&EPA
United States
Environmental Protection
Agency
Policy
(2126)
Washington, DC 20460
EPA231-R-98-007
December 1998
Assessing the Emissions and
Fuel Consumption Impacts of
Intelligent Transportation
Systems (ITS)
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Assessing the Emissions and Fuel
Consumption Impacts of Intelligent
Transportation Systems
Energy and Transportation Sectors Division
Office of Policy
U.S. Environmental Protection Agency
Prepared under EPA Contract Numbers 68-W6-0055 and 68-W4-0041
NOTICE
This technical report does not necessarily represent final EPA decisions or positions. It is
intended to present technical analysis of issues using data which are currently available. The
purpose in the release of such reports is to facilitate the exchange of technical information and
to inform the public of technical developments which may form the basis for a final EPA
decision, position or regulatory action.
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FOREWORD
Intelligent Transportation Systems (ITS) include a broad range of transportation improvements,
such as traffic signal control, freeway management, transit management, incident management,
and regional multimodal traveler information services. ITS has generated considerable
enthusiasm in the transportation community as a potential strategy for reducing highway
congestion, improving highway safety, and reducing environmental impacts associated with
motor vehicle travel. Some policy makers, however, are concerned that induced travel associated
with ITS may partially offset the potential emission benefits of improved traffic operations. As a
result, methodologies are needed to evaluate the full traffic and emissions implications of ITS
deployment.
This study describes the types of modeling approaches needed to capture the short- and long-term
transportation, emissions, and fuel consumption impacts of ITS deployment. It describes needed
progressions in modeling approaches, including developments in travel demand, traffic
simulation, and modal emissions modeling. The study identifies a framework for modeling the
impacts of specific ITS components and discusses data issues relevant to an evaluation of ITS
impacts.
This report was prepared under contract for the United States Environmental Protection Agency
(EPA), Office of Policy by Hagler Bailly Services, Inc.
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CONTENTS
1. INTRODUCTION 1
2. ITS EVALUATION METHODOLOGY 4
2.1 NEEDED PROGRESSIONS IN METHODOLOGY 5
2.1.1 Emissions Models 5
2.1.2 Travel Demand Models 6
2.1.3 Traffic Simulation Models 8
2.1.4 Linking Travel Demand, Traffic Simulation, and Emissions Models 9
1.1 MODELING SPECIFIC ITS COMPONENTS 10
2.2.1 Traffic Signal Control 11
2.2.2 Freeway Management 14
2.2.3 Transit Management 16
2.2.4 Incident Management 19
2.2.5 Regional Multimodal Traveler Information 19
2.2.6 Other ITS Components 23
2.3 DATA ISSUES RELEVANT TO AN EVALUATION OF ITS IMPACTS 25
2.3.1 Data Availability 26
2.3.2 Data Collection Plans 27
2.3.3 Overview of Data Framework 28
2.4 SUMMARY OF METHODOLOGIES 30
3. REVIEW OF RESEARCH EFFORTS & MODELS 31
3.1 MODELS IN DEVELOPMENT 31
3.1.1 INTEGRATION (v. 2.0) Traffic Simulation Model 32
3.1.2 FHWA's TRAF-NETSIM Traffic Simulation Model 34
3.1.3 UC Berkeley's AIRQ Post-Processor for Linking Transportation Models 36
3.1.4 EPA'sMOBILE6 Emissions Factor Model 38
3.1.5 UC Riverside's Modal Emissions Model 40
3.1.6 Georgia Tech's GIS-Based Mobile Emissions Model 41
3.1.7 Los Alamos National Laboratory's TRANSIMS Model 44
3.1.8 Mitretek's Travel Demand Modeling Efforts 45
3.1.9 STEP Travel Demand Model 48
3.2 SUMMARY OF MODELING DEVELOPMENTS 50
3.3 MODELING vs. MEASUREMENT 50
APPENDIX: MODELING DATA NEEDS 53
A.1 TRANSPORTATION SYSTEM DATA 54
A. 1.1 Travel Demand Models 54
A. 1.2 Traffic Simulation Models 57
A.2 EMISSIONS AND FUEL CONSUMPTION RELATED DATA 59
A.3 INTELLIGENT TRANSPORTATION SYSTEMS DATA 61
REFERENCES 64
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EXHIBITS
1. Tracing the Emissions and Fuel Consumption Impacts of ITS 1
2. Traditional Transportation Planning Process 6
3. Linking Travel Demand, Simulation, and Emissions Models 9
4. Summary of the Spatial and Temporal Dimensions of ITS Components 11
5. Modeling Traffic Signal Control Systems 13
6. Modeling Freeway Management Systems 15
7. Modeling Transit Management Systems 18
8. Modeling Incident Management Systems 20
9. Modeling Regional Multimodal Traveler Information Systems 22
10. Relationships among System Integration Components 25
11. Data Collection, Organization, and Retrieval Cycle 29
12. Research Programs Reviewed in this Study 32
13. Conceptual Approach to Fuel Consumption and Emissions Analysis 34
14. Linkage of UTPS and Simulation Models 37
15. Process for Developing the Proposed Model 38
16. UC Riverside Model Input/Output 40
17. Modal Emissions Model 40
18. GIS-based Modal Model Conceptual Design 42
19. HC Emissions in Modeled Portions of Atlanta 43
20. Proposed MOEs for Evaluation 45
21. Traffic Flow Analysis 47
22. Summary of Modeling Developments 51
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
1. Introduction
Intelligent Transportation Systems (ITS) have generated considerable enthusiasm in the
transportation community as a potential strategy for reducing highway congestion, improving
highway safety, enhancing the mobility of people and goods, promoting economic productivity,
and reducing the environmental impacts associated with motor vehicle travel. However, as state
and local governments across the country proceed with the deployment of ITS services and
technologies, some policy makers are concerned that the detrimental emission effects of
increasing the number of vehicle trips and miles traveled may partially offset the potential
emission benefits of improved traffic operations and system efficiencies. The objective of this
study is to describe the types of modeling approaches needed to capture the short- and long-term
transportation and emission/fuel consumption impacts of ITS deployment.
As illustrated in Exhibit 1, the deployment of ITS user services can be expected to affect the
following general parameters, which can be Exhibit 1
thought of as deployment outcomes: Tracing the Emissions and Fuel
Consumption Impacts of ITS
traffic flow along specific links of the
highway network
the number of trips (vehicle or person)
by time-of-day and mode along specific
links
trip distance for specific origin and
destination (O-D) pairings.l
Traveler
Behavior
Changes in trip making
Changes in trip distance
Transportation System
Efficiency
Traffic flow improvements
Level of Service improvements
Emission
&
Fuel Consumption
Changes in activity parameters
Changes in emissions and
consumption rates
Changes in fleet composition
These general parameters are interrelated
and encompass most of the factors that
characterize transportation system
performance and traveler behavior. For
example, the integration of traffic management and traveler information systems may affect travel
behavior as users of the system adjust their departure times, destinations, mode choices, and/or
route choices in response to a more complete information set. Adjustments in these variables
determine the number of trips and traffic flow along links, as well as trip distances across O-D
pairings. Likewise, traffic signal control, freeway management, and incident management may
1 Note that at the aggregate level (i.e., for a specific transportation network) the number of vehicle trips and trip
distances combine to determine vehicle miles of travel (VMT). Likewise, traffic flow is normally characterized by
average travel speed, which determines travel time. VMT and speed are important activity-based indicators of trip
emissions and fuel consumption. However, changes in average travel speed will not capture the full effects of ITS
deployment on motor vehicle emissions and fuel consumption as the operating profile of the vehicle itself (i.e.,
accelerations and decelerations) is often more important.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
affect volume-to-capacity ratios, vehicle hours of travel and delay, average trip speeds, vehicle
modes of operation, and other indicators of traffic flow along specific links. As traffic flow
improves on a specific link, the number of trips along that link may increase as travelers adjust to
changing traffic conditions. These effects may in turn impact trip distances.
A methodology for evaluating the emissions and fuel consumption effects of ITS must address all
potential deployment outcomes, including the potential for induced travel effects. Induced travel
is defined as: 1) trips that people would like to be able to take, but that they forgo because of
associated delays and excessive travel time; 2) increases in motor vehicle trips resulting from
mode shifts; and 3) increases in vehicle miles of travel (VMT) stemming from increases in the
distances of trips. Significant improvements in the transportation system (usually, increases in
roadway capacity) that lower users' perceived costs of travel (primarily by reducing travel times)
can induce additional vehicle miles of travel (VMT), therefore increasing emissions and fuel
consumption.2 Thus, a methodology will need to evaluate the impacts of modified traveler
behavior that may, in some cases, reduce or negate potential emission benefits from improved
traffic flow.
Mapping the deployment of specific ITS elements to transportation system performance, travel
behavior parameters, and, subsequently, to emissions and fuel consumption impacts is a
challenging exercise, as many of the effects attributable to ITS will be multidimensional.
Although progress has been made in enhancing or developing new travel demand, traffic
simulation, and emissions models, more must be done to accurately assess the short and long-term
emissions and fuel consumption impacts of ITS.
The remaining sections of this report describe potential modeling processes for estimating the
impacts of ITS infrastructure (Section 2); and review developments in travel demand, traffic
2 The magnitude of changes in demand as a result of changes in price is summarized by the elasticity of demand.
Elastic demand implies that a change in user cost (i.e., price) leads to major increases in travel. Inelastic demand
implies that a change in price leads only to minor changes in the amount of travel. In the short run, the demand for
travel is relatively inelastic, since travelers cannot readily alter their existing housing and employment locations.
Consequently, changes in the perceived user cost of travel have a relatively small impact on travel demand. In the
long run, however, households have the capability to select different housing and employment locations and to alter
their behavior in other ways that may increase the demand for travel (as represented by the number and length of
trips).
Recent work by the Institute of Transportation Studies at the University of California, Berkeley, shows that the
elasticity of VMT with respect to capacity at the county level is 0.62, while the elasticity at the metropolitan level is
0.94. The increased VMT from a capacity expansion is estimated to be realized within two years at the county level
and within four years at the metropolitan area level. This means, for example, that a one percent increase in capacity
at the county level is expected to result in a 0.64 percent increase in traffic two years after the expansion project has
been completed. (Source: Hansen, Mark. The Traffic Inducement Effect: It's Meaning and Measurement,
Transportation Research Circular, no. 481, Transportation Research Board, National Research Council)
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
simulation, and modal emissions modeling (Section 3). The appendix summarizes the types of
data that must be collected to accurately assess the impacts of ITS.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
2. ITS Evaluation Methodology
Decision makers rely on distinct models to address the broad range of transportation issues at the
local, regional, and state levels. Metropolitan Planning Organizations (MPOs), for example,
execute short- and long-term planning activities with the help of travel demand models that
forecast the effects of alternative strategies on congestion within a regional transportation
network. In contrast, transportation engineers typically use traffic simulation models to address
road, link, or intersection-specific traffic issues and to develop site-specific improvement plans.
Meanwhile, air quality and transportation planners estimate vehicle emissions using emission
factor models (specifically MOBILE and EMFAC) that rely on aggregate activity estimates
provided by conventional travel demand models.
Quantifying the emissions and fuel consumption impacts of ITS deployment will require a
methodology that accounts for or resolves the significant imperfections of existing mobile source
emissions models, travel demand models, traffic simulation models, and traveler behavior
analysis. These deficiencies are summarized below.
EPA's MOBILE emissions factor model cannot accurately estimate the effect of traffic flow
improvements on trip emissions (e.g., the effect of changes in the speed profiles of trips that
are outside the scope of the Federal Test Procedure, or FTP).
Conventional four-step travel demand models cannot account for 1) the impacts of changes in
perceived travel costs on travel demand, 2) the long-term effect of transportation investments
on land use patterns (both of which are relevant to assessments of induced demand), 3) the
impacts of congestion relief on trip generation, and 4) the impacts of pre-trip information on
mode or route choice.
Conventional traffic simulation models focus on sub-area phenomena, but must be able to
simulate traffic at the network level to account for the impacts of ITS system integration
across traffic analysis zones (TAZ) and/or jurisdictions.
Conventional travel demand, traffic simulation, and emissions/fuel consumption models are
not linked and therefore do not ensure analytic consistency across spatial and temporal
dimensions.
The purpose of this section is to present the types of modeling approaches needed to assess the
emissions and fuel consumption impacts of ITS. First, shortcomings characterizing conventional
emissions and transportation models are explored in more detail, and a conceptual modeling
platform is described. Second, specific analytic methods are presented for each component of ITS
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
deployment in metropolitan regions. Finally, the data needs associated with a comprehensive
evaluation of ITS emissions and fuel consumption impacts are summarized.
2.1 Needed Progressions in Methodology
Analysis of the transportation and travel impacts of ITS deployment requires a modeling approach
that integrates travel demand models with traffic simulation models. Traffic simulation models are
needed to analyze the effects of specific ITS elements (e.g., advanced traffic signal control or
ramp metering) on facility performance, while travel demand models are needed to assess the
impacts of user services that affect traveler behavior. Furthermore, the effects of ITS deployment
on facility-specific level-of-service must also be captured at the regional level, thereby requiring
feedback mechanisms between traffic simulation and travel demand models. Once an integrated
transportation modeling platform is developed, emissions and fuel consumption models can be
linked to this platform to assess the effects of ITS deployment on vehicle emissions and fuel
consumption.
2.1.1 Emissions Models
Motor vehicle emissions are highly dependent on the modal activity of a given trip. Power
enrichment (acceleration) and motoring (deceleration) events are discrete vehicle operating modes
that are each capable of producing significant emissions. High vehicle emissions during rapid
vehicle acceleration result from enrichment of the engine's fuel-air mixture, which achieves
maximum engine power but creates high levels of unburned hydrocarbons and carbon monoxide.
Laboratory tests have indicated that high acceleration rates are significant contributors to
instantaneous emission rates, and that in some cases one sharp acceleration can cause as much
pollution as the entire remaining trip.3 Likewise, the poor combustion caused by rapid throttle
closing (i.e., sharp deceleration) results in high emissions of unburned hydrocarbons and carbon
monoxide. The fuel injection systems in most newer vehicles stop the addition of fuel during
vehicle decelerations, but the resulting rapid throttle closing still causes a "spike" of unburned
hydrocarbons and carbon monoxide.
The number of episodes of power enrichment and rapid throttle closing are a function of the
smoothness of traffic flow. Vehicles operating in unsteady traffic may experience numerous rapid
throttle-closing events without coming to a full stop or even sharply decelerating. These throttle
closings, coupled with the accompanying accelerations, lead to high levels of emissions under
stop-and-go or other variable speed conditions. Thus, ITS deployments that decrease speed
3 M. Barth, et. al. The Development of a Comprehensive Modal Emissions Model: Operating Under Hot-Stabilized
Conditions. Transportation Research Board, Annual Meeting Paper No. 970706. January 1997.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
variation by improving traffic flow can have important effects on a vehicle's emission rate during
a given trip.
Changes in the speed profiles of vehicle trips cannot be accurately accounted for in such widely
used models as MOBILE and EMFAC. The baseline exhaust emissions data used in both models
are based on a standardized driving cycle that was originally developed to duplicate the speed-
time profile of a route in the Los Angeles metropolitan area in the late 1960s. New drive cycles
are being constructed to represent a wider variety of functional class roadways and driving
situations. A methodology that quantifies the emissions and fuel consumption impacts of ITS
deployment must account for the impact of traffic flow improvements on vehicle operation events
that are outside the envelope of drive cycles imbedded in MOBILE and EMFAC.
In response to this need, modal emissions models are being developed that can predict second-by-
second tailpipe emissions under a variety of driving conditions. Calibration of such models
requires test runs on freeways, arterials, collectors, and local streets, with instruments measuring
the vehicle's emissions by type, fuel consumption, and instantaneous speed variations. This
calibration results in a correlation between emissions/fuel consumption and vehicle speed
(acceleration/deceleration) under the entire spectrum of operating modes.
Exhibit 2
Traditional Transportation Planning Process
Precursor Activities
Four-Step Planning Process
2.1.2 Travel Demand Models
Exhibit 2 shows the sequential forecasting process in
a typical travel demand model. The transportation
planning process is often referred to as the "four-step
planning process." The steps are summarized
below.
Precursor Activities: Land Use &
Socioeconomic Projections. Forecasts of future
land-use patterns and socioeconomic
demographics are the pre-step to transportation
planning. In the traditional model, these
independent variables influence trip generation
rates. One major weakness in the sequential
forecasting process is that land-use and socioeconomic distributions serve only as an input to
trip generation but do not reflect changes due to improvements in the transportation system.
Fee
Not
Regional 1
Growth Forecasts!
f
Land Use |
Allocations 1
Vehicle |
Ownership 1
Ot
Poten
Feedba
dback:
Incorporated
,er
Mai
:ks
Trip |.
Generation rnT^
t
Trip |
Distribution 1
'
r
Mode |
Split |
L._...
Time of Day
1
Highway and Transit!
Assignment 1
Emissions 1
4 Schematic is based on that presented in EPA's Technical Methods for Analyzing Pricing Measures to Reduce
Transportation Emissions, EPA231-R-98-006. Description of the four-step process is gleaned from: Papacostas, C.S.
and P.D. Prevedouros. Transportation Engineering and Planning. Prentice Hall, Englewood Cliffs, NJ. 1993.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Trip Generation. As the first step of the traditional four-step process, trip generation deals
with the decision to travel. It involves forecasting the number of person-trips that will begin
from or end in each traffic analysis zone (TAZ) in the region on a typical day of the target
year, estimated separately for a number of trip purposes (work, school, social and recreational,
etc.). In this step, the number of trip ends (origins and destinations, or productions and
attractions) in each zone are determined based on trip production/attraction rates associated
with each land-use category and other characteristics.
Trip Distribution. The second step involves the choice of destination, specifically the
assignment of trips between trip-producing zones and trip-attracting zones to determine the
trip volumes between all pairs of zones. Most travelers are attracted by zones that have higher
levels of "attractiveness" (measured in terms of distance, travel time, and/or out-of-pocket
costs). Common mathematical formulations of trip distribution include various growth factor
models, the gravity model, and opportunities models.
Modal choice. This third step involves the choice of travel mode. Factors that affect the mode
selected include: trip characteristics (length of trip, time of day, orientation to CBD); trip
purpose (home based work, non-work); transportation system characteristics (relative service
level and costs associated with available modes); and trip-maker characteristics (auto-
ownership, income). Models may also account for a transit-captive population.
Highway and Transit Assignment. The fourth step involves the choice of route or paths
between pairs of zones for each travel mode. Network assignment is used to forecast vehicular
flows on the individual links that make up the transportation network. These estimates of link
utilization can be used to assess likely levels of service and to anticipate potential capacity
problems. A number of issues must be considered, including: average auto-occupancies,
patterns of demand; assignment by time of day (to investigate performance during peak
periods when capacity limitations become critical); and trip direction (e.g., flows during
morning peak times are predominantly toward major activity centers).
Trips are assigned between nodes on the network by either minimizing individual user cost, called
user equilibrium, or by minimizing overall cost to the system, called system equilibrium. Speed
post processing is used to modify highway speeds, which can be fed to transit assignment and also
back to trip distribution and mode choice.
Using the data collected through travel surveys, traffic counts, and studies, these models are
created, calibrated, and applied to evaluate the present system and analyze the future performance
5 Under user equilibrium users choose the route that minimizes their own travel times. Under system equilibrium
users select routes such that they have equal travel times between the same origin-destination sets.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
of the system. The calibrated results of the four-step process are used to identify the number of
vehicle trips taken, vehicle miles traveled (VMT), and average speeds under varying infrastructure
scenarios. These aspects of travel are used as inputs to EPA's MOBILE model and PARTS model
in order to calculate the emissions impacts of infrastructure or other transportation projects.
Travel demand models were originally developed as a tool for planning both the location and size
of new highway facilities. Some structural and theoretical aspects of travel demand models
preclude a thorough analysis of ITS. In particular, assumptions of perfect knowledge of the
system, presumed equilibrium condition, and lack of variation complicate the application of
conventional travel demand models to ITS deployment evaluations.
To quantify the emissions and fuel consumption impacts of ITS deployment, models of trip
generation, trip distribution, mode choice, and network assignment must be improved. For
example, travel demand models are generally not responsive to corridor level, intersection, and
communications related improvements or changes. These types of changes are usually analyzed
using traffic simulation models. Traffic simulation models, however, may lack the behavioral
underpinnings of travel demand models that are calibrated using regional travel behavior survey
data.
2.1.3 Traffic Simulation Models
Traffic simulation models simulate the flow of traffic at a vehicular level, and some versions offer
the option to simulate specific vehicle maneuverings such as acceleration, deceleration, weaving,
start, and stop.6
Most of the existing microscopic traffic flow simulation models are designed for either a freeway
system or a non-freeway network. A typical freeway traffic model simulates freeway segments and
ramp segments. A typical non-freeway simulation model can simulate such road networks as
arterials, collectors, and local streets. Outputs from these models include vehicle speed at specific
link locations, moving delay, static delay, percent of total traffic in weaving mode, intersection
delay, ramp delay, progression effectiveness, signal effectiveness, emissions, fuel consumption,
volume to capacity ratios, and level of service. Such information can be used by vehicle
There are three types of traffic simulation models:
macroscopic models such as FREFLO, NETFLO, and CORFLO
mesoscopic models such as FRESYS, FREQ, Transyt, Passer, and HCM
microscopic models such as FRESIM, NETSIM, CORSIM, HUTSIM, and INTEGRATION.
Microscopic models can keep track of each vehicle in the simulated network and include vehicle maneuvering models
which be used to study the vehicle acceleration/deceleration characteristics under different levels of traffic flow.
Examples of vehicle maneuvering models are TRANSIMS (under development), the Swedish VTI model, the
German AUTOBAHN SIMULATOR model, INTEGRATION, and others.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
maneuvering models to determine the speed-time profile of individual vehicles in a stream. The
speed-time profiles can then be used by modal emissions and fuel consumption models to
quantify trip emissions and fuel consumption.
A central theme of ITS is the deployment of integrated information networks to support travel and
increase utilization of the transportation infrastructure. This integration requires models that can
simulate the effect of information on traffic flow at the corridor level and travel behavior at the
regional level. Methodologies that quantify ITS impacts must be able to accept and interpret real
time traffic data received from surveillance points along the network, and to represent real time
demand and supply conditions along a corridor or over the entire regional network. As a result,
use of a simulation model as a stand-alone tool would be an inadequate methodology for
evaluating any ITS system.
2.1.4 Linking Travel Demand, Traffic Simulation, and Emissions Models
Exhibit 3 presents a generic methodology for
linking transportation and emissions models. As
shown in the exhibit, transportation models will
require the following feedback loops:
Feedback from traffic assignment to trip
generation. The purpose of this loop is to
capture the long-term effects of additional
ITS-generated capacity on regional land use
and trip making.
Feedback from traffic assignment to trip
distribution. The objectives of this loop are
to: 1) provide speed-sensitive travel times
for trip distribution, and 2) capture the long-
term changes in destination (e.g.,
employment) or origin (e.g., housing
location) due to ITS-generated capacity.
Exhibit 3
Linking Travel Demand, Simulation, and
Emissions Models
Mode characteristics
Congested speeds Highway
^~^~ Assignment
Transit
Assignment
imulation
Modal
Emissions
To capture
land use shifts
To capture
O/D shifts
Speed feedback
Feedback from simulation models to traffic assignment. The purpose of this loop is to provide
more accurate travel time and congested speed estimates for traffic assignment, since most
assignment models estimate link volumes based on congested speeds.
These model linkages and feedback loops make it possible to more accurately predict the
emissions and fuel consumption impacts of ITS deployment. However, modeling individual ITS
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
components may require a shift in emphasis to reflect the component's area of focus. For
example, if examining traffic control systems, micro-simulation models may be used to capture
the time savings that will be fed-back to the traffic assignment module of a travel demand model.
The next section provides details on how specific ITS components can be modeled.
2.2 Modeling Specific ITS Components
Typically, the effects of transportation improvements on emissions and fuel consumption have
been measured and analyzed using a combination of the following techniques:
regional travel demand modeling
microscopic traffic simulation, including vehicle maneuvering modeling
emissions and fuel consumption modeling
statistical analysis methods.
As discussed above, ITS evaluation methodologies must have the capability to produce emissions
and fuel consumption impacts estimations that reflect a vehicle's mode of operation and level of
acceleration/deceleration. Furthermore, a comprehensive, reliable, and executable ITS modeling
methodology should have three primary characteristics.
It should be flexible enough to allow the user to specify different parameter values, including
implementation level and behavioral principles.
It should be able to capture the dynamic mode and route choice effects of various ITS
components.
It should have the capability to simulate the driving patterns of each vehicle, yet provide
output that can be handled by a reasonable amount of human and computing resources.
A generic modeling approach (such as the one presented in Exhibit 3) may not yield accurate
results for all ITS components, as each component may have different traveler behavior and
system performance repercussions. Some ITS components, such as freeway management systems,
may have impacts that are predominantly corridor-level, rather than regional. Similarly, traffic
signal control systems may impact performance in the short term (e.g., within six months of
implementation) and in specific locations or corridors. In contrast, components such as
multimodal traveler information systems may have predominantly regional impacts, but only after
several years from the date of deployment. In this manner, the spatial and temporal dimensions
and the expected impacts of a specific ITS component will determine the appropriate evaluation
tool(s). Exhibit 4 summarizes the spatial, temporal, data, and modeling issues related to each of
the nine components (plus system integration) of metropolitan-level ITS deployment initiatives.
10
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Exhibit 4
Summary of the Spatial and Temporal Dimensions of ITS Components
ITS Element
Traffic Signal Control
Freeway Management
Transit Management
Incident Management
Electronic Fare Payment
Electronic Toll Collection
Railroad Grade Crossings
Emergency Management Services
Regional Multimodal Traveler Informati
System Integration
pea ion of It
Region/Transit
System
X
X
X
X
>n X
X
Corridor/
Transit 1 inp
X
X
X
X
X
X
X
X
rmact
Intersection/Park-
and-Ride Lot/
Transit Strip
X
X
X
Time of Imoact
Instantly
Short Term
(Less than
6 months)
Medium Term
(6 mth. to
1 year)
Long Term
(More than
1 year)
Given that the spatial, temporal, and impact magnitude dimensions of specific ITS components
may differ, a range of modeling approaches and analytic processes will be required. The following
subsections present specific modeling platforms for each of the nine ITS components (plus system
integration) listed in Exhibit 4. The underlying structure for each platform is the integrated
modeling framework that is described in Exhibit 3. For several of the ITS components, specific
analytic approaches are presented to illustrate ways in which the emissions and fuel consumption
impacts of ITS can be assessed. The modeling approaches for each component rely on a modal
emissions and fuel consumption model as the basis for impact assessments.
2.2.1 Traffic Signal Control
Exhibit 5 presents a framework for evaluating the emissions and fuel consumption impacts of
implementing traffic signal control systems. As depicted in the flowchart, travel demand
modeling, traffic simulation modeling, and modal emissions/fuel consumption modeling are
linked to form a comprehensive process for evaluating the impacts of traffic signal control
systems. The solid-line arrows represent the base case (without) scenario. The with ITS scenario is
represented by the dashed-line arrows. The base case model uses land use/socioeconomic inputs
in conjunction with highway and transit systems specifications to develop highway volumes and
transit ridership estimates. These values are inputs to the traffic simulation models, which are
used to develop vehicle speed-time profiles. The speed-time profiles and other vehicle operating
characteristics are used in the modal emissions/fuel consumption model for generating estimates
of emissions and fuel consumption impact. Arrows in bold and related text represent points in the
modeling process that are expected to be influenced most by traffic signal control systems.
Because traffic signal control systems are not expected to impact land use, the proposed modeling
11
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
framework for the with ITS scenario does not include a
feedback process to the trip generation step.
Bus and emergency vehicle signal preemption is
expected to affect the shortest path travel time, even
though the shortest path itself may not be impacted.
Reductions in in-vehicle and out-vehicle travel times
translate into modal shifts through the mode choice
model. Thus, the ITS element is modeled in the travel
demand modeling stage. Transit travel time savings
may result in higher levels of transit ridership along the
affected transit line(s). Traditionally, off-line
techniques have been used for measuring emissions
from and fuel consumption of transit vehicles. It may
be possible to use the same off-line techniques for
measuring the emissions and fuel consumption impacts
related to buses and other transit vehicles.
Traffic Signal Control Systems have four
components:
signal coordination
bus signal preemption
emergency vehicle signal preemption
arterial variable message signs.
The following impacts are expected from these
systems:
reduced arterial travel times, implying an
increase in average travel speed
reduced stop/idle delay times for vehicles
traveling on mainlines and at intersections
better response to incidents and special
events
reduced bus round-trip times, implying
increases in speed
some mode shift to/from transit depending
upon the strength of the impact
reduced emergency vehicle response times
Arterial variable message signs (VMS) and signal progression can change highway assignment
via shifts in route choice. Most travel demand models are not capable of accurately capturing
route choice changes caused by minor travel time savings or signal progression. A more accurate
way of modeling signal progression and arterial VMS is to use the network link volumes from
traffic assignment as the starting point. The link volumes, travel time savings, and signal
progression information serve as inputs to the simulation model. Such simulation models as
TRAF-NETSIM/FRESIM and INTEGRATION can model route choice and provide the
acceleration/deceleration characteristics of vehicles in the system for use in modal emissions
models.
Any link-level traffic volume changes resulting from time savings must be captured by means of
the feedback loops from the simulation model to highway assignment, and then from highway
assignment to trip distribution. Any decreases in travel time due to signal control systems should
be fed back to the trip generation and distribution steps.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Exhibit 5
Modeling Traffic Signal Control Systems
Land Use & Socio-
Economic Data
shortest
path tirne
Bus i$ Emergency Vehicle
Signal Pre-emption
Time savings
Shortest path time savings
Higher speeds
shortest
path time
Arterial VMS & Coordination
Arterial time savings
Arterial shortest path time savings
Average travel speed increase
Reduced intersection delay
I
Highway
Assignment
1
4
1 . Link volume
2. Link speed &
Yi
capacity
r
1. Bus ridership
2. Bus speed, runtime
Traffic
Simulation
Jl. Vehicle speed-time graph
12. Vehicle travel time
B Modal
missions
LEGEND:
No ITS element scenario
ITS element scenario
Original vehicle emissions &
fuel consumption
versus
Modified vehicle emissions &
fuel consumption
13
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
The installation of traffic signal control systems is expected on primary and secondary arterials in
urban settings. With time, their coverage may extend to less urbanized areas. The magnitude of
change in overall origin-destination travel times arising from deployment on primary and
secondary arterials can be captured via the feedback from assignment to trip generation or trip
distribution. There may be some route diversions, which may be captured through the feedback
loop from simulation to the traffic assignment step.
2.2.2 Freeway Management
Exhibit 6 presents a methodology for
determining the emissions and fuel
consumption impacts of implementing
freeway management systems. It shows
that freeway management systems do not
only affect the relevant freeway corridor,
but also the entire region. This is because
freeway management systems reduce
freeway travel times while altering ramp
and arterial delays. The size of reductions
depends on the level of congestion on
freeways and the placement of variable
message signs (VMS) on arterials.
Freeway management systems may have
system-wide effects in terms of:
land use shifts
re-distribution of origins and
destinations if travel time savings are
significant
mode shifts from HOVs to SOVs (and
route changes from parallel arterials to
Freeway Management Systems have four components:
ramp metering
variable message signs (VMS)
lane-control
decision support systems, which provide freeway managers with
options for adjusting freeway operations in real time.
The following impacts are expected from these systems:
reduced freeway travel times due to ramp metering
increased ramp travel times and queues
changes in acceleration ramp metering is expected to result in
sharp acceleration events that produce high levels of emissions,
possibly negating the benefits of reduced congestion on the
freeway
dynamic changes in freeway exit-points due to variable message
signs, thus increasing freeway speeds
better utilization of existing freeway capacity stemming from
improvements in lane control
reduced travel times on freeways that may be off-set by
increases in single-occupant vehicle (SOV) travel
increased travel times on arterials due to spillage of vehicles
waiting to get on freeways
increases in HOV travel resulting from HOV bypass to ramp
metering
longer through trips on freeways, given that such trips are
tvnicallv favored over local access trins
vice-versa) on freeways
freeways.
The flowchart presented in Exhibit 6 shows that the highway network and a region's socio-
economics are the primary inputs to the travel demand model. Freeway travel-time savings are not
expected to influence transit travel times, except in the event of bus/HOV bypass at ramps or
extensive freeway use by buses. Consequently, this methodology focuses on the highway
assignment process. Output from the highway assignment process is fed into the freeway
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Exhibit 6
Modeling Freeway Management Systems
Land Use & Socio-
Economic Data
i
Ramp Metering, VMS, Lane Control,
Incident Management & Decision Support
Systems
Freeway travel time savings
Ramp queue length increase
Ramp delay increase
Increased freeway link capacity
Arterial travel time increase at freeway
interchanges
Increase in On-Ramp Acceleration
Rates
Trip^^N.
Generation J
Revised freeway, arterial,
and ramp travel times,
Bus/HOVtravel times
f Transit ^\
\^ Assignment J
1. Link volume
2. Link speed & capacity
4*
r
1. Bus travel times
2. Ridership
1. Freeway travel time savings
2. Ramp time increase
LEGEND:
No ITS element scenario
I. Arterial travel times/speed
2. Local street travel times
Modal
Emissions
Original vehicle emissions &
fuel consumption
versus
Modified vehicle emissions &
fuel consumption
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
simulation model. Traffic simulation modeling is divided into two sequential steps: 1) freeway
simulation, and 2) non-freeway simulation. This is because changes in freeway and ramp travel
times are expected to affect arterial travel times.
The freeway simulation process receives link volume and speed information from the highway
assignment process. In addition, specific information on the nature of ITS deployments is
provided. The output of freeway simulation is freeway link travel times, speed, ramp travel times,
delay, spillage onto arterials in terms of vehicles per hour, and speed-time profiles for vehicles.
Spillage information is then used as an input to the non-freeway simulation process, which then
converts this into arterial travel time changes and delay time changes. Next, the freeway link time
obtained from highway assignment is compared with the simulated freeway link travel time. If
this difference is greater than a predetermined threshold (e.g., five percent), the simulated link
times are fed back into the trip distribution step of the travel demand model. The feedback loop
also contains information about arterial and ramp link travel times. This feedback loop does not
directly connect the simulation model with trip distribution because the former processes
information at the link level, while the latter processes information at the zone level. Traffic
assignment processes information at both the link and zone levels, thereby providing a link
between simulation and distribution. The feedback loop to trip distribution is used to capture
increases in travel on freeways (stemming from route diversion or mode shift) arising from
capacity increases.
In addition, a feedback loop to trip generation is required to capture long-term changes in land use
patterns due to the deployment of freeway management systems. Once the modeled travel times
are within acceptable limits, vehicle speed-time traces serve as inputs for a modal emission/fuel
consumption model (or can be cross-referenced to emissions/fuel consumption look-up tables that
reflect modal events). Emissions and fuel consumption estimates generated by this process can be
compared subsequently to those under the base case scenario.
Overall, freeway management systems may have a significant impact on regional travel behavior
and patterns. Induced travel as a result of changes in travel times can be captured through two
feedback loops - one to trip distribution that will capture shifts in origins and or destinations, and
another to trip generation to capture long-term land use and socioeconomic changes arising from
significant improvements in the efficiency of the affected facilities.
2.2.3 Transit Management
Exhibit 7 presents a methodology for evaluating the emissions/fuel consumption effects of
implementing transit management systems. These systems will directly impact transit route run
times, out-vehicle and in-vehicle times, and transit route speeds. These impacts can increase the
demand for transit services by shifting person-trips from other modes of travel. Transit in-vehicle
16
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
times are expected to fall due to dynamic selection between options, which results in the shortest
path between two stops. Transit management centers can relay information to buses and other
vehicles about any diversions from the scheduled path between any consecutive stops.
The transit assignment process is used to
produce line ridership estimates. These
estimates are compared with observed ridership
as part of a convergence test. If the two values
are not statistically similar, the mode choice
model is suitably modified to reflect
improvements in the amount and quality of
transit-related information. Once the mode
choice model is re-calibrated and the
convergence test is passed, a new transit trip
table is generated. This trip table is subtracted
from the total trip table to produce the highway
trip table. The transit trip table is then assigned
to the transit network, and the resulting line
ridership, run times, and average speeds are
used to produce bus speed-time profiles. The highway trip table, which is obtained by removing
the transit portion from the total, is used for developing estimates of emissions and fuel
consumption.
Transit Management Systems have three important
components:
user information dissemination
automated scheduling
automatic vehicle location (AVL).
The following impacts are expected from these systems:
decreased out-vehicle and in-vehicle times
increased transit system reliability
improved transit fleet utilization, which reduces
operating expenses
improved coordination between transit services such as
bus transfers and bus-rail interface, promoting overall
transit usage
increased opportunity to provide fixed route deviation
during non-peak periods
reductions in labor intensive ridership data collection
which reduces expenses.
Transit management systems are not expected to shift highway-related origin-destination pairs.
Instead, minor changes in highway travel times are expected to change dynamic route choice. This
is captured in the flowchart through the feedback loop from traffic simulation to highway
assignment. A full re-calibration of the highway assignment procedure is not necessary because
transit management systems are not expected to affect route choice. Therefore, the reduced
highway trip table is assigned to the highway network. The resulting link volumes, speeds, and
speed-time profiles are then used by a modal emissions/fuel consumption model to generate
emissions and fuel consumption estimates.
In the short term, dynamic route diversion and time-of-day changes in tripmaking may cause
mode shifts from single occupant vehicles to transit. Changing transit travel times can capture
these mode shifts. In the long term, travel patterns will stabilize, resulting in equilibrium between
the number of transit and non-transit person trips. However, total person trips are not expected to
change as a result of improvements in the level of service of transit systems. Consequently, the
induced demand effects of transit management systems are expected to be negligible.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Exhibit 7
Modeling Transit Management Systems
Land Use & Socio-
Economic Data
i
r
reduced highway^ Increased
j 'trip table Transit Trip Table
^^^^^^^^J
!
1. Vehicle travel time
2. Vehicle speed-time graph
i
:±
Emissions
Model
Original vehicle emissions &
fuel consumption
versus
Modified vehicle emissions &
fuel consumption
1. Line ridership
2. Line speed & run time
shortest
path time
Information Dissemination, Trip
Scheduling, AVL
Transit travel time reduced
Out-vehicle time reduced
LEGEND:
No ITS element scenario
ITS element scenario
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Incident Management Systems have two
important components:
incident detection, verification, and
response
agency coordination.
The following impacts are expected from these
systems:
reduced incident response times
increased travel reliability and changes in
departure times
reduced delays, especially those due to
incidents on highways and freeways
reduced highway travel times
dynamic changes in destination, mode,
and/or route choice.
2.2.4 Incident Management
The incident management system evaluation
methodology is presented in Exhibit 8. This
methodology is similar to the freeway management
systems evaluation methodology presented in Exhibit 6.
The only difference is that instead of performing
sequential freeway and non-freeway simulation, one
combined traffic simulation module is sufficient.
Impacts from the deployment of incident management
systems will be felt at the corridor level. The regional
impacts of these systems are expected to be minor and
short in duration. However, information on incidents
may cause dynamic changes in the destination, mode, or
route choice of travelers, particularly if there is a high frequency of serious incidents with major
delays and unreliable travel times. Therefore, the travel demand model component of an incident
management evaluation methodology must be able to capture the dynamic effects of incident
management systems. In comparison to other ITS components, incident management systems
usually address non-recurrent congestion, and therefore, the modeling methodology must account
for the probability of incidents (by type of incident) to capture the variation in incident
occurrence.
In order to capture potential short-and long-term induced demand effects of incident management
systems, the modeling approach includes feedback from traffic assignment to trip generation and
trip distribution. Incident management systems are expected to have a greater short-term effect on
route and mode shifts than on long-term land use shifts.
2.2.5 Regional Multimodal Traveler Information
Regional multimodal traveler information systems serve two primary functions:
providing information about transportation services and performance of the system to users
providing an information link between the various ITS components.
Information may be delivered to users through public channels such as kiosks, transit in-vehicle
interfaces, television broadcast, radio broadcast, VMS, rideshare matching programs, and the
Internet. Information may be provided by private companies in return for a fee. Such information
would be available through cable television, auto navigation systems, and commercial vehicle
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Exhibit 8
Modeling Incident Management Systems
Land Use & Socio-
Economic Data
i
Incident Management Systems
Reduction in highway network delays
Reduction In stop-and-go traffic
Improved highway travel speeds
Increased reliability of transportation
system
Generation
Distribution
Freeway, arterial, and
ramp travel times,
Bus/HOV'travel times
based on incident
occurrence probability
from observed data
Mode
Choice
f Transit ^\
\^ Assignment J
\. Link volume
2. Link speed & capacity
: I I
I > ^
| f Micro- ^\
! ^^ Simulation /
4*
r
3. Bus travel times
4. Ridership
i
i
1. Travel times/speed
2. Vehicle speed profiles
i
M
r
LEGEND:
E Modal
missions
No ITS element scenario
ITS element scenario
Original vehicle emissions &
fuel consumption
versus
Modified vehicle emissions &
fuel consumption
20
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
routing and scheduling systems. Several different types of information will be available through
regional multimodal traveler information systems:
pre-trip planning information for both surface and air travel
in-vehicle trip planning information especially in the case of unplanned events
rideshare matching information
mayday services information
commercial vehicle routing and scheduling information.
The emissions and fuel consumption impacts of regional multimodal traveler information systems
are driven by improvements in the quality and amount of information on traveler behavior at a
microscopic level. Regional impacts associated with the level and quality of transportation
information provided to the travelling public are expected to be realized in the medium to long
term, since the habits and preferences of travelers may be relatively inflexible in the immediate or
short term. The effectiveness of information dissemination, the user friendliness of information
dissemination channels, the content of information, and user application of improved information
for travel decisions will determine the magnitude of impacts.
Exhibit 9 presents a methodology for evaluating the emissions/fuel consumption-related effects of
implementing regional multimodal traveler information systems. It will be necessary to collect
information on user acceptance by means of user surveys, market penetration research, stated
preference surveys, and revealed preference surveys. It is expected that these systems will affect
all decisions, including tripmaking, destination choice, mode choice, time of travel choice, and
route choice. Thus, data will have to be gathered on these variables under the base case and the
ITS scenario case.
Assuming that it is possible to survey users, the following information will be required to re-
calibrate the four steps of regional travel demand models:
trip production and attraction counts in select residential and non-residential localities
origin-destination surveys on major highway and transit routes
stated preference and revealed preference surveys for mode choice and time of travel choice
behavior
screen-line traffic counts and select link travel times.
Once the traveler behavior coefficients embedded in the travel demand model are adjusted to
represent modified travel patterns, the modified travel demand model can be used for forecasting.
Time of day choice can be modeled via an off-network method using travel demand management
tools. A daily (24-hour) vehicle trip table can be input to a travel demand management model to
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Exhibit 9
Modeling Regional Multimodal Traveler
Information Systems
Travel
time/speed
feedback
LEGEND:
No ITS element scenario
ITS element scenario
Modified production
& attraction factors
1. Vehicle travel time
2. Vehicle speed-time graph
Modal ^^\
missions J
Original vehicle emissions &
fuel consumption
versus
Modified vehicle emissions &
fuel consumption
Level of Information
Transit Schedule
Level of Congestion
Alternate Route
Alternate Time
Alternate Mode
Transit
Assignment
^
ir
1. Ridership
2. Travel times
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
vary the percent of daily traffic occurring during any given hour of the day. This model will
produce hourly trip tables that can be used in the traffic assignment step of the travel demand
modeling phase.
Under the with ITS scenario (represented by the dashed line Exhibit 9), trip generation rates are
modified to adequately reflect the effect of increased information on trip making behavior. Once
modified trip productions and attractions are available, the trip distribution process must be re-
calibrated using new friction factors between zone pairs. The calibrated trip distribution trip tables
can then be used by the mode choice equations to determine mode shares. Stated preference and
revealed preference surveys provide a basis for re-calibrating the coefficients of various mode
choice parameters. The assignment process may be judged based upon the accuracy of the link
volumes predicted by the process. Accuracy may be determined by a comparison with the
observed screen-line volumes. If the assignment results are not close, trip generation can be
modeled again.
Once link volumes and speeds are obtained from assignment, this information will be input into
the traffic simulation package. The traffic simulation package, with link geometry data, is used to
determine the operational characteristics of each individual vehicle. Feedback from simulation to
assignment is used to fine-tune link-level travel times and speeds, which are fed into trip
generation and/or distribution to capture ITS-impacted travel times. Speed-time profiles of
vehicles can then be used by modal emissions/fuel consumption models to generate fuel
consumption and emissions estimates. These estimates can be compared with those for the base
case to determine the relevant impacts of regional multimodal traveler information systems.
Improvements in the quality and amount of travel information are expected to impact traveler
behavior as users make travel decisions based on observed rather than perceived travel
times and costs. In the long term, under the with ITS scenario, travel patterns are expected to
stabilize at a higher level of demand. Differences in long-term demand levels between the with
and without ITS scenarios can be modeled by using feedback loops from traffic assignment to trip
generation. In this manner, the induced travel effects of multimodal traveler information systems
can be assessed.
2.2.6 Other ITS Components
Other ITS components such as electronic toll collection and railroad crossing may be modeled
based on local practices and data availability. Once all the individual components are in place,
they must be integrated into one system that supports travel across all modes and regional
boundaries. System integration is comprised of a set of protocols, methods, mechanics, and
telecommunications elements that bring together the individual ITS components. Once integrated,
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
the effect of individual components is expected to multiply several fold, thus making system
integration an extremely important process in the implementation of ITS.
System integration has the four key dimensions:
integration of information across ITS components such as transit management, freeway
management information, emergency response management information, traffic signal control
information, railroad grade crossing systems, and incident management
integration of information across regions, such as coordination of signals between
jurisdictions to ensure smooth traffic flow
integration of transit management systems in abutting regions to ensure continuous service to
users
integration of incident management information across regions to provide the fastest possible
response to an incident.
To determine the emissions/fuel consumption impacts of system integration, it is important to
determine the inter-relationships between various ITS components. Exhibit 10 presents a
schematic representation of these inter-relationships. It shows that ITS components are closely
linked to each other either directly or through other components.
Multimodal traveler information is the most important component, as it connects all other
components. Emergency and incident management systems are connected to freeway management
systems (FMS), traffic management systems, and transit management systems through the
regional information network. The regional information network consists of a dynamic database
accessed by various means.
The methodology for modeling system integration brings together all the different ITS
components discussed in the earlier sections of this report. Each ITS element is expected to have
individual impacts. But when components are integrated, it is difficult to identify the individual
impacts of each component. Several components are expected to have similar impacts, thus
making the impact more pronounced; conflicting impacts are expected to nullify each other. One
logical way to model this complex system is to include all the individual components in the
methodology. The methodology will require re-calibration at almost every modeling step, as it is a
combination of the enhancements detailed in Exhibits 5 through 9.
Thus, the difficult question to answer especially for ITS implementation is whether or not
integration provides benefits that are greater than the sum of individual ITS components.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Operational efficiencies and inter-jurisdictional coordination may provide integrated benefits that
result in better management of the transportation system. In terms of the impact on emissions and
fuel consumption, only a comprehensive modeling approach can capture the benefits and/or costs
of total system integration.
Exhibit 10
Relationships among System Integration Components
Ottier
Comms
Other
Devices
TOC: Traffic Operations Center
FMS: Freeway Management Systems
ATMS: Advanced Traffic Management System
2.3 Data Issues Relevant to an Evaluation of ITS Impacts
Extensive data support will be required to fully evaluate the emissions/fuel consumption impacts
of ITS deployment. Data will be required for the without and with scenarios. The without
scenario, referred to as the base case, may not include ITS elements. The with scenario builds on
the base case by including ITS deployments and other transportation infrastructure development
programs.
This subsection presents a detailed assessment of the types of data that are needed for an
evaluation of the fuel consumption and emissions impacts of ITS deployment. First, results from
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
an Expert Panel Session7 are summarized to help guide the ITS modeling process. Second, an
overview of a data collection, organization, and retrieval methodology is presented to guide the
data needs assessment. Third, data needs are reviewed for each general category of the data
collection process (e.g., transportation system, emissions and fuel consumption, and ITS
deployment).
A central objective of the Expert Panel Session was to obtain input on the types of data needed to
support the fuel consumption and emissions evaluation process and on the strategies needed to
collect such data. The following general issues were raised by the panelists during the Session.
A comprehensive inventory of ITS deployment initiatives must be developed for the relevant
region. This issue is linked to the need for a well-defined evaluation baseline that will dictate
data needs.
Emissions and fuel consumption data must be evaluated using transportation planning
information to assess the state of the region-specific transportation systems and travel
patterns, and to identify information gaps that must be resolved.
Supplementary data collection plans need to be designed to assist regions in the execution of
primary and secondary data acquisition. Such plans should be based on a comprehensive
assessment of data needs and alternative collection strategies.
Newly developed survey instruments and other data collection strategies should reflect the
entire spectrum of an ITS evaluation.
These issues are further discussed in the following subsections.
2.3.1 Data Availability
As part of their transportation and air quality planning processes, a given region or Metropolitan
Planning Organization (MPO) has extensive information characterizing its transportation system,
the manner in which the system is used by travelers, and the performance of the system in terms
7 An Expert Panel Session was convened on January 16, 1997 to gather information on the types of analytic tools that
are available (or will be forthcoming) for quantifying the fuel consumption and emissions impacts of ITS
deployment. The Expert Panel consisted of the following individuals: 1) Mohan Venigalla of EG&G
Dynatrend/Volpe Center (now with Wilbur Smith Associates); 2) John German of EPA's Office of Mobile Sources;
3) Joon Byun of FHWA's Office of Environment and Planning; 4) Matthew Barth of the University of California at
Riverside; 5) Ron Smith of the Los Alamos National Laboratory; 6) Jim Bunch of Mitretek; 7) Simon Washington of
the Georgia Institute of Technology; 7) the late Greig Harvey of Deakin, Harvey, Skanardonis Associates; 8) Brian
West of the Oak Ridge National Laboratory; 9) Bob Dulla of Sierra Research; and 10) Michael van Aerde of Queens'
University (now with Virginia Polytechnic Institute and State University).
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
of air quality, safety, congestion, and other indicators. The following data sources and/or activities
need to be reviewed to ensure that an evaluation process reflects site-specific conditions.
The relevant region's transportation improvement program (TIP) should be reviewed to
identify the system performance and traveler behavior impacts of other transportation projects
that may be initiated within the ITS deployment time frame. This will ensure that ITS
deployment impacts are not confused with impacts from other transportation investments.
The relevant region's transportation and air quality planning processes should be reviewed to
identify information gaps and opportunities for augmenting planned data collection activities.
For instance, regions periodically execute travel surveys to collect data for input into travel
demand models. Data from the most recent surveys could be used for the ITS modeling
process.
Although regions may currently house useful information, much of the data needed for the
evaluation process will need to be collected from primary and/or secondary sources.
2.3.2 Data Collection Plans
Given that the fuel consumption and emissions evaluation will require a modeling platform that
integrates travel demand models with traffic simulation and fuel consumption/emissions models,
data needs may not be met by current data collection activities at a relevant site. Significant
resources will need to be devoted to data collection. In essence, data collection plans need to focus
on the following needs.
Data characterizing vehicle operations at each site. Some regions have already formulated
plans to deploy probe vehicles to gather speed data along specially equipped facilities.
Similarly, data that reflect spatial and temporal variability in vehicle operations can be
collected by vehicle instrumentation. This will ensure that vehicle operations can be
characterized at the desired level of specificity (e.g., on a second-by-second basis). Data
collected from probe vehicles and vehicle instrumentation can be employed to calibrate the
models used for the evaluation of the fuel consumption and emissions impacts.
Data on system market penetration. A central focus of an ITS deployment plan may be the
integration of traffic management systems with traveler information and public transportation
systems. Consequently, data must be collected to assess the expected market penetration of
traveler information services. Such an assessment is crucial for the estimation of route choice,
mode choice, and induced demand impacts.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Data characterizing traveler behavior. Given that many ITS deployment strategies may focus
on traveler information systems, surveys may be used to collect information on traveler
behavior, especially as it relates to pre-trip planning. The manner in which travelers will
respond to more complete and improved travel information must be assessed using travel
surveys. Surveys may also address traveler behavior changes stemming from congestion
mitigation.
Defining a baseline. The assessment of impacts stemming from ITS deployment depends
largely on the analytic baseline from which differences in traveler behavior and system
performance will be calculated. Baseline definition will dictate the types and quantity of data
needed for the evaluation process. For instance, if the baseline is defined as September 1996,
data on system performance and traveler behavior must be collected for the period between
September 1996 and the evaluation date. This period will correspond to the "before" (or
"without") ITS deployment scenario. These baseline data will then be compared to data
collected during the evaluation period. In contrast, if the baseline is defined to capture ITS
initiatives prior to September of 1996, data on the effects of those systems will need to be
collected or constructed.
Although these data needs were identified during the Expert Panel Session many other general
categories of data will be required for the evaluation process. The appendix details the type of
data needed for an emissions and fuel consumption evaluation of ITS.
2.3.3 Overview of Data Framework
To effectively use information, it is necessary set up a data collection, organization, and retrieval
framework. This will help in reducing the time and cost involved with acquiring accurate data,
and will prevent any duplication of data collection efforts. Exhibit 11 shows a generalized data
collection, organization, and retrieval framework that can be used to guide this process.
Data Collection. Once data needs are defined, data can be collected by one or more planning
agencies at the state, metropolitan planning organization (MPO), or local government levels.
Data collection responsibilities may be shared or individually managed depending upon the
data to be collected. Data collection characterizing a region's transportation system (such as
roadway inventories) may be best performed at the state level, whereas census population data
are available from federal sources. The MPO or the local planning agency may collect site-
specific data on traffic signals, for example. Ultimately, the determination of the responsible
party will be based upon economies of collection, geographic scope, resources, and data uses.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Exhibit 11
Data Collection, Organization, and Retrieval Cycle
Federal State MPO Local
Federal State MPO Local
I
Transportation
Emissions & Fuel Consumption
ITS
Demand Supply Utilization Input Meteorological Output Utilization user Result
Acceptance
Data Organization. The manner in which data are grouped affects the efficiency and stability
of the evaluation process. Data organization should start at the data collection level. Data
should be collected so that little effort is expended in re-organizing them to the format shown
in Exhibit 11. Data could be organized under three major groups: transportation, environment
(i.e., emissions and fuel consumption), and ITS. Under each of the three major groups, data
could be organized in three sub-categories. For example, the three transportation system
categories could be demand, supply, and utilization; the three environment system categories
could be input, meteorological, and output; and the three ITS categories could be utilization,
user acceptance, and result.
Data Retrieval. Data retrieval is directly related to data storage and access technologies. This
implies data sharing and protocol checks among the involved agencies. The data retrieval
system should be based on a platform that is both cost-effective and used widely. Sample
methods are relational databases and GIS-based systems. Data retrieval processes should be
secured to protect against damage to the data at source.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
2.4 Summary of Methodologies
Evaluations of the transportation system efficiency, traveler behavior, and emissions/fuel
consumption impacts of ITS deployments will require significant progressions in transportation
and emissions/fuel consumption models. The modeling approaches presented in this section rely
on an integrated platform that links regional travel demand, traffic simulation, and modal
emissions and fuel consumption models. To date, such integrated models have not been
developed, and their construction will require a significant amount of resources.
Consequently, the methodologies presented in this section delineate the types of analytic and
modeling challenges that need to be considered by an evolutionary progression in state-of-the-art
transportation and emissions/fuel consumption modeling. Future models must be able to capture
the short- and long-term impacts of ITS deployment, including land use and induced travel
effects. Likewise, future models will require highly disaggregate data on vehicle activities to
capture many of the intricate system performance effects of ITS deployments.
The following section summarizes the research efforts to develop state-of-the-art transportation
and emissions modeling. The methodology progressions that characterize these efforts are
important to the eventual development of analytic procedures to accurately and reliably estimate
the emissions and fuel consumption impacts of ITS.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
3. Review of Research Efforts & Models
As part of developing this report, an Expert Panel Session was convened on January 16, 1997 to
gather information on the types of analytic tools that are available (or will be forthcoming) for
quantifying the fuel consumption and emissions impacts of ITS deployment. Input on two key
issues was sought from the Panel members.
What are the most effective analytic tools for assessing emissions and fuel consumption
impacts?
What types of data need to be collected to support modeling needs or impact assessment
requirements?
The objective of the Session was not to achieve consensus on the specific elements of the
modeling methodology, but rather to obtain input to help guide the development of analytic
approaches for an evaluation effort. In this regard, the Session provided useful insight on the types
of analytic processes that need to be employed to successfully estimate the emissions and fuel
consumption impacts of ITS deployment. This insight has served as the basis for the
methodologies presented in this report.
The objective of this section is to review current research that can be used to facilitate current and
future evaluations of the emissions and fuel consumption impacts of ITS. The Session provided
useful overviews of research activities in transportation and emissions modeling. However, to
obtain detailed information on the modeling platforms and research timelines, follow-up
telephone discussions and a literature review were conducted. Although numerous modeling
efforts are underway across the country, this section focuses on the research programs and
available models shown in Exhibit 12.
3.1 Models in Development
A review of each of the referenced models is provided in the following subsections. The model
descriptions that follow include features currently available, as well as those proposed for the
future. The distinction between proposed and available features should be noted when comparing
the capabilities of various models, as features present in existing models may appear relatively
weak when compared with proposed features of models that are not yet available. To adequately
model the fuel consumption and emissions impacts of ITS, a modeling tool or a set of tools must
be developed that incorporates some of the advanced features of the ongoing efforts.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Exhibit 12
Research Programs Reviewed in this Study
Model Type
Traffic Simulation Models
Travel Demand Models
Emissions Models
Combined Transportation Models
Model Reviewed
INTEGRATION Model
FHWA's TRAF-NETSIM Model
General Developments in Travel Demand Modeling
Deakin, Harvey, & Skabardonis (DHS) STEP Model
MOBILE6
UC Riverside's Modal Emissions Model
Georgia Tech's GIS-Based Mobile Emissions Model
UC Berkeley's AIRQ Model
TRANSIMS
Mitretek System's Modeling Efforts
Consequently, continued developments in models that may be functional in the longer term (i.e.,
within three to five years) may become integral to modeling efforts around the nation.
3.1.1 INTEGRATION (v. 2.0) Traffic Simulation Model
INTEGRATION (v.2.0) is a microscopic traffic simulation and dynamic assignment model that
has been proposed for use in the evaluation of ITS implementation. Version 2.0 traces the
movement of individual vehicles on freeways and arterials to a resolution of one deci-second.8
Incorporating a built-in traffic assignment algorithm, the model tracks, on a second-by-second
basis, the spatial and temporal activities of up to 500,000 vehicles operating on a sub-area with a
maximum of 10,000 links. INTEGRATION'S ability to combine arterial and freeway movements
sets it apart from most conventional traffic simulation models.
While INTEGRATION tracks vehicle positions and speeds to within one deci-second, the current
fuel and emissions logic uses values averaged over a one-second period. Fuel consumption rates
are calculated for three modes of vehicle operation: constant speed cruise, velocity change, and
idle. For a given vehicle, the fuel consumption rate (in liters/hour) is modeled as:
a function of travel speed for the constant speed cruise vehicle operation mode
a function of initial and final speed for the velocity change operation mode
a constant during the idle operation mode.
' The name "INTEGRATION" comes from the model's ability to combine movements on arterials and freeways.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
As part of the TravTek Route9 Guidance experiment, the fuel consumption algorithm was
calibrated using information collected from an instrumented 1992 Oldsmobile Toronado
operating in Orlando, Florida. Using published EPA fuel consumption ratings by vehicle model
and a procedure for decomposing EPA's highway and city driving cycles, the fuel consumption
algorithm was generalized to reflect variances across the representative vehicle fleet. Fleet
coverage can be expanded to model the fuel consumption of any gasoline-fueled, light-duty
vehicle for which EPA fuel economy ratings are provided. In this manner, the basis for expansion
is achieved using information obtained from the instrumentation of only one vehicle the
Oldsmobile Toronado and secondary data to approximate the in-use vehicle fleet.
Consequently, INTEGRATION does not truly characterize the in-use vehicle fleet, and does not
account for fuel consumption and emissions attributable to medium- and/or heavy-duty vehicles.
Using relationships between fuel consumption and tailpipe emissions, a compatible emissions
module has been developed and incorporated into the modeling platform. Specifically, correlation
coefficients were developed between the fuel consumption rates for the 1992 Oldsmobile
Toronado (at travel speeds of 0 mph, 19.6 mph, and 55 mph) and the HC, CO, and NOx
emissions rates predicted by MOBILESa for all 1992 gasoline-fueled vehicles traveling at the
referenced speeds. Using curve-fitting techniques, curves describing emissions per gram of fuel
consumed (as a function of average travel speed) were derived independently for CO, HC, and
NOX.10 The effects of cold-start and hot-start operation and ambient temperature are modeled by
correction factors applied to the fuel consumption rates calculated via the relationships specified
above.
Neither the fuel consumption nor the emissions algorithm is sensitive to the levels of acceleration
experienced by a vehicle during a specified trip. However, given that the traffic simulation
component of the model traces vehicle operations on a second-by-second basis and speeds are
updated every deci-second, routines that calculate levels of acceleration can be readily
incorporated into the methodology.
Exhibit 13 depicts the approach for validating and calibrating the fuel consumption and emissions
algorithms imbedded in INTEGRATION (v.2.0). The emissions algorithm is "reversed
engineered" to mimic the drive cycle inherent in MOBILESa. Thus, the current version of
INTEGRATION will produce a different emission profile when using a drive cycle different from
9 Van Aerde, Michael and Baker, Mark, Department of Civil Engineering, Queen's University, Kingston, Ontario,
Canada, Modeling Fuel Consumption and Vehicle Emissions for the TravTek System. Presented at IEEE-IEE Vehicle
Navigations and Information Systems Conference (VNIS), 1993.
10 Note that in predicting fuel consumption, INTEGRATION differentiates between vehicle models (e.g., a Honda
Civic versus an Oldsmobile Toronado). However, the emissions to fuel consumption relationships imbedded in the
model are not sensitive to different vehicle models and only reflect the 1992 model year.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
the default used by MOBILESa. Other characteristics of the fuel consumption and emissions
modules of INTEGRATION are summarized below.
Algorithms incorporate the effect of grade,
, . . , , , , ,
and if regions have grade data at the sub-
area level, they can be incorporated into
the model.
Every trip start is assumed to be a cold
start, and as a vehicle progresses through
the simulation, fuel consumption and
emissions reflect changes in catalytic
converter efficiencies.
Trip segments are accounted for, but trip-
chains are not; the algorithms could be
changed to account for these chains, given
supporting data.
Despite some shortcomings specifically in the
Of modeling
^ 4 Exhibit 13
Conceptual Approach to Fuel Consumption and
Emissions Analysis
STEP I: Code EPA Driving Cycles
(City / Highway) within
INTEGRATION
STEP II: Simulate Movement of
One Vehicle Along the Coded Network
STEP Ilia: Tabulate INTEGRATION
Simulation Results for Fuel
Consumption
STEP Illb: Tabulate INTEGRATION
Simulation Results for HC, CO
and NQ< Emissions
STEP IVa: Tabulate EPA Fuel
Consumption Ratings for
Different Vehicle Types
STEP IVb: Tabulate MOBILE
Estimates at a Range of Temperatures
and for Hot and Cold Engine Modes
STEP V: Compare EPA Fuel Ratings
and MOBILE Emission Estimates to
INTEGRATION Results
STEP VI: Produce Correlation
Coefficients
Source^anAerdeandBaker.QueenWmvemty,^
modal emissions modeling and a bias towards 1992 Oldsmobile model vehicles, INTEGRATION
is a major step in the right direction. It emphasizes the importance of micro-scale modeling to
better capture the fuel consumption and emissions impacts of ITS implementation. Opportunities
to improve the fuel consumption and emissions algorithms of the model will be investigated in
conjunction with work being conducted by other researchers.
3.1.2 FHWA's TRAF-NETSIM Traffic Simulation Model
TRAF-NETSEVI is a microscopic traffic simulation model that tracks the movements of individual
vehicles on a second-by-second basis at single intersections and on freeway segments and ramps.
Unlike INTEGRATION, TRAF-NETSEVI's operating environment does not cover entire freeways
or corridors. However, the range of driving behavior covered by TRAF-NETSEVI includes
velocities between 0 and 110 feet per second and acceleration levels between ± 9 feet/sec2 (i.e.,
acceleration levels between roughly + 7 mph/sec). In this manner, this model is well suited to
capture events outside of the FTP, which only extends to accelerations that are between + 3
mph/sec.
As in the case of INTEGRATION, the modeling capabilities of TRAF-NETSIM have been
expanded to include modules for predicting fuel consumption and emissions. Using a user-
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
specified drive cycle, the current version of TRAF-NETSEVI estimates hot-stabilized emissions of
CO, HC, and NOX as a function of a vehicle's travel speed and level of acceleration.
The relationship between emissions, speed, and accelerations inherent in TRAF-NETSEVI has
been established from data gathered on-road (via vehicle instrumentation) and on a chassis
dynamometer. In the early 1980's, the Oak Ridge National Laboratory (ORNL) developed fuel
consumption models for TRAF-NETSEVI based on testing data covering fifteen light-duty
vehicles. Emissions data were also collected on six of the fifteen vehicles, and results were
employed to develop lookup tables that relate CO, HC, and NOX emissions as a function of travel
speed and acceleration. More recently, ORNL has revised the relationships to include information
collected from tests on eight well-functioning, late-model year vehicles, ranging from a 1988
Chevrolet Corsica to a 1995 Geo Prizm. The testing techniques included:
steady-speed testing conducted on public roads
acceleration testing conducted on an airport runway
emissions and fuel consumption measurements on a chassis dynamometer.
The derived emissions and fuel consumption relationships were incorporated into TRAF-NETSEVI
and then validated against four different drive cycles: the FTP, ARB02, REP05, and HL07.
Results indicate that the relationships imbedded in TRAF-NETSEVI map relatively well to
estimates developed under each of the referenced drive cycles.
While the current version of TRAF-NETSEVI incorporates the revised emissions and fuel
consumption relationships developed by ORNL, FHWA plans to upgrade the model in the
following ways:
Expand the traffic simulation capabilities of TRAF-NETSEVI by linking it to a macroscopic
traffic model specifically, TRAF-NETFLO. This will allow for larger spatial scales than the
single intersections, freeway segments, or freeway ramps that are currently modeled.11
Add modal emissions and fuel consumption lookup tables that represent such specific
facilities as freeways, arterials, and freeway ramps, and such specific driving activities as lane
changing.
Further revise the emissions and fuel consumption relationships to better represent the in-use
vehicle fleet. For example, ORNL is currently soliciting funds to conduct a second battery of
tests on eight malfunctioning vehicles.
11 FHWA is in the process of developing CORSIM. This traffic simulation model will expand on the geographic
representation of TRAF-NETSIM while retaining TRAF-NETSIM's fuel consumption and emissions components.
FHWA is currently testing the relevant algorithms.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
These activities will enhance future versions of TRAF-NETSIM. In the short-term, it may be
possible to use TRAF-NETSEVTs current emissions and fuel consumption relationships to
improve the emissions and fuel consumption modules of INTEGRATION. Doing so will ensure
that the operating environment (i.e., freeways, corridors, arterial networks) inherent in
INTEGRATION is maintained and that a more robust emissions and fuel consumption
methodology is developed.
Some important caveats characterize this option, however. First, TRAF-NETSIM does not
account for trip-based emissions, since it only estimates emissions under hot-stabilized operating
conditions. Second, although the emissions and fuel consumption relationships inherent in TRAF-
NETSIM may be more robust than those inherent in INTEGRATION, they do not reflect the in-
use vehicle fleet. Finally, incorporating the TRAF-NETSIM emissions and fuel consumption
relationships into INTEGRATION only addresses the modal emissions issue, it does not address
the need for feedback mechanisms to travel demand models.
3.1.3 DC Berkeley's AIRQ Post-Processor for Linking Transportation Models
Exhibit 14 demonstrates the process that can be used to link travel demand models (such as
UTPS) with traffic simulation models (such as TRAF-NETSIM). Projected traffic volumes from
the travel demand model can serve as inputs for the traffic simulation models, which are then
executed to produce estimates of the total time spent by vehicles in each driving mode. Travel
speeds predicted by the simulation model are then fed back to the traffic assignment algorithm
employed by the planning model to refine link volumes until a user-specified convergence
criterion is achieved. Given convergence, the simulation models can be executed with the final
link volumes to estimate the time spent in each driving mode (including level of acceleration).
While this feedback routine increases the accuracy of the volume estimates, two issues complicate
the application of this approach in the field.
This model formulation requires multiple microscopic simulations of large-scale networks;
this may not be feasible to simulate given the computer resources typically employed by
practitioners.
The data required to run the models are not readily available to most MPOs. Specifically,
simulation models require detailed network coding at the intersection/approach level, as well
as data on lane channeling and usage, traffic control, etc.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Exhibit 14
Linkage of TJTPS and Simulation Models
NETWORK INTERFACE
Network Refinement
12
PLANNING MODEL
Trip Generation
Trip Distribution
Modal Split
\
r
TRAFFIC ASSIGNMENT
LINK VOLUMES
^Deterministic
"packets" version
The University of California at Berkeley
recently developed a transportation modeling
platform that addresses these constraints, and
can be employed to develop detailed modal
emissions inventories for large urban areas.
Using TRAF-NETSHvI, INTRAS,13 and field
data, UC Berkeley developed relationships
between the time spent in each driving mode
(i.e., cruise, acceleration, deceleration, and idle)
and basic link characteristics based on
simulations of selected surface street networks
and freeway sections in the San Francisco Bay
Area (which encompasses 1,120 zones). To
integrate the simulation models with four-step
travel demand models (specifically UTPS), the
relationships were incorporated in a post-processor model named AIRQ. AIRQ consists of
relationships between link characteristics and the proportion of the total time spent per driving
mode. In turn, these relationships are used to calculate vehicle activity from the travel demand
model outputs.
To decrease the data and coding requirements inherent in the approach depicted in Exhibit 14,
AIRQ incorporates a stratification scheme of individual network links into distinct "link types"
that are a function of facility type, design, traffic, and control characteristics. For each "link type,"
the process centers around two steps.
Using micro-simulation models, vehicle activity data are generated on small-scale networks
for different combinations of link characteristics and demand patterns.
Simulation outputs produce the set of relationships determined by the following functional
relationship:
T,,
F(link type, v/c)
where Ttj is the proportion of time spent on a network link / in driving modey expressed as
a function of the link's type and the volume to capacity ratio (v/c) specific to the link.
12 See Skabardonis, Alexander, TRB Paper No. 97-0123, A Modeling Framework for Estimating Emissions in Large
Urban Areas, January 1997.
13 INTRAS is a microscopic traffic simulation model that traces the second-by-second activities of vehicles operating
along freeways.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
In this manner, the VMT, travel time, volume, and v/c for each link type estimated by the travel
demand model serves as the input for the post-processor to obtain time spent by mode. Exhibit 15
demonstrates the process employed to develop the AIRO model.
To validate the model, predicted volumes and Exhibit 15
speeds were compared with field data over 100 Process for Developing the Proposed Model
representative links in the MTC San Francisco
Bay Area network. Although results were
developed for the typical morning peak hour, the
model can be applied to other time periods by
executing the assignment algorithm of the four-
step travel demand model to obtain link volumes
and speeds specific to a temporal scenario.
Given that results from planning models can be
affected by peak spreading, adjustment factors
can be applied based on volume profiles and
network congestion patterns to modify the
volume estimates per time period.
DEVELOP
POST-PROCESSOR
MINUTP NETMRG File
FORTRAN Code
Source: University of California, Berkeley, January 1997
This approach exemplifies an innovative modeling methodology for linking travel demand models
to traffic simulation models. Even though the Expert Panel Session did not discuss AIRQ, this
model may be useful for the fuel consumption and emissions modeling effort. At a minimum,
results from the model integration routines developed for this effort can be compared to those
developed by UC Berkeley.
Although UC Berkeley's modeling methodology addresses the necessary linkages across travel
demand and traffic simulation models, it does not detail how the model results can be linked to
modal emissions and fuel consumption data to develop a comprehensive methodology for
modeling emissions and fuel consumption impacts of ITS. Efforts are currently underway that
may provide valuable modal emissions data and model interfaces for such a modeling
methodology. These are discussed below.
3.1.4 EPA's MOBILE6 Emissions Factor Model
EPA is in the process of revising and improving MOBILE, its highway vehicle emissions factor
model. The current version of the model, MOBILESa, was released in March 1993, and one
14
interim update to the model has been conducted since then (MOBILE5a_H). Given that four
14 MOBILE5a_H incorporated a number of changes intended to improve the ability of modelers to estimate the
benefits of various innovative inspection and maintenance (I/M) programs and to improve the accuracy of modeling
situations in which such programs are applied to different subsets of the relevant motor vehicle fleet.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
years have elapsed since the last comprehensive revision to MOBILE, and that additional test data
and analyses have become available since 1993, EPA's Office of Mobile Sources (OMS) is
planning significant changes to be incorporated in a new version of the model, MOBILE6.
OMS plans significant changes not only to the underlying emission factor estimates, but also to
how emissions factors are modeled. The most pertinent changes relevant to modeling the
emissions impacts of ITS are described below.
Development of new facility-specific driving cycles. One area of concern with respect to the
accuracy of modeled emission factors has been the methods used to correct emission estimates
based on the Federal Test Procedure (FTP), which represents overall urban area driving. EPA
is improving the accuracy of emission estimates over the range of travel speeds of interest
along specific roadway functional classes. Facility-specific drive cycles have been developed
using second-by-second speed data collected from vehicle instrumentation. EPA is in the early
stages of conducting vehicle tests to characterize emissions under the representative drive
cycles. However, emissions will not be characterized on a second-by-second, or mode-by-
mode, basis. Nevertheless, facility-specific drive cycles will reflect events outside of the
envelope of the FTP, thereby enhancing emission factor estimates. Likewise, facility-specific
drive cycles and emissions estimates will facilitate the integration of emission factor models
with traffic simulation models.
Start emissions and separation of start from running emissions. MOBILE has used operating
mode fractions (describing the portion of overall vehicle miles traveled under cold-start,
hot-start, or stabilized operating conditions) as an input to provide exhaust emission factors in
grams per mile that include start emissions. EPA is proposing to make two major changes in
this area: 1) the provision of start emissions (in grams per vehicle per start) and stabilized
running exhaust emissions in grams per mile, and 2) the modeling of start emissions as a
function of the time that vehicles have been off, or "soak time."
While these changes would strengthen an ITS emissions evaluation process, MOBILE6 will not be
released for use until 1999. Until then, facility-specific drive cycles are available and vehicle
testing is almost complete.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
3.1.5 DC Riverside's Modal Emissions Model
Under the sponsorship of the National
Cooperative Highway Research Program
(NCHRP), The University of California at
Riverside is currently developing a
comprehensive modal emissions model involving
in-house dynamometer testing of over 300
vehicles.15 As conceptualized, the model will -v
predict second-by-second tailpipe emissions
under a variety of driving conditions for a wide
range of light-duty vehiclesvehicles that can be
characterized as well-functioning, deteriorating,
or mal-functioning. A generalized concept of the
model's inputs and outputs is provided in Exhibit
16.
Exhibit 16
UC Riverside Model Input/Output
sec-by-sec velocity, (grade);
or
distribution of modal activity,
average traffic characteristics;
(e.g. average velocity)
fleet -
distribution I
(default) I
«
moda
mode
_
~
optional sec-by-sec
emissions, fuel use
total emissions,
fuel use
Source: University of California, Riverside, November 1996
As depicted in Exhibit 17, the
approach that consists of six i
Exhibit 17
Modal Emissions Model
larameterized physical
air/fuel ratio, 4) fuel-
(1)
Power
Demand
T
^- --
S to i c, li i n m e
-^>
-^
trio^
(2)
Engine Speed
(N)
^.
^
^.
(3)
Air/Fuel
Equ. Ratio
(4)
Fuel Rate
(FR)
^
-*
>-
(5)
Engine
Out
Emissions
^-
^
(6)
Catalyst
Pass
Fraction
A
Tailpipe
Emissions
& Fuel Use
^-
'
1
Source: University of California, Riverside, November 1996
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
rate, 5) engine-out emissions, and 6) catalyst pass fraction. The following vehicle operating
conditions will be included in the model:
cold and hot vehicle starts
normal, stoichiometric operation
high-power enrichment
lean-burn operation.
UC Riverside's modal emissions model is being designed so that it can interface with both
microsimulation traffic models that generate second-by-second speed and acceleration trajectories
along specific links or at a sub-area level, and travel demand models that produce average speed
and total vehicle volume data over a regional network. To ensure that the model can be readily
integrated with transportation models, both temporal and vehicular aggregations are embedded in
the algorithms. Consequently, the model is being built up by measuring second-by-second engine-
out and tailpipe emissions of individual vehicles. In addition, 28 different vehicle/technology
categories (referred to as composite vehicles) have been chosen based on vehicle class (e.g., car or
truck), emissions control technology (e.g., no catalyst, 3-way catalyst, etc.), emissions standard
levels (based on model year), power-to-weight ratio, and emitter level categories (e.g., normal
emitter, high emitter).
Using a bottom-up approach, the basic building block of the model is an individual vehicle
operating on a second-by-second basis. The ultimate goal of UC Riverside's research, however, is
to develop an emissions model that can predict emissions in several-second modes for each
average, composite vehicle category.
As of summer 1998, over 300 vehicles had been tested, analyzed, modeled, and calibrated as part
of the NCHRP effort. These vehicles represent a wide variety of emission-level categories,
ranging from relatively clean vehicles (such as the 1995 Toyota Tercel) to gross emitters (such as
the 1981 Toyota Celica). Results from the vehicle tests contain both engine-out and tailpipe
emissions for HC, CO, NOX, and CO2 under hot-stabilized vehicle operating conditions. The final
model is expected to be completed by the end of 1998.
3.1.6 Georgia Tech's CIS-Based Mobile Emissions Model
Under a cooperative agreement with the Federal Highway Administration and the U.S.
Environmental Protection Agency, the Georgia Institute of Technology is currently developing a
GIS-based emissions modeling process that predicts modal vehicle operations and generates
15 See TRB Paper No. 970706, M. Earth, et. al, The Development of a Comprehensive Modal Emissions Model:
Operating Under Hot-Stabilized Conditions, January 1997.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
mesoscopic estimates of HC, CO, and NOx for a metropolitan area. The methodology allows for
facility-level aggregations of microscopic traffic simulation, or disaggregation of traditional
macroscopic four-step travel demand forecasting models to develop emission-specific vehicle
activity data. Vehicle activity data are joined with fleet characteristics and operating conditions to
produce spatial and temporal (hourly) emission estimates. Emissions estimates are developed for
road segments and zonal areas and aggregated to grid cells for input into photochemical models.
The Georgia Tech model is being developed to be compatible with most of the four-step travel
demand models used by transportation planning agencies.
Vehicles are not tracked through the urban road network in the Georgia Tech model. Instead,
statistical distributions of vehicle activity by facility type and underlying traffic, roadway, and
traffic control conditions are used. This foundation in fundamental traffic engineering principles
allows the model to be transportable to any travel demand modeling system used by metropolitan
areas. Furthermore, GIS technology is well developed in many transportation planning agencies;
this allows modeling features and presentation graphics to be efficiently developed.
Exhibit 18
GIS-based Modal Model Conceptual Design
Land Use Classifications
Transportation Model
Vehicle Registration Data
Road Geometries
Exhibit 18 presents the conceptual
design for the model. The interface
between transportation model
outputs and the modal emissions
module is accomplished via the
development of modal activity
profiles that are a function of traffic
flow, volume to capacity ratios,
average travel speeds, roadway
grades, and facility configuration.
Cold and hot-start emissions are
developed as a function of vehicle
registration data. Engine starts are forecast and allocated at a 'start' zone spatial level, and
running emissions (hot-stabilized and enrichment) are forecast and allocated at the road segment
or link level.
Operating Conditions
Emission rates for CO, NOx, and HC are derived as functions of vehicle technology groups and
modal variables via the use of a refined tree-based regression analysis of vehicle emission test
data from a variety of sources (e.g., the U.S. EPA and the California Air Resources Board). This
results in a database of emissions data collected on over 700 vehicles, representing over 4,000
different vehicle-tests. Vehicle technology groups are broken down by pollutant, model year, fuel
delivery technology, and high versus normal emitter status.
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Exhibit 19 represents a modeled portion of the Atlanta, GA, Metropolitan region from 7:00 to
8:00 AM. The exhibit shows increased levels of HC emissions with increased shading of grid
cells. The following processes characterize the specific vehicle activity algorithms embedded in
Georgia Tech's modeling framework.
Trips generated by transportation models are disaggregated by land use and socioeconomic
data to the census block level a 'start' zone.
Currently, the primary road segments that are modeled are determined by the local MPO's
travel demand model (e.g., TRANPLAN). Other links (local roads) are modeled on a zonal
basis.
Every modeled link is assigned a speed (from Exhibit 19
empirical observation) and acceleration HC Emissions in Modeled Portions of Atlanta
profile that is determined by the
modeled activity on the link, the
road's classification, and its
geometries.
Spatially and temporally allocated
technology group distributions are
developed for each 'start' zone and
road segment and aggregated to grid
cells.
Model validation will be based on two methods: remote sensing for emissions and traffic
monitoring for travel. Remote sensing data has been collected at over fifty sites with observations
totaling more than 300,000 vehicles. These data provide site-specific measurements of CO and
HC, roadway geometry, traffic volumes, and license tag information that can be linked to the
vehicle registration data. The model is currently being validated using these data.
The Atlanta Advanced Traffic Management System (ATMS) data, collected as part of the 1996
Olympics, will provide unique opportunities for assessing the air quality impacts (measured
during the Olympic period) of major changes in activity patterns.
Georgia Tech's GIS-based model reflects a comprehensive and innovative approach to motor
vehicle emissions modeling and may be considered for evaluation of fuel consumption and
emissions impacts of ITS implementation in the future. The automobile component of the model
was completed in 1997, with current efforts focusing on other parts of the model. Further analyses
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
are needed to determine how the model and/or its supporting data can be integrated into the fuel
consumption and emissions evaluation methodology.
3.1.7 Los Alamos National Laboratory's TRANSIMS Model
Georgia Tech's modeling efforts highlight the types of changes in transportation modeling
paradigms that are receiving much attention from the transportation community. In the long term,
it may be possible to do away with established modeling platforms and move toward large-scale
simulation efforts. As evident in ongoing transportation model developments, such efforts are
being aimed at fully integrating transportation and emissions models so that the following key
issues are addressed:
representation of individual traveler and freight movements
representation of environmental and vehicle characteristics
simulation of continuous traffic, transit, freight, bike, and pedestrian activity-based travel
patterns over an extended period of the day, as well as different days of the week, months, and
seasons.
TRANSIMS, which is under development at the Los Alamos National Laboratory, with funding
from FHWA, the Federal Transit Administration (FTA), and EPA, is the only example of ongoing
work to fully deploy a large scale transportation simulation effort that considers each of these
issues. When fully developed, it will represent a new modeling paradigm that goes well beyond
existing transportation modeling platforms.
TRANSIMS is being designed as a system of linked modules with explicit techniques for
feedback and linkages to an emissions module. It is planned that the model will use the emissions
data collected by UC Riverside as part of the NCHRP study to develop a modal emissions model.
TRANSIMS also includes modules that mimic travel behavior and patterns on an activity-by-
activity basis. In general, the flow among the different modules of TRANSIMS may be
summarized as follows.
Given sufficient demographic data, synthetic populations of households are created at the
desired level of detail and distributed to match observed development patterns.
Household activities, activity priorities, activity locations, activity times, mode, and travel
preferences are generated using transportation models.
Individual travel plans are simulated and the demand for travel is determined and assigned on
a second-by-second basis.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Vehicle data on velocities, accelerations, decelerations, average travel speeds, and average
travel times are determined and fed into the emissions module.
Emissions estimates can be fed into EPA's models to assess ambient concentrations of criteria
pollutants at the regional and/or local level.
The Los Alamos National Laboratory is currently developing Interim Operational Capabilities
(IOC) for TRANSEVIS. For instance, a Network Editor is being developed that will modify
transportation system networks to include added detail on intersections. The Network Editor has
been tested as part of a recently completed Dallas IOC case study in which traffic simulation
enhancements were investigated. However, a timeline for the availability of the Network Editor
for use by practitioners has not yet been determined.
Although progress has been made on TRANSEVIS, neither products of IOC case studies nor a
fully operational model will be ready for the public domain in the near future. A fully operational
TRANSEVIS will not be available until 2003.
3.1.8 Mitretek's Travel Demand Modeling Efforts
In an effort to evaluate the benefits, costs and impacts
of ITS, and to evaluate ITS investments under a Major
Investment Study (MIS) process, Mitretek has
developed a methodology that evaluates traditional
and ITS strategies. The methodology is specific to the
transportation needs of Seattle's North Corridor,
extending from the Seattle central business district to
SR526. As part of this effort, Mitretek analyzed
alternatives consisting of hypothetical ITS and
traditional strategies, with the objective of
highlighting and testing analysis methods.
Mitretek's methodology is composed of modifications
to an existing planning model and its linkage to a
simulation model: namely, the Puget Sound Regional
Council's approved Regional Travel Modeling
Process that was modified based on the EMME/2
travel forecasting platform and INTEGRATION
(1.5x). Specifically, a set of measures of effectiveness
Exhibit 20
Pronosed MOEs for Evaluation
Primary MOEs
Derived MOEs
Alternate MOEs
Travel time by mode (HOV, SOV,
Transit)
Throughput (person, vehicle)
Mode choice, Trips by Mode
VMT by mode (HOV, SOV, Transit)
PMTby Mode (HOV, SOV, Transit)
Deferred Trips
Capital Costs
O&M Costs
Value of Time Savings
Delay (recurrent & incident) reduction
Mode Shift from SOV
Congestion Index
Reliability and Variance reduction (Std.
Dev. of arrival times, travel times)
Mobility Index
Accidents
Fatalities
Air Emissions
LOS by Link
Energy Consumed
Equity
Average vehicle occupancy/Transit load
factor
Accessibility
Travel time/Best information travel time
Usefulness of travel information
No. of person trips with error in route/
mode choice due to poor information
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
(MOEs) were defined that were sensitive to ITS strategies and variability in the system. Exhibit
20 lists the candidate MOEs and the measures that will be used in the evaluation.
Once the MOEs were defined, the travel analysis flow was developed for the study. Exhibit 21
depicts the basic process developed by Mitretek for the study. This basic process includes the
following steps:
regional forecasting to predict regional travel patterns and expected travel conditions
a sub-area traffic simulation to capture the operational characteristics and the system variation
during the period of analysis
scenario analysis of representative travel days in the AM peak period to capture non-recurrent
conditions and effects of congestion due to weather, incidents, construction, special events,
etc.
feedback to ensure that the estimated traffic effects in the sub-area simulation is also reflected
in the regional analysis.
The specific enhancements to the PSRC travel forecasting process under this study are listed
below.
Additional facility types, capacities, and speed codes for different ramp types and HOV and
express facilities were introduced, as were additional volume delay functions for ramps.
Additional network detail was included to capture any proposed changes in the sub-area and
to ensure consistency of coding requirements of the simulation model and its interface to the
regional travel model.
The additional link types defined for the study networks include the following:
a High level of service ramps: a ramp connection with free merge at entry and exit
a Low level of service ramps: a ramp connection with control at exit
a Ramp meters: link with ramp meter at exit
a Local access links: neighborhood diversion for access to expressway where direct ramp
does not exist
a HOV bypass ramps: ramp/lane for HOV bypass around ramp meter
a Freeway HOV diamond lanes: freeway HOV in diamond lane configuration
a Freeway barrier-separated HOV: barrier-separated HOV with controlled access/egress
a Arterial HOV: HOV lane along arterial
a Ferry: representing ferries crossing the Puget Sound.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
Feedback mechanisms were developed between planning and simulation models allowing
changes in capacity, delay, and congestion to be captured. Feedback loops between
assignment and trip distribution were already part of the PSRC process.
Exhibit 21
Traffic Flow Analysis
Base Case
For Analysis year
V
Code Alt. Changes in
Regional Networks
1. Traditional
2. ITS
Code Alternative in
Regional Networks
Based upon
Detailed Definition
Base Case Analysis
Regional
Travel Forecasts
Su
h Simulation ^
Area * Td- ^^ *
-----^ 2. ITS ^x>^ ^
i i
Regional 1
Captu
"perceive
for am
(type of d
e\. avei
AM Peak
Code A
Regional
Regions
* ^
L
Feedback to Re
Forecast Pro
"orecast Process Initial Sub-area
res average Traffic Simulation
d" conditions Captures re-current
ilysis period conditions, time
ay, time of day, variation within
-age weekday simulation period,
3 hour period.) traffic operations.
Iternative in
travel patterns
il Diversions
Base Case
Simulation
^ Results
across
Scenarios
cen..n
Combined
Seen 3.
^ Impacts ^
from
i Seen 2. .
T^ Simulations
, . Seen 1 1 j
Incident j
Constr. I
Weather i
>ional i i
:ess 1 j
Scenario
Analysis Calculation of
To Capture change in perceived
[ non-recurrent conditions, statistics
conditions, across scenarios
traveler Feedback to
information regional models
from
Base.
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ASSESSING THE EMISSIONS AND FUEL CONSUMPTION IMPACTS OF ITS
The effect of traveler information, incidents, weather and other conditions on mode choice
were examined.
Mitretek's enhancements to the regional travel modeling process are a contribution to research
efforts that focus on capturing the effects of non-recurrent conditions, especially as they relate to
the incorporation of the effects of pre-trip information on route choice, mode choice, and temporal
trip distributions. This modeling framework could serve as an analytic platform for regions across
the country.
3.1.9 STEP Travel Demand Model
As with any other economic activity that consumes scarce resources, tripmaking involves a cost.
Faced with alternative modes of transportation and routes from an origin to a destination, the
consumer selects a mode and route on the basis of out-of-pocket financial costs, travel time,
comfort, and convenience. In the case of motor vehicle travel, money costs generally include such
operating costs as gasoline, parking, vehicle repair, and toll costs, as well as such ownership costs
as vehicle depreciation and insurance. Costs associated with the time involved in undertaking a
trip are referred to as the time costs of travel. These costs reflect an opportunity cost to the
motorist since the time devoted to travel could be used to generate income, participate in
consumption activities, or engage in leisure. The perceived comfort and convenience of a given
route or mode must also be accounted for in the total cost of tripmaking. These qualitative cost
measures differ from one individual to another, and modes and routes are often ranked differently
by individual travelers based on comfort and convenience. All of these costs taken together are
often referred to as the perceived user cost of travel.
An important element in the derivation of the demand for travel is the effect of carrying capacity
(i.e., facility performance) on perceived user costs. As a given highway becomes congested,
virtually every component of perceived user cost increases. Money costs increase in proportion to
fuel costs and vehicle depreciation (the wear and tear of stop and go driving). Comfort and
convenience also decrease. But more importantly, time costs increase dramatically. As congestion
worsens on a given facility, travelers respond to these rising costs by switching to alternative
modes, routes, or times of travel. In this manner, travel demand on the highway is inversely
related to perceived user costsas costs increase, users demand less travel on the facility.
Likewise, as congestion is mitigated via, for example, either ITS deployment or highway capacity
expansion, perceived user costs decrease, and travelers demand more travel on the affected
facility. These increases in travel stemming from decreases in perceived user costs are called
"induced demand."
Given that ITS deployment is expected to mitigate recurrent and non-recurrent congestion,
corresponding changes in the demand for travel must be captured to ensure that the evaluation
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process considers the full impacts of deployment on fuel consumption and emissions. However,
the inability to capture the effect of changes in perceived user costs on traveler behavior and
travel demand is an important limitation of conventional four-step travel demand models.
In an effort to address the linkages between perceived user cost, traveler behavior, and the demand
for travel, DHS has developed STEP, a travel demand modeling package designed for planning
applications and policy analysis related to transportation pricing issues. STEP is essentially a logit
model based on disaggregate household data. It is composed of an integrated set of travel demand
and activity analysis models, supplemented by a variety of impact analysis capabilities. To
approximate the effects of changes in network performance on travel demand (or vice versa),
STEP incorporates the Bureau of Public Roads (BPR) equation for estimating level of service.16 In
this manner, the calibrated BPR equation can be used to compute a new equilibrium quantity of
travel.
STEP's simplified level of service model, however, is intended only to approximate the effect of
changes in network performance. It is likely to be inadequate in cases where large network
perturbations could occur or where specific route changes are at issue. In those cases, STEP can
be used in conjunction with a more detailed network-based travel demand model.
Several additional features of STEP suggest that this modeling package is useful for a fuel
consumption and emissions modeling framework, especially as it relates to induced travel demand
impact analyses.
STEP's regional, sub-area, and corridor-level analysis capabilities may fit the scope and scale
of the expected travel behavior and system performance impacts associated with ITS
deployment.
STEP's microsimulation formulation permits the modeling package to be used as a survey
tabulation technique employing sophisticated data transformations and linkages. For example,
vehicle data from travel surveys can be tabulated so that exact usage patterns by model year or
vehicle type can be determined.
16 This equation uses peak and off-peak travel times and base case demand estimates to calibrate a supply function
for appropriate spatial groupings of trips (i.e., trips in broadly defined corridors). The basic form of this equation is as
follows:
t = ax(l + (V/C)b)
where, t is travel time in minutes per mile, Vis the volume in vehicles per hour, C is the capacity in vehicles
per hour, and a and b are coefficients fit to each corridor.
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STEP's models are applied using actual or forecast data on household socioeconomic
characteristics, the spatial distribution of population and employment (i.e., land use), and
transportation system characteristics for the selected analysis year(s).
STEP can analyze any change in the transportation system that can be represented in terms of
the variables in its models and associated with a specific geographic area or grouping of
households. For example, a new highway or transit service can be represented by changed
travel times and costs for the areas served.
Over the years, the STEP modeling package has been applied in a number of Bay Area studies
and has been adapted for use in studies in Los Angeles, Sacramento, Chicago, and the Puget
Sound region (Seattle). Currently, STEP is being calibrated for the New York region.
Applications can proceed with model re-estimation for a specific region by creating a new set of
models for STEP. To date, however, nearly all applications outside the Bay Area have relied on
extensive re-calibration of the default models plus a limited amount of re-estimation to match
local conditions.
3.2 Summary of Modeling Developments
Exhibit 22 summarizes the research efforts described in this section and their applicability to the
development of a fuel consumption and emissions modeling framework. Useful information,
methodologies, and data are available from most of the referenced research efforts. For instance,
modal emissions algorithms and data are currently available from TRAF-NETSEVI and will be
available shortly from the UC Riverside work and possibly even from the Georgia Tech work.
Likewise, UC Berkeley's interface methodology has been finalized, and the post-processor can be
used to interface the chosen traffic simulation model to travel demand models.
However, results from current research efforts do not address the entire spectrum of issues that the
emissions and fuel consumption modeling methodology must deal with. For instance, modal
emissions data will only be available for hot-stabilized vehicle operating conditions and will not
represent the full range of variance inherent in in-use vehicle fleets. As a result, gaps in
methodology must be identified and resolved via additional analytic techniques.
3.3 Modeling vs. Measurement
Developments associated with the next generation of MOBILE may facilitate a less resource-
intensive evaluation approach that is based on field measurement techniques. This approach
would rely on MOBILE'S facility-specific emissions factors and on vehicle instrumentation at
each site to develop emissions and via instrumentation fuel consumption impact estimates
for the
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Exhibit 22
Summary of Modeling Developments
Model Contribution Availability
INTEGRATION
TRAF-NETSIM
AIRQ
MOBILE6
Mitretek Systems
Integrated Travel
Demand/Traffic
Simulation Modeling
System
UC Riverside's Modal
Emissions Model
Georgia Tech's CIS-
Based Mobile Emissions
Model
STEP
TRANSIMS
Fuel consumption and emissions
algorithms must be refined.
Can be used to enhance the modal fuel
consumption and emissions algorithms
inherent in INTEGRATION.
Can be used to help interface
INTEGRATION with the chosen travel
demand models.
Drive cycles by functional road class
and improved trip-based emissions
algorithms can be drawn.
By interfacing the regional travel
demand model with a traffic simulation
model, the effects of pre-trip
information on route choice, mode
choice, and temporal trip distributions
can be evaluated.
By interfacing the "interim" model
with the revised (for level of
acceleration) traffic simulation
component of INTEGRATION, results
from this work can be used to estimate
the modal emissions impacts of
deployment.
Emissions component (MEASURE)
can be used to fortify the emissions
algorithm inherent in INTEGRATION.
Potential tool for evaluating the effect
of changes in level of service on travel
demand, particularly induced travel.
Cannot be used.
Currently operational
Currently operational
Currently operational
Release for use in 1999
Currently operational
To be released in late 1998
Beta versions of emissions
component (MEASURE) are
being tested. Not available until
after EPA peer review process
is completed.
Currently operational
Not available until 2003
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without and with ITS scenarios. Specifically, a measurement-based approach would involve three
basic steps.
Instrumented vehicles would be operated at each site to assess changes in the operation
profiles of vehicles for each functional road class.
Data collected from vehicle instrumentation would be used to develop functional road class
drive cycles for the without and with ITS scenarios. In this manner, before and after
speed/acceleration profiles for each functional road class would be generated.
These data would then be mapped to MOBILE6 functional road class-specific emissions
factors to estimate emissions impacts of ITS deployment.
Various issues would need to be resolved if such a measurement-based approach is to be used.
First, as ITS deployment at sites is occurring gradually over time, collecting baseline data requires
a clear definition of the without and with ITS scenarios. Second, fleet representation must be
addressed during vehicle instrumentation. At a minimum, at least one vehicle from each vehicle
class (as defined by MOBILE) must be instrumented. Data extrapolation techniques would then
be needed to capture fleet variability within a classthis is especially needed if fuel consumption
estimates are generated directly from instrumentation. Third, changes in drive cycles resulting
from non-ITS investments or events will not be captured by this approach. Techniques for
isolating ITS-related changes in the speed/acceleration profiles of trips need to be developed.
Finally, emissions and fuel consumption impacts estimated by this approach will not isolate
impacts attributable to the deployment of specific components, unless instrumentation is
conducted before and after the deployment of specific ITS elements. Such an approach may not be
practical.
As a result, although a measurement-based approach can help to validate estimates developed via
an integrated modeling approach, it may not be robust enough to meet the needs of decision-
makers, especially in those regions that will draw on the ITS modeling development process for
guidance. Moreover, as ITS investments are mainstreamed into the transportation planning
process, a measurement-based approach becomes less feasible.
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Appendix: Modeling Data Needs
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This appendix discusses individual data elements that will be required to model ITS components
under the umbrella of modeling techniques discussed in Section 2 of the report. These dataused
as model inputsinclude collected data, projections, and outputs from other models. The data
needs are discussed for each of the following categories: transportation systems; emissions and
fuel consumption; and telecommunications and ITS.
A.1 Transportation System Data
Transportation systems are modeled by means of two techniques:
travel demand modeling
traffic simulation, including vehicle maneuvering modeling.
The two modeling techniques have distinct yet complementary data needs. Travel demand models
require more regional level data, while traffic simulation models require corridor, link, and
individual vehicle level data. Each of these data elements is discussed in more detail in the
following subsections.
A.1.1 Travel Demand Models
Travel demand models are used for predicting the change in transportation system usage or travel
behavior changes stemming from changes in land use and socioeconomic inputs
and changes in the composition of the transportation system (e.g., capacity expansion). Data
requirements for these models are classified under the following categories: demand; supply; and
system performance.
Data needs relevant to each category are discussed below.
Demand
Typically, travel demand is estimated based on economic data, demographic distribution, and
land use data.
Economic Data. For the purpose of evaluating the emissions and fuel consumption impacts of
ITS technologies, regional data on the following economic variables must be identified or
developed:
a income by household
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a employment by groups such as office, retail, industrial, and other
a vehicle ownership by household/dwelling units
a number of households/dwelling units, group quarters, and manufactured homes
a specific special generator data such as supermarkets, stadiums, historic and tourist sites,
clubs, recreation grounds
a projected future growth for each of the economic variables.
Demographic Data. As only a certain section of the total population can drive either because
of legal restrictions or because of personal ability, demographic data are useful in determining
the production factors. The following data variables are to be used for demographic data
needs:
a population by age, sex, population density
a household size, age distribution, number of dependents
a future projections for each of the demographic variables.
Land Use Data. Land use information is used for determining the effect of land use planning
and zoning on transportation system utilization and performance. The following land use
variables are needed for this modeling methodology:
a land area under use for residential and employment purposes
a concentration of housing and employment land uses
a walk access and drive access from residential and non-residential areas to transit stop
locations
a future land use proj ections.
Supply
Information on the supply of transportation systems relevant to travel demand models is available
at the regional level. Supply data can be divided into the following modal groups: highway, rail
transit, bus transit, and carpool.
Highway. The following highway link data are needed to effectively model the entire highway
system:
a road segment length in miles
a number of lanes
a posted speed in miles per hour
a capacity in number of vehicles per lane per hour
a jurisdiction code for regional models
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a road facility type such as local, collector, minor arterial, major arterial, expressway, and
freeway.
Non-Rail Transit. Non-rail transit such as commuter bus, express bus, feeder bus, and
paratransit services have the following data requirements:
a round trip travel time
a bus speed and average stop time
a time to enter and exit from a park-and-ride lot
a bus stop location
a peak and off-peak headway or service frequency.
Rail Transit. Data need to be collected or estimated for the following variables related to rail
transit system supply:
a round trip run time in minutes
a stop locations and stop access methods through walk and drive
a station-to-station fare matrix in current year dollars
a park-and-ride lot locations and access
a peak period and off-peak period headway (service frequency in trips per hour)
a single-track versus double-track service
a direction of service, especially important for commuter rail service.
System Performance
Transportation system performance is measured through regional and more microscopic
utilization measures. Data needs associated with these two broad categories of measures are
presented below.
Regional Measures. Data need to be collected on the following regional measures to check the
performance of travel demand models in predicting regional behavior:
a production/attraction counts at zonal level
a vehicle miles of travel (VMT) by road facility class
a vehicle hours of travel (VHT) by road facility class
a regional mode split at the county level or some other jurisdiction level
a percent of links with volume to capacity ratio greater than 1.0 by road facility class
a origin-destination vehicle and person trips at the county level or some other jurisdiction
level
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a stated preference surveys and revealed preference surveys for transit and HOV lane usage
under ITS scenarios
a average regional trip length by mode of travel
a fare box recovery.
Microscopic Measures. The following data are needed to evaluate the performance of travel
demand models in predicting local corridor or transit line level travel patterns:
a screenline traffic counts in vehicles per hour
a link level travel times in minutes by mode of travel
a link level travel speeds in miles per hour
a percent of total link traffic made up of trucks and buses
a link level volume to capacity ratios
a transit ridership by time of day, also passenger miles of travel
a maximum load point (the segment of the transit route that has the greatest number of
passengers on-board) and maximum load (the maximum number of passengers on-board
at any point during a typical transit vehicle run)
a individual line farebox recovery
a volume to capacity ratio during peak and off-peak periods on roadway facilities where
buses operate.
A.1.2 Traffic Simulation Models
Traffic simulation models use link level data and produce microscopic measures of effectiveness
while simulating the movements of individual vehicles. Specific vehicle movements such as
turning movements, acceleration/deceleration rates, lane changing behavior, yellow-signal
reaction, aggressiveness/defensiveness in driving, passing, gap acceptance, and even accidents
can be modeled using traffic simulation models. Link level measures of effectiveness (MOE's)
such as volume, density, level of service, stop delay, moving delay, volume to capacity ratio,
queue backup, number of signal cycles needed for clearance, emissions, and fuel consumption are
also produced. Some simulation models have animation programs tied to their mathematical
models. This helps in the visualization of the traffic flow along the network.
Data requirements for traffic simulation models are classified under three categories: demand,
supply, and performance.
Demand
Traffic data comprise the demand side of a traffic simulation model. Most traffic models require
that the traffic data be converted to a relatively small time period such as fifteen minutes or less.
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Traffic Data. Traffic models use the following traffic data in conjunction with other data to
simulate traffic in a network:
a link entry volume by lane and by time of day (peak hour, peak period, all-day)
a link travel time
a pedestrian traffic crossing roads
a percent trucks, buses, and heavy vehicles
a turning volume data at intersections and interchanges
a carpool and vanpool trips by time of day
a forecasts for all the above for a future model
a emergency response vehicle time
a time needed to clear an incident, and the associated back up queue.
Supply
The system supply data needs of traffic simulation models include link geometry, operation of
signals and traffic signs, and the following highway operating conditions.
Link Data. Data in this category includes:
a link length in miles including turn lane lengths
a number of lanes including turn lanes
a lane width in feet
a posted speed on links in miles per hour
a capacity of links in vehicles per lane per hour
a lane adds and/or drops for freeway links
a length of acceleration/deceleration lanes
a connections between adjoining links
a grade and radius of curvature
a direction of traffic per lane, especially useful for reversible lanes in urban areas.
Signal and Sign Data. The following data must be collected:
a type of signals on roads
a inter-signal spacing and progression on arterials
a signal phasing and turning movements
a upstream distance of freeway exit signs from the exits
a mean start up delay at each signal
a distance of VMS from nearest downstream ramp or signalized intersection
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a bus preemption and emergency vehicle preemption (yes/no).
Highway Operating Conditions Data. This includes information on wet/dry pavement and
concrete, asphalt or unpaved surfaces.
System Performance
Data on traffic systems performance are needed to check the accuracy of the output from traffic
simulation models. Performance of traffic simulation models is typically judged on the basis of
the following variables:
traffic volume at specific link locations through the use of radar detectors, loop detectors, and
any other means
traffic classification data at specific link locations
number of signal cycles required for clearing an intersection
percent of vehicles exiting or entering at a freeway ramp
A.2 Emissions and Fuel Consumption Related Data
Some of the input data required for estimating motor vehicle emissions and fuel consumption may
be available from the region where ITS deployment is being considered. For instance, some
regions compile information describing the motor vehicle fleet, and have used these data to
execute MOBILE and generate regional emission inventories specific to highway vehicles. Such
fleet characterization data include: registration distributions by vehicle class, fuel type, and
vintage; VMT data by functional road class and time-of-day; ambient conditions data, such as
temperature by season, time-of-day, etc.; and trip data, including number of trips by purpose,
cold-versus hot-start fractions, etc. While some of these data can be readily employed by the
methodologies described in Section 3, more refined data will be needed to accurately model the
fuel consumption and emissions impacts of ITS deployment.
As discussed in other sections of this report, modal emissions models require a different set of
data that characterize vehicle operations and emissions on a second-by-second basis. These data
are being collected through dynamometer tests and will eventually be compiled to reflect the
range of operating conditions (e.g., cold starts, hot-stabilized, etc.) and the range of fleet
characteristics (e.g., engine families, malfunctioning versus well functioning, etc.). Furthermore,
drive cycles are being developed that are more representative of true vehicle operations. Data for
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the development of these drive cycles are being collected via vehicle instrumentation. Together,
modal emissions factors being developed with the help of dynamometer tests and drive cycles
being developed via vehicle instrumentation will be the central components of modal emissions
and fuel consumption models.
Emissions and fuel consumption data will need to be collected under the without ITS base case
and under the with ITS case. It is suggested that data be obtained directly from individual
vehicles so that emissions and fuel consumption estimates can be directly correlated to model
inputs such as vehicle age, engine type and size, and speed-time profile.
The remainder of this subsection focuses on the more rudimentary types of supporting data that
are needed for the emissions and fuel consumption evaluation. The more rudimentary data on
emissions and fuel consumption can be divided into three categories: input, meteorological, and
output.
Input
Input to an emissions and fuel consumption model includes the number of vehicles and their
mechanical and operating characteristics. Data on the following variables typically are required
for these models:
number of vehicles (i.e., link volume)
vehicle mix, such as percentage trucks, buses, other heavy vehicles, and light vehicles
vehicle age including information on engine size, presence of catalytic converters
vehicle speed-time (acceleration/deceleration) profiles
regional vehicle miles of travel (VMT)
regional vehicle hours of travel (VHT)
average trip length by purpose.
Meteorological
The meteorological conditions prevailing during the time-period being modeled include the
important environmental conditions of humidity, temperature, and existing air quality.
Output
Output from emissions and fuel consumption models include emissions and the fuel consumption
rates. The following data variables comprise the output side of emissions and fuel consumption
data:
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total vehicle emissions by pollutant (VOC, NOx, PM)
individual vehicle emissions by speed and acceleration modes
vehicle emissions and fuel consumption by road facility type and signal spacing
savings/reduction in emissions and fuel consumption due to placement of individual ITS
component.
A.3 Intelligent Transportation Systems Data
Section 3.5 describes the need for data collection plans that stress the importance of developing a
comprehensive inventory of ITS deployment initiatives being consider by a region. Data
characterizing ITS initiatives must cover the spatial and temporal dimensions of the systems,
especially as they relate to network location (e.g., VMS location, freeway coverage of CCTVs and
loop detectors, etc.) and temporal operation (e.g., ramp metering). In general, test plans developed
by a region for ITS deployment must provide adequate specificity to support modeling and field
measurement efforts undertaken for the purpose of evaluations. This subsection exemplifies the
types of data that may be needed, especially for traveler information systems.
ITS-related data can be classified into three categories: utilization, user acceptance, and results.
The data are essentially a measure of the level of usage of information being dispensed by various
means; the level of acceptance or a perceived reliability indicator for users; and such results as
permanent or dynamic changes in travel behavior. Each ITS component's performance can be
judged with the help of such data.
A sample scenario of ITS related data collection efforts will clarify the importance of the three
parts mentioned above. Let us assume a scenario in which a traveler is going from Seattle to a
fictitious Phoenix suburb called Abcdef Abcdef is connected to Phoenix by rail and a bus
transfer. The traveler plans the trip to ensure that travel time to the airport and the waiting time
therein are minimized. For this pre-trip planning stage, the traveler may use the Internet,
automated telephone service, or an information kiosk. As soon as the traveler uses such a service,
the utilization counter goes up by one. The traveler interacts with the systems and gets some
specific information. The traveler may or may not use this information depending upon the level
of confidence in the information or depending upon other personal reasons. To find out if the
traveler used such information, a survey might be conducted. This survey will provide data on the
user acceptance/information reliability part of data on ITS components.
Let us further assume that the traveler has decided to use the information provided by the system
to make a travel decision. This decision may be to totally avoid transit, based upon a possibility of
missing a transfer connection between the train and bus to Abcdef. On the other hand, the
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decision might be to use the trip schedule provided by the system. In any event, a result has been
achieved. The result can be a permanent one, implying that every time the traveler goes to the
airport, the same mode/route are adopted. On the other hand, the result may be trip specific. Once
again, a user survey will bring out the permanency of the result. Permanent results can be
classified as static changes in travel behavior whereas temporary results may be classified as
dynamic changes in travel behavior.
The scenario presented above can be applied to a daily commute trip to work, or to an occasional
trip to the supermarket, or to an even less recurrent trip to the beach. In any case, the data needs
are the same and are as follows:
Utilization. Every ITS component has its own user interface device or method. VMS systems
interface through a remote VMS screen, kiosks have the ability to be interactive through touch
screens, in-vehicle information may be through audio/video displays. Since every system has
its specific interface medium, the data collection medium is also the same. Most of the data on
these may be collected at source. Some of the data such as that on non-interactive systems will
be derived from surveys and interviews. Data needs, however, can be generalized into the
following variables:
a number of occasions on which the system was accessed by users in a typical time period
a in case of interactive systems, the number of inquiries made of the system during an
average interaction period
a the types of inquiries: pre-trip planning, in-vehicle route choice, in-vehicle trip planning,
incident response or emergency vehicle response inquiry
a purpose of inquiry: trip planning or reactive to a situation
a all the above information by time of day.
User Acceptance. User acceptance is important to understand the "real" utilization of the
system. The following data will bring out the real utilization of ITS systems:
a number of times the information from ITS was used for: pre-trip planning, in-vehicle trip
planning, route diversion, mode of travel change, destination of travel change in the past
day, week, month. This information needs to be asked in conjunction with the number of
times the traveler interfaced with ITS technologies
a the reliability of information in terms of incident detection and response, transit
management and trip planning, freeway management systems
a the travel time savings perceived due to signal progression and traffic signal control on a
commonly used arterial
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a the travel time savings perceived on a freeway due to freeway management systems
a number of repeat uses of the same system.
Results. Any results in dynamic or permanent changes in travel behavior are included in the
following data variables:
a permanent changes in travel behavior such as the decision to make a trip, destination
choice, mode choice, and route choice
a changes observed in average speeds and travel times on road links and transit lines
a dynamic changes in travel behavior.
The ITS-related information collected and organized into the groups mentioned above is useful in
determining any changes in travel behavior due to the impact of ITS components only.
Information on travel behavior changes feeds into the travel demand modeling stage of the models
presented in Section 3 of this report, thus affecting the emissions and fuel consumption estimates.
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