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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
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and shopping centers, social service agencies and providers (including daycare centers), and
schools.
Table 7-1.
Summary of methods for studying accessibility
Assessment Appropriate Use Data Expertise
Method level uses when needs required
1. Unmodified Screening Estimate travel The project will impact travel Medium Standard travel
transportation demand (TD) demand patterns demand modeling;
census data
demand between TAZs
analysis
models
2. Adapted Detailed Estimate travel The project will impact travel Medium/ Standard travel
transportation demand (TD) demand patterns and High demand modeling;
demand between census protected population census data
models tracts distribution is uniform within analysis
census tracts
3. Advanced Detailed Estimate travel The project will impact travel High Standard travel
adapted demand (TD) demand patterns and demand modeling;
transportation between census protected population census data
models blocks distribution is not uniform analysis
within census tracts
4. HERS-ST Screening/ Estimate traffic The project will impact travel Medium HERS-ST
model detailed congestion cost for protected populations application;
and/or travel TransCAD
cost
5. Activity- Detailed Estimate traffic Detailed, dynamic analysis of High Advanced
based travel congestion traffic patterns is required or modeling tools
simulation and/or travel for large or high-impact and techniques
cost projects
6. Transportation Detailed Estimate traffic Detailed, dynamic analysis of High Advanced
analysis and congestion traffic patterns is required or modeling tools
simulation and/or travel for large or high-impact and techniques
system cost projects
(TRANSIMS)
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;
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· 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
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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.
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· 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.
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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.
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in vehicle speed or level of congestion in one of the submodels. Figure 7-2 is a graphic
representation of the HERS model.
Rural roads Arterials Collectors Local roads
Rural interstates Rural major collectors Rural local roads
Rural other
Rural minor collectors
principal arterials
Rural minor
arterials
Urban roads Arterials Collectors Local roads
Urban interstates Urban collectors Urban local roads
KEY
Urban freeways and
FHWA uses HERS expressways
to project needs
for these highways
Urban other
principal arterials
FHWA uses non-HERS
methods to project
needs for these highways
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.
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Travel
forecast
Estimate costs Evaluate
Forecast (travel time, and select
pavement operating, safety, improvements for
condition emissions, etc.) implementation
Forecast
vehicle
speed
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
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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,
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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
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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.
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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
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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.
Input files
Census Population Traveler Transit Network Air quality Arbitrary
forecast survey surveys box data
Modules
Population Activity Route Traffic micro- Emissions Output
synthesizer generator planner simulator estimator visualizer
Input & output files
Synthetic Activity Vehicle Traveler Simulation Emissions MODELS3
population plans output inventory database
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
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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
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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.
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