion pictures. Assessment. According to the FHWA, TRANSIMS will enable planners and citizens to have a better understanding of the effects and implications of transportation policy choices. It provides planners with the means to evaluate proposals to enhance the serviceability of the highway system, as well as transit, bikeways, and pedestrian amenities. The FHWA further surmised that the fine level of detail afforded by the software would not only more accurately represent the impact of transportation movements on travel, driving, and air pollution emissions but would also aid in the assessment of the socio-economic impacts of proposals for improvements (Public Roads 2000). The latter capability underlines the importance of this model as a valuable tool in assessing environmental justice concerns in the foreseeable future. Models such as TRANSIMS, however, have large data requirements and therefore would require a major commitment of resources by an agency. STATE OF THE PRACTICE–TRANSPORTATION CHOICE Equity is perhaps the most important goal served by increasing transportation choice. Some members of a community may not be well served by the automobile-oriented transportation systems prevalent in most U.S. cities. Lower-income populations, children, and people with disabilities are often particularly sensitive to restricted transportation choice because they tend to walk and cycle more than average and are more vulnerable to barriers and risks. Transportation disadvantage refers to people who face significant, unmet transportation needs. The four attributes below are key determinants of whether an individual or group is transportation disadvantaged: 4 Weaving is the crossing of two streams of vehicular traffic traveling in the same direction along a significant length of highway without the aid of traffic control devices. Capacity is significantly reduced in these weaving areas because drivers from two upstream lanes compete for space to merge into a single lane and then to diverge into two different streams.

187 • Nondrivers – People who cannot drive or do not have access to a motor vehicle. • Low-income persons – Drivers and nondrivers whose basic transportation needs are significantly constrained by financial limitations, especially out-of-pocket costs. • Disabled persons – People who have physical disabilities that limit their ability to travel independently. • Automobile-dependent people – People who live in a community with automobile- dependent transportation and land use patterns. A person with any one or two of these attributes is not necessarily transportation disadvantaged. For example, individuals who use a wheelchair are not transportation disadvantaged if they can afford an automobile and chauffeur or can drive and live in a community with good universal access (i.e., one designed to accommodate people with a range of needs, including wheelchair users, people with visual disabilities, and pedestrians pushing strollers or handcarts). On the other hand, the greater the number of these attributes a person has, the more likely he or she is to be transportation disadvantaged. Obtaining information on the number of people with attributes associated with being transportation disadvantaged may be difficult. Table 7-2 describes some indicators that may be used when more specific data are unavailable. There is considerable overlap among these categories. One should try to identify the number of residents who have multiple attributes, such as lower-income, employed, single parent, and low-income with disabilities. Table 7-2. Indicators of transportation disadvantage and possible data sources Indicator Data sources Households that do not own an automobile (sometimes called zero-vehicle households) Census, NPTS, consumer surveys, and local transportation surveys People with significant physical disabilities Social service agencies and special surveys Low-income households Census, household, and labor surveys Low-income single parents Census, social service agencies, and special surveys People who are too young or old to drive Census and other demographic surveys Adults who are unemployed or looking for work Census and labor statistics Recent immigrants who cannot drive Census, social service agencies, and special surveys Note: NPTS is the National Personal Transportation Survey, available at http://www.bts.gov. Although not everybody in these groups is transportation disadvantaged—and not everybody outside of them has their mobility needs satisfied—these populations may be used as surrogates if better data are unavailable. Table 7-3 suggests which modes tend to be particularly useful for various user groups.

188 Because their transport options are constrained, people who are transportation disadvantaged can be seriously affected by even relatively small changes in transportation systems. For example, low-income nondrivers may be highly dependent on a particular walking path or transit route. Changing that route may have major repercussions on their access to destinations important to them. To the greatest possible extent, it is important to use data collection and analysis methods that can identify such effects. Occasionally, this may require different analysis techniques than are used in conventional transportation planning. Table 7-3. Modes that are particularly important for specific user groups Mode Non- drivers Low- income persons Disabled persons Commuters Walking A A B B Bicycle A A — B Taxi A B B — Fixed-route transit A A B A Paratransit B A A B Automobile — B B A Ridesharing B A B A Note: A = primary mode; B = potential mode. A preliminary, qualitative analysis of a project’s effects on transportation choice should be conducted for all projects. Relatively detailed analyses are useful whenever a project: • Widens an existing road; • Is expected to increase traffic volumes; • Eliminates or moves a transit stop, trail, sidewalk, or other nonmotorized facility; • Reduces the shoulder width of the road or adds shoulder rumble strips; • Increases the length of city blocks; • Increases the number of driveways that intersect nonmotorized facilities; and • Increases the incline of pedestrian or bicycle facilities. In most cases, an understanding of the transportation choices available within a community provides vital information for cities and regions trying to enrich the opportunities for non- motorized transportation as part of their demand management goals. The following four general steps are suggested for analyses of the extent to which protected populations have a choice of transportation modes and services:

189 Step 1 – Define the study area. As with the other analyses presented in this guidebook, it is important to take a critical look at the neighborhoods and infrastructure surrounding the proposed project and to determine which, if any, are likely to be affected by it. A geometric change in a roadway, for example, may affect transit routes well beyond the location of the change. Step 2 – Perform a preliminary inventory of the modes (both motorized and non- motorized) and facilities available in the study area. Site visits, combined with reviews of sidewalk, trail, and transit maps, can be used to inventory modes and facilities that the proposed project may affect—either positively or negatively. Nonmotorized travel data may be available from existing travel surveys and traffic counts, although conventional sources such as these tend to under-record nonmotorized trips. Some data sources exclude nonmotorized trips altogether, and many undercount short trips, nonwork trips, travel by children, and recreational trips. Automatic traffic counters may not record nonmotorized travelers, and manual counters are usually located on arterial streets that may be less used by cyclists than are adjacent streets with lower traffic. For these reasons, special efforts are usually required to obtain the information needed to evaluate nonmotorized travel. Whenever possible, the data should be geocoded and incorporated into a GIS. This makes it easy to create maps that integrate various types of data (such as roadway and sidewalk conditions) with the demand for nonmotorized travel to identify areas where effects might be greatest. Step 3 – Examine the demand for alternative modes. This step involves estimating how many people use (or want to use) alternative modes of transportation. Applying one (or a combination of) the methods presented in this section, one assesses how many people are likely to be directly affected by changes to the availability and usability of modes other than the automobile. If surveys are used, it may be possible to estimate how people value transportation choice as part of the community, even if some residents currently do not use alternative modes. Step 4 – Evaluate how mobility would be affected by a project. Depending on the scope of the assessment, an analysis of the use and safety of alternative modes of transportation may range from a qualitative assessment of the project’s impacts on transportation choice to an actual calculation of the total number of trips or people likely to be affected. Either way, the analysis results will be enriched by feedback from local planners, officials, and transportation users. METHODS FOR STUDYING TRANSPORTATION CHOICE Table 7-4 summarizes the three methods we suggest for evaluating the extent to which transportation choice exists for protected populations. Method 7. Modal quality assessment Qualitative analysis is a screening tool that is especially useful during the design phase of a project. The analysis answers the question of how a transportation project will affect the number and quality of transportation choices.

190 Table 7-4. Summary of methods for evaluating transportation choice Method Assessment level Appropriate uses Use when Data needs Expertise required 7. Modal quality assessment Screening Assess demand for various transportation modes Design phase when project will produce significant changes in availability of certain transportation modes Low Survey methods; graphs, charts, maps 8. User demand and evaluation surveys Screening Assess current level of use of various transportation modes Planning phase when project will produce significant changes in availability of certain transportation modes Low Survey methods; graphs, charts, maps 9. Improved transportation surveys and models Screening/ detailed Assess current use and future demand for various transportation modes Planning phase when project will produce significant changes in availability of certain transportation modes Medium Survey methods; graphs, charts, maps When to use. An assessment of modal quality provides you with a baseline condition of transportation choice in an area of the community that is likely to be affected by a proposed transportation project. If the analysis reveals significant deficiencies, they can and should be addressed in the process of planning for the proposed project. If the project would worsen the level of choice, either it should be redesigned or substantial mitigation efforts must be carried out. Analysis. The analysis has three steps: 1. Identify the transportation modes to be considered. 2. Select suitable standards, guidelines, or indicators for each mode. This selection depends on two factors: – Overall goals and objectives. For example, an analysis focusing on equity effects would probably use different indicators of transportation choice than would an analysis focusing on TDM objectives, such as congestion and emission reduction. – Community preferences. Some communities may place greater weight on a particular choice or indicator. Consultation with elected officials and public advisory committees or a public forum may be useful to gauge community preferences. 3. Consolidate material from Step 2 into a small number of indicators that reflect the nature of the project being designed and the preferences and concerns of affected residents. Although a qualitative analysis certainly can involve the development of numeric measures, its principal objective is to give a general idea of who is likely to be affected by a transportation project and how. Using GIS, it is possible to categorize residential areas according to the number

191 of transportation-disadvantaged residents and other attributes that may affect the need for alternative modes. Incorporated into a transportation model that has been modified to include alternative modes and transportation-disadvantaged groups, spatial data can indicate how the project would change transport choice and trip affordability for residents and visitors to the affected area. Data needs, assumptions, and limitations. Table 7-5 summarizes a series of simple factors that indicate whether an alternate mode would help provide mobility for nondrivers, low-income households, or people with disabilities within the affected area of the community. All of these impacts are highly relevant to an environmental justice assessment. Additionally, one is able to assess related quality-of-life factors such as whether it supports TDM objectives such as reduced traffic congestion, road and parking facility cost savings, and reduced environmental impacts. Results and their presentation. The results of a qualitative analysis can be presented using graphs or maps and incorporated into a transportation model. For example, analysis of a highway-widening project could include graphs showing how pedestrian and cycling level of service (LOS) would change under various design options (see Chapter 6), along with maps showing the location of major activity centers (e.g., schools, shops, transit stops, parks, and recreation centers) and residential areas relative to the project. Assessment. An analysis of modal quality is a potentially valuable element in an assessment of the current mobility of protected populations. It also is a relatively simple way to gain a general sense of how various options for achieving environmental justice objectives might affect the transportation choices available to residents of a geographic area. Its advantage is that it can be done quickly for several design options, and it can provide important insights. Using a rather basic checklist such as that in Table 7-5, one can evaluate the probable effects of each alternative on the transportation choices of area residents and visitors. Such an analysis can hardly be regarded as rigorous or definitive, but it can be a useful tool for providing an early warning at a critical juncture in the development of a transportation project. Method 8. User demand and evaluation surveys User demand and evaluation surveys can be used to gather information from travelers who may be inclined to use a particular transportation alternative. These surveys can also be used to obtain feedback on the specific barriers and problems facing people who currently walk or cycle on a particular facility or in a specific area. Such surveys are useful in that they help identify specific attributes of roadways and their environs that make them especially conducive to travel by means other than the automobile. The National Highway Institute (1996, Chapter XVI.B) provides information on user surveys to evaluate bicycle and pedestrian conditions. When to use. User demand and evaluation surveys are a practical method for assessing the capacity of an area of a community to enable localized mobility. If an area that is within the activity space of protected populations would be affected by a proposed transportation project, this method can be used to assess current capabilities and those if the project were undertaken. User surveys can be distributed to walkers and cyclists at a study site (e.g., survey forms can be

192 Table 7-5. Sample of factors to use in a modal quality analysis Issue Likely result As a result of this transportation project, traffic volumes are likely to:  Increase  Decrease  Stay the same As a result of this transportation project, the number of pedestrian facilities surrounding the facility is likely to:  Increase  Decrease  Not change As a result of this transportation project, the quality of pedestrian facilities (e.g., number of cracks or potholes) surrounding the facility is likely to:  Increase  Decrease  Not change Will the number of pedestrian barriers (e.g., steep inclines or lengthy road crossings) increase, decrease, or not change as a result of this project?  Increase  Decrease  Not change As a result of this project, will residents surrounding the facility have increased, decreased, or the same access to transit stops?  Increased access  Decreased access  No change Are transit service coverage (i.e., the number of routes within a quarter mile), reliability, and frequency likely to increase, decrease, or stay the same as a result of this project?  Increase  Decrease  Stay the same The quality of service associated with paratransit services to residential areas surrounding the new facility is likely to:  Increase  Decrease  Stay the same Are availability and response times for taxi services likely to increase, decrease, or not change as a result of this transportation project?  Increase  Decrease  Not change Will the number of mobility barriers identified by people with physical disabilities increase, decrease, or not change as a result of this project?  Increase  Decrease  Not change The portion of the pedestrian network surrounding the project that meets barrier-free design standards is likely to:  Increase  Decrease  Stay the same As a result of this transportation project, the number of bicycle facilities (e.g., lanes or trails) will:  Increase  Decrease  Stay the same As a result of this transportation project, accessibility of bicycle facilities (e.g., lanes or trails) is likely to:  Increase  Decrease  Stay the same In general, will the proposed transportation project improve, worsen, or not affect the environmental conditions for nonmotorized travel in the area surrounding the facility?  Improve  Worsen  Not affect

193 passed out along a sidewalk or trail), distributed through organizations (e.g., hiking and cycling clubs) and businesses (e.g., bicycle shops), or mailed to area residents. Analysis. Pedestrian and bicycle travel surveys should attempt to gather the following information: • Origin and destination of trips, including links by other modes (such as transit); • Time, day of the week, day of the year, and conditions (such as weather, road, and traffic conditions); and • Factors that influence travel choice (such as whether a person would have chosen another route or a particular mode if road conditions or facilities were different). A crucial part of this analysis involves identifying specific problems that travelers encounter when walking and cycling, such as streets with inadequate sidewalks, roads with inadequate curb lane widths or shoulders, and dangerous railroad crossings. These problems can then be addressed during the design phase of transportation projects in the area. Data needs, assumptions, and limitations. The following questions might be included in non- motorized travel surveys: • How much do you rely on walking and cycling for transportation and recreation? • How do you rate walking and cycling conditions in the study area? • What barriers, problems, and concerns do you have related to walking and cycling in the study area? • What improvements or programs might improve walking and cycling conditions? Note that in some circumstances results may be skewed by the fact that club members, people who frequent bicycle shops, and people most inclined to return surveys may not be representative of the entire user population. Results and their presentation. User survey results should be summarized to highlight key findings. The results can then be used to identify how transportation choice should be evaluated and how a particular policy or project is likely to affect transportation options. Standard statistical analysis techniques can be used to evaluate the accuracy of survey results. Geographic information can be presented on maps, and time series data can be graphed to illustrate trends. Results from user surveys can be used to demonstrate mode, group, or location analysis findings. For example, to analyze the effects a highway project will have on the travel choices of transportation-disadvantaged people, it may be appropriate to present survey data indicating the number of people in various groups near the project site (e.g., nondrivers, low-income persons, and persons with disabilities), their current travel patterns (e.g., how many currently walk and bicycle along the proposed route), and how these travel modes are likely to be affected. Assessment. User evaluation surveys are a commonly applied tool for determining the current circumstances facing pedestrians and cyclists. Problem areas identified in these surveys can then

194 be addressed as a transportation project is designed. More specifically, this gives planners a better understanding of features to avoid or include to facilitate travel by alternative modes when designing upgraded or reconfigured facilities. By making it easier to travel by modes other than the auto, those whose resources are severely limited are bound to enjoy greater mobility. As is true of any user survey, however, the results will reflect only the views and experiences of current or past users. Those who have not been able or willing to use the various forms of alternative transportation will not be represented. Thus, it must be recognized that these surveys are only one useful source of information; they cannot be regarded as definitive for establishing the needs and preferences surrounding alternative transportation issues. Method 9. Improved transportation surveys and models Various conventional travel surveys can be improved to more accurately assess demand for alternative modes and how this demand would be affected by particular policies and projects. Most current surveys tend to undercount nonmotorized modes because the walking and cycling links of motorized trips are ignored (e.g., a walk-bus-walk trip is coded only as a transit trip). One study found that the actual number of nonmotorized trips is six times greater than what conventional surveys indicate (Rietveld 2000). Other limitations of most current surveys include not being sensitive to many factors that affect public transit demand. For example, most surveys are not sensitive to convenience and comfort features or to the quality of the pedestrian environment around transit stops. Furthermore, most current surveys do not consider certain alternative modes at all; they generally exclude ridesharing, taxi trips, automobile sharing, and delivery services. Most are not very accurate in predicting the effects of TDM strategies. Finally, many surveys and models are unable to specifically address travel by transportation-disadvantaged persons. When to use. In many circumstances, travel surveys can be improved to provide better information on travel demand for alternative modes, on travel requirements of transportation- disadvantaged groups, and on functional barriers to the use of alternative transportation. This information can be of great value when assessing the extent to which a proposed transportation project would reduce or worsen such functional barriers. Analysis. Surveys that are sensitive to alternative modes can be analyzed using fairly standard methods to answer such questions as how basic mobility for transportation-disadvantaged persons or travel choice by commuters is likely to be affected by a particular policy or project. In addition to examining direct, short-term effects, the analysis should consider to what degree the project is likely to contribute to long-term changes that increase automobile dependency and how this is likely to affect alternative modes. For example, the issues emerging from user surveys can become a checklist for identifying specific effects of the project that need to be assessed in the design phase. They also should be factored into go, no-go decisions. Data needs, assumptions, and limitations. The first step in improving standard travel surveys is to determine what questions the analysis is to answer. For example, the question might be, “How

195 will widening this highway affect the travel choices within the study area?” Answering this question may require data such as the following: • Survey data concerning the number of people who have various transportation-relevant attributes (e.g., nondrivers, low-income persons, persons with physical disabilities, commuters, and tourists) in the area; • Survey data concerning the demand for transportation alternatives by the different groups (i.e., the types of modal attributes they find desirable and within their reach); • Survey data on the current quality of alternative modes and on the barriers that different user groups encounter, such as poor pedestrian conditions or inconvenient transit access; and • Analysis of survey data that can evaluate how a particular change in the transportation network would affect alternative modes and their use, especially by protected populations. Results and their presentation. Information can be presented in much the same way that current transportation survey data are presented: using tables, graphs, and maps, with results disaggragated by mode and demographic group as appropriate. Below are some examples of ways in which the survey results might be presented: • Graphs showing the number and quality of travel options currently available to different groups (e.g., motorists, nondrivers, minority populations, low-income populations, and those with disabilities) and how these options are likely to be affected by a particular policy or project; • Maps showing the location of barriers to walking and cycling identified in a survey and their relationship to public transit stops, shops, and employment and education centers; • Maps showing the location of transit access points and retail shops that provide delivery services and their proximity to residential areas with a sizable population of nondrivers; and • Graphs comparing average door-to-door commute times and financial costs between various residential areas and common workplace sites for travel by automobile and by alternative modes. Assessment. Travel surveys have long been an important tool for transportation planners. Such surveys have been almost entirely directed at the automobile, but it is certainly possible to adapt them for inquiries into the performance and needs of alternative transportation modes. Knowing as much as possible about people’s concerns regarding current facilities and their desires for travel by alternative modes will help you assess the extent to which a proposed project would support these other modes. The surveys also can provide insights into how a proposed design could be modified to better support travel by alternative modes.

196 RESOURCES 1) American Association of State Highway and Transportation Officials (AASHTO). 2003. User Benefit Analysis for Highways. Washington, DC: AASHTO. This publication is a replacement for the 1977 AASHTO “Redbook” that provided guidance in estimating the user effects of highway and transit projects. The new book focuses on highways and provides the latest thinking on user effects. It is presented in an easy-to-access format. 2) Beinborn, Edward A. 1995. A Transportation Modeling Primer. Milwaukee, WI: University of Wisconsin, Milwaukee, Center for Urban Transportation Studies. Available at http://www.uwm.edu/Dept/CUTS/primer.htm. This Web document is an excellent overview of the traditional four-step transportation modeling process. It presents technical considerations in an easy-to-understand manner. 3) Bureau of Transportation Statistics. 1997. “Mobility, Access and Transportation.” Transportation Statistics Annual Report. Washington, DC: U.S. Department of Transportation, Chapter 6, pp. 135–145, and Chapter 8, pp. 173–192. These chapters define and distinguish access and mobility in an historical context. There is little explanation of tools to measure “accessibility,” but there is a discussion of factors that affect it. 4) Dixon, Linda. 1996. “Bicycle and Pedestrian Level-of-Service Performance Measures and Standards for Congestion Management Systems.” Transportation Research Record 1538. Washington, DC: Transportation Research Board, National Research Council, pp. 1–9. This article describes LOS ratings for walking and cycling conditions to help identify ways to improve and encourage nonmotorized transportation. The ratings take into account the existence of separated facilities, conflicts, speed differential, congestion, maintenance, amenities, and TDM. These are relatively easy-to-use methods for evaluating nonmotorized roadway conditions that may be simpler to apply than other, more data-intensive methods. 5) Eash, Ronald. 1999. “Destination and Mode Choice Models for Nonmotorized Travel,” Transportation Research Record 1674. Washington, DC: Transportation Research Board, National Research Council, pp. 1–8. This article describes the techniques used to modify the Chicago Area Transportation model so that it could evaluate pedestrian and bicycle travel. Smaller analysis zones were created, and various demographic and transportation system factors that affect nonmotorized travel behavior were incorporated into the model. This article should be useful to planners and modelers who might want to incorporate nonmotorized travel into a conventional traffic model. 6) Federal Highway Administration. 1983. Calibrating and Testing a Gravity Model for Any Size Urban Area. Washington, DC: U.S. Department of Transportation. Available from the National Transportation Library at http://www.bts.gov/NTL/DOCS/CAT.html.

197 This document provides a technical definition of accessibility measurement as implemented with gravity models in urban travel forecasting models. It explains how zonal accessibility measures are used with gravity models to estimate impacts of transportation projects on trip distances and the spatial distribution of trips in a metropolitan area. 7) Handy, Susan. 1994. “Regional Versus Local Accessibility: Implications for Nonwork Travel.” Transportation Research Record 1400. Washington, DC: Transportation Research Board, National Research Council, pp. 58–66. This article shows the correlation between automobile-oriented transportation development and subsequent changes in patterns of accessibility to retail and service activity within metropolitan areas. It demonstrates how alternative land use and transportation patterns can affect trip distances, and it shows how local access and broader regional access can be affected differently. 8) Landis, Bruce. 1996. “Bicycle System Performance Measure.” ITE Journal, Vol. 66, No. 2 (February), pp. 18–26. This article describes relatively easy-to-use techniques for estimating potential bicycle travel demand (the Latent Demand Score) and evaluating roadway conditions for cycling in a particular area (the Interaction Hazard Score). These approaches are similar to other models used by traffic engineers that require demographic, geographic, and road condition information. 9) Schwartz, W.L., C.D. Porter, G.C. Payne, J.H. Suhrbier, P.C. Moe, and W.L. Wilkinson III. 1999. Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods. Turner-Fairbanks Highway Research Center. FHWA-RD-98-166. Washington, DC: Federal Highway Administration. This guidebook describes and compares various techniques that can be used to forecast non- motorized travel demand and to evaluate and prioritize nonmotorized projects. It provides an overview of each method, including pros and cons, ease of use, data requirements, sensitivity to design factors, typical applications, and whether it is widely used. REFERENCES Los Alamos National Laboratory and Price Waterhouse Coopers. 2002. “TRANSIMS-3.0 Documents.” U.S. Department of Transportation. Vol.3. Available at http://transims. tsasa.lanl.gov/. National Highway Institute. 1996. Pedestrian and Bicyclist Safety and Accommodation; Participants Handbook. National Highway Institute Course No. 38061. Washington, DC: Federal Highway Administration. RDC, Inc. 1995. “Activity-Based Modeling System for Travel Demand Forecasting: Travel Model Improvement Program.” Washington, DC: U.S. Department of Transportation/U.S. Environmental Protection Agency. Available at http://tmip.fhwa.dot.gov/clearinghouse/ docs/amos/.

198 Richardson, A. 2001. “Never Mind the Data – Feel the Model.” Paper presented at the International Conference on Transport Survey Quality, Kruger National Park, South Africa. Available at http://www.tuti.com.au/PUBLICATIONS/2001/2001Kruger.pdf. Rietveld, P. 2000. “Nonmotorized Modes in Transport Systems: A Multimodal Chain Perspective for the Netherlands.” Transportation Research, Vol. 5D, No. 1 (January), pp. 31–36. Public Roads. 2000. “TRANSIMS Computer Software Improves Transportation Decisions.” Public Roads. (November). Online Publication available at http://www.tfhrc.gov/pubrds/ nov00/along.htm. U.S. General Accounting Office (GAO). 2000. Highway Infrastructure: FHWA’s Model for Estimating Highway Needs Is Generally Reasonable, Despite Limitations. Report to Congressional Committees. GAO/RCED-00-133 Washington, DC: GAO (June). U.S. General Accounting Office (GAO). 2001. Highway Infrastructure: FWHA’s Model for Estimating Highway Needs Has Been Modified for State-Level Planning. Report to Congressional Committees. GAO-01-299, February. Available at http://www.gao.gov/ new.items/d01299.pdf.

Next: Chapter 8 - Community Cohesion »
Effective Methods for Environmental Justice Assessment Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

TRB’s National Cooperative Highway Research Program (NCHRP) Report 532: Effective Methods for Environmental Justice Assessment is designed to enhance understanding and to facilitate consideration and incorporation of environmental justice into all elements of the transportation planning process, from long-range transportation systems planning through priority programming, project development, and policy decisions. The report offers practitioners an analytical framework to facilitate comprehensive assessments of a proposed transportation project’s impacts on affected populations and communities.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!