Technical Report Documentation Page 1. Report No. SWUTC/02/167403-1 2. Government Accession No. 3. Recipient's Catalog No. 5. Report Date March 2002 4. Title and Subtitle Sustainable Transportation: Conceptualization and Performance Measures 6. Performing Organization Code 7. Author(s) Josias Zietsman and Laurence R. Rilett 8. Performing Organization Report No. Report 167403 10. Work Unit No. (TRAIS) 9. Performing Organization Name and Address Texas Transportation Institute The Texas A&M University System College Station, Texas 77843-3135 11. Contract or Grant No. 10727 13. Type of Report and Period Covered 12. Sponsoring Agency Name and Address Southwest Region University Transportation Center Texas Transportation Institute The Texas A&M University System College Station, Texas 77843-3135 14. Sponsoring Agency Code 15. Supplementary Notes Supported by general revenues from the State of Texas 16. Abstract Sustainable transportation attempts to address economic development, environmental stewardship, and social equity of current and future generations. While numerous qualitative studies have been performed on this topic, there has been little quantitative research and/or implementation of sustainable transportation concepts. The main reasons for this are related to a lack of understanding of sustainable transportation and a lack of quantified performance measures. To address this problem, a comprehensive definition for sustainable transportation was developed, as well as a framework on how to identify, quantify, and use performance measures for sustainable transportation in the transportation planning process. The proposed framework was applied to a test bed, comprising two freeway corridors in Houston, Texas. New innovations such as Automatic Vehicle Identification (AVI) data and the Transportation Analysis and Simulation System (TRANSIMS) model make it possible to obtain travel-related information at highly disaggregate levels. This information can be used to quantify sustainable transportation performance measures at the individual level and levels of spatial and temporal disaggregation, which has previously not been possible. The AVI data, the TRANSIMS model, and a number of transportation environmental impact models were used to quantify the performance measures at various levels of aggregation. The performance measures that were quantified on disaggregate levels were compared to measures that were quantified with traditional aggregate data sets. It was found that the traditional approach is much less accurate due to a loss of detail and the effect of aggregation bias. It was illustrated that the performance measures based on disaggregate data can potentially provide different results as compared to aggregate approaches, when used with multi-objective decision-making techniques in transportation planning. Finally, it was demonstrated that the disaggregate approach can be used to allocate responsibility for negative externalities, and to assess the sustainability as experienced by different user groups. 17. Key Words Sustainable Transportation, Performance Measures, Disaggregate, Decision-Making, TRANSIMS, AVI Data 18. Distribution Statement No restrictions. This document is available to the public through NTIS: National Technical Information Service 5285 Port Royal Road Springfield, Virginia 22161 19. Security Classif.(of this report) Unclassified 20. Security Classif.(of this page) Unclassified 21. No. of Pages 163 22. Price Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
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3. Recipient's Catalog No. 5. Report Date March 2002
4. Title and Subtitle Sustainable Transportation: Conceptualization and Performance Measures 6. Performing Organization Code
7. Author(s) Josias Zietsman and Laurence R. Rilett
8. Performing Organization Report No. Report 167403 10. Work Unit No. (TRAIS)
9. Performing Organization Name and Address Texas Transportation Institute The Texas A&M University System College Station, Texas 77843-3135
11. Contract or Grant No. 10727 13. Type of Report and Period Covered
12. Sponsoring Agency Name and Address Southwest Region University Transportation Center Texas Transportation Institute The Texas A&M University System College Station, Texas 77843-3135
14. Sponsoring Agency Code
15. Supplementary Notes Supported by general revenues from the State of Texas 16. Abstract Sustainable transportation attempts to address economic development, environmental stewardship, and social equity of current and future generations. While numerous qualitative studies have been performed on this topic, there has been little quantitative research and/or implementation of sustainable transportation concepts. The main reasons for this are related to a lack of understanding of sustainable transportation and a lack of quantified performance measures. To address this problem, a comprehensive definition for sustainable transportation was developed, as well as a framework on how to identify, quantify, and use performance measures for sustainable transportation in the transportation planning process. The proposed framework was applied to a test bed, comprising two freeway corridors in Houston, Texas. New innovations such as Automatic Vehicle Identification (AVI) data and the Transportation Analysis and Simulation System (TRANSIMS) model make it possible to obtain travel-related information at highly disaggregate levels. This information can be used to quantify sustainable transportation performance measures at the individual level and levels of spatial and temporal disaggregation, which has previously not been possible. The AVI data, the TRANSIMS model, and a number of transportation environmental impact models were used to quantify the performance measures at various levels of aggregation. The performance measures that were quantified on disaggregate levels were compared to measures that were quantified with traditional aggregate data sets. It was found that the traditional approach is much less accurate due to a loss of detail and the effect of aggregation bias. It was illustrated that the performance measures based on disaggregate data can potentially provide different results as compared to aggregate approaches, when used with multi-objective decision-making techniques in transportation planning. Finally, it was demonstrated that the disaggregate approach can be used to allocate responsibility for negative externalities, and to assess the sustainability as experienced by different user groups. 17. Key Words Sustainable Transportation, Performance Measures, Disaggregate, Decision-Making, TRANSIMS, AVI Data
18. Distribution Statement No restrictions. This document is available to the public through NTIS: National Technical Information Service 5285 Port Royal Road Springfield, Virginia 22161
19. Security Classif.(of this report) Unclassified
20. Security Classif.(of this page) Unclassified
21. No. of Pages 163
22. Price
Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
SUSTAINABLE TRANSPORTATION: CONCEPTUALIZATION AND
PERFORMANCE MEASURES
By
Josias Zietsman Associate Research Scientist Texas Transportation Institute
and
Laurence R. Rilett Associate Professor
Texas A&M University
Research Report SWUTC/02/167403-1
Southwest Region University Transportation Center Center for Transportation Research University of Texas at Austin
Austin, TX 78712
March 2002
iii
DISCLAIMER
The contents of this report reflect the views of the authors, who are responsible for
the facts and accuracy of the information presented herein. The document is disseminated
under the sponsorship of the Texas Department of Transportation, University
Transportation Centers Program, in the interest of information exchange. Mention of
trade names or commercial products does not constitute endorsement or recommendation
for use.
ACKNOWLEDGEMENT
The authors recognize that support for this research was provided by a grant from the
U.S. Department of Transportation, University Transportation Centers Program to the
Southwest Region University Transportation Center which is funded 50% with general
revenue funds from the State of Texas.
v
ABSTRACT
Sustainable transportation attempts to address economic development, environmental
stewardship, and social equity of current and future generations. While numerous
qualitative studies have been performed on this topic, there has been little quantitative
research and/or implementation of sustainable transportation concepts. The main reasons
for this are related to a lack of understanding of sustainable transportation and a lack of
quantified performance measures. To address this problem, a comprehensive definition
for sustainable transportation was developed, as well as a framework on how to identify,
quantify, and use performance measures for sustainable transportation in the
transportation planning process. The proposed framework was applied to a test bed,
comprising two freeway corridors in Houston, Texas.
New innovations such as Automatic Vehicle Identification (AVI) data and the
Transportation Analysis and Simulation System (TRANSIMS) model make it possible to
obtain travel-related information at highly disaggregate levels. This information can be
used to quantify sustainable transportation performance measures at the individual level
and levels of spatial and temporal disaggregation, which has previously not been
possible. The AVI data, the TRANSIMS model, and a number of transportation
environmental impact models were used to quantify the performance measures at various
levels of aggregation.
The performance measures that were quantified on disaggregate levels were
compared to measures that were quantified with traditional aggregate data sets. It was
found that the traditional approach is much less accurate due to a loss of detail and the
effect of aggregation bias. It was illustrated that the performance measures based on
disaggregate data can potentially provide different results as compared to aggregate
approaches, when used with multi-objective decision-making techniques in transportation
planning. Finally, it was demonstrated that the disaggregate approach can be used to
allocate responsibility for negative externalities, and to assess the sustainability as
experienced by different user groups.
vii
EXECUTIVE SUMMARY
Transportation is an essential social and economic activity that also results in a
number of negative externalities. The concept of sustainable transportation was
developed to ensure that despite the negative externalities associated with transportation,
the needs of present and future generations can be met. Sustainable transportation can be
viewed as an expression of sustainable development in the transportation sector, and for
this research sustainable development can be defined as follows: sustainable development
is development that ensures intergenerational equity by simultaneously addressing the
multi-dimensional components of economic development, environmental stewardship,
and social equity. It is a dynamic process, which considers the changing needs of society
over space and time. Sustainable development can be viewed as a continuum,
representing various degrees of sustainability. It must, however, be achieved within
resource, environmental, and ecological constraints.
While numerous qualitative studies have been performed on this topic there has been
little quantitative research and/or implementation of sustainable transportation concepts.
Inadequate transportation planning practice is mostly blamed for the poor implementation
record of sustainable transportation. Specific deficiencies include a lack of understanding
and appreciation for sustainable transportation, as well as a lack of quantified measures to
monitor progress and to assist with decision-making. The state of the practice for
quantifying performance measures from both observed and modeled data is based on
aggregate models. Important shortcomings of this approach are the inaccuracies due to a
loss in detail and the effect of aggregation bias. The latest state of the art in transportation
modeling and data collection techniques, however, make it possible to quantify
performance measures at the individual level, as well as a wide range of levels of spatial
and/or temporal aggregation.
The first challenge for implementing the concepts of sustainable transportation,
therefore, is to define sustainable transportation and to provide a framework on how to
identify, quantify, and apply performance measures for sustainable transportation. The
second challenge is to use the latest state-of-the-art technologies in transportation
simulation modeling and data collection techniques to quantify performance measures at
viii
a disaggregate level as compared to the traditional aggregate level. The third and final
challenge is to illustrate how the quantified sustainable transportation performance
measures can be used in the decision-making process related to transportation planning.
The scope of the research was such that the methodologies developed are of a generic
nature that can be applied at both the local and network-wide levels, as well as for a wide
range of sustainable transportation performance measures. The applications, however,
focused on mobility and environmental related performance measures for freeway
corridors. A twenty-two-kilometer section of the Interstate 10 (I-10) corridor and a
twenty-one-kilometer section of the US-290 corridor in Houston, Texas, were selected as
test beds for this research.
Researchers addressed the first challenge by developing a definition for sustainable
transportation, as shown above, and to develop a framework on how to identify, quantify,
and use performance measures for sustainable transportation in the transportation
planning process. The framework is comprised of the following five phases that are inter-
linked to ensure adequate feedback and information flow:
• Identifying performance measures;
• Database development;
• Quantifying performance measures;
• Decision-making; and
• Implementation.
The second challenge was addressed by identifying and quantifying a broad range of
sustainable transportation performance measures. These measures were quantified at the
individual level, as well as various levels of aggregation, by making use of Automatic
Vehicle Identification (AVI) data, the TRANSIMS model, and a number of transportation
environmental impact models. Comparisons were made between the results as obtained at
the various levels of aggregation. It was shown that considerable errors could be
encountered when performance measures for sustainable transportation were quantified at
the traditional aggregate levels. Appropriate levels of aggregation were identified that can
ix
achieve accurate results and at the same time be efficient in terms of computing speed
and memory allocations.
The research illustrated how disaggregate travel information can be obtained and used
to improve the way in which performance measures for sustainable transportation are
quantified. The following are some of the individual contributions of the research:
sustainable transportation is defined and a framework is proposed for identifying,
quantifying, and using performance measures in the decision-making process; the
shortcomings of the current aggregate-based practice and the benefits of the proposed
methodology for quantifying performance measures for sustainable transportation at a
disaggregate level are demonstrated; and a methodology for using performance measures
for sustainable transportation in the decision-making process is proposed.
xi
TABLE OF CONTENTS CHAPTER 1: INTRODUCTION ....................................................................................1 Background....................................................................................................................1 Statement of the Problem ..............................................................................................5 Research Objectives ......................................................................................................6 Contribution of the Research.........................................................................................7 Organization of the Report ............................................................................................8 CHAPTER 2: LITERATURE REVIEW ........................................................................9 Sustainable Transportation ............................................................................................9 Legislative, Planning, and Policy Frameworks ...........................................................14 Performance Measures ................................................................................................20 Modeling Techniques ..................................................................................................28 Concluding Remarks ...................................................................................................35 CHAPTER 3: A FRAMEWORK FOR ACHIEVING SUSTAINABLE TRANSPORTATION .....................................................................................................39 Defining Sustainable Transportation ...........................................................................39 Decision-Making Process for Sustainable Transportation ..........................................42 Concluding Remarks ...................................................................................................50 CHAPTER 4: TRAVEL TIME ANALYSIS FROM ITS DATA................................53 Description of the Test Bed .........................................................................................53 Candidate Performance Measures ...............................................................................54 Description of the AVI Data........................................................................................56 Identification of the Regular Commuters ....................................................................58 Travel Time Estimation ...............................................................................................59 Estimation of Travel Time Variability ........................................................................67 Link-Based Comparison ..............................................................................................72 Concluding Remarks ...................................................................................................75 CHAPTER 5: MOBILITY RELATED PERFORMANCE MEASURES .................77 Methods of Disaggregation .........................................................................................77 Smoothing of Simulated Speed Profiles......................................................................80 Qunatifying Mobility-Related Performance Measures................................................81 Concluding Remarks ...................................................................................................89 CHAPTER 6: ENVIRONMENTAL RELATED PERFORMANCE MEASURES..91 Air Pollution ................................................................................................................91 Noise Pollution ..........................................................................................................106 Fuel Consumption......................................................................................................109 Concluding Remarks .................................................................................................113
xii
CHAPTER 7: APPLICATIONS OF PERFORMANCE MEASURES....................117 Making Decisions in the Context of Sustainable Transportation ..............................117 Allocating Responsibility for Negative Externalities................................................124 Concluding Remarks .................................................................................................133 CHAPTER 8: CONCLUSIONS AND FUTURE RESEARCH.................................135 Conclusions ...............................................................................................................135 Future Research .........................................................................................................137 CHAPTER 9: REFERENCES .....................................................................................141
xiii
LIST OF FIGURES
Figure Page
2.1 Interaction of the World Economy with the Global Ecological System ...............13 2.2 Old and New Paradigms for Performance Measures.............................................25 2.3 Basic Modules within TRANSIMS.......................................................................32 3.1 Illustration of the Definition of Sustainability.......................................................41 3.2 The Dimensions of Sustainable Transportation.....................................................42 3.3 Framework for Identifying, Quantifying and using Performance Measures.........43 3.4 Phase 1: Identifying Performance Measures .........................................................46 3.5 Phase 2: Database Development............................................................................47 3.6 Phase 3: Quantifying Performance Measures........................................................48 3.7 Phase 4: Decision-Making Framework .................................................................49 3.8 Phase5: Implementation Framework .....................................................................50 4.1 Location Map of the Freeway System in the Houston Area..................................54 4.2 Relationship of Frequency of Commuting and Number of Observations .............59 4.3 AAD Corridor Travel Times Calculated with the Link-Based and Corridor-Based Approaches ..................................................................................62 4.4 Mean Travel Times of Regular Commuters and Aggregate Estimates .................64 4.5 Individual Travel Times and Aggregate Estimates Based on the
AAD Approach......................................................................................................65 4.6 Individual Travel Times and Aggregate Estimates Based on the
ABD Approach ......................................................................................................66 4.7 Corridor versus Link-Based Travel Time Standard Deviations ............................69 4.8 Standard Deviation of Travel Time versus Standard Deviation of Entering Time.......................................................................................................................70 4.9 Individual and Aggregate Estimates of Travel Time Standard Deviations ...........71 4.10 Correlation Coefficients of Link Travel Times .....................................................74 5.1 Relationship Between the Different Types of Disaggregation..............................78 5.2 Simulated and Smoothed Speed Profiles of an Individual Vehicle.......................81 5.3 Temporal Variation in Mean Corridor Travel Times ............................................83 5.4 Temporal Variation in Total Delay .......................................................................84 5.5 Temporal Variation in Percentage of the Corridors Congested ............................85 5.6 Spatial and Temporal Variation in Travel Time Variability .................................87 5.7 Spatial and Temporal Variation in Travel Rates ...................................................88 5.8 Spatial and Temporal Variation in Level of Service .............................................89 6.1 Percentage Deviation as a Result of Various Levels of Temporal and Spatial Disaggregation.....................................................................................................100 6.2 Example of Spatial Disaggregation in Noise Pollution .......................................108 6.3 Comparison Between Aggregate and Disaggregate Scenarios............................111 7.1 Layout of the I-10 Corridor and the Locations of the AVI Stations....................117 7.2 Normalized Utility Values for the Various Approaches .....................................123
xiv
LIST OF TABLES
Table Page
2.1 Defining the Dimensions of Sustainability............................................................11 2.2 Negative Externalities Associated with the Transportation Sector .......................13 2.3 Policies to Assist in Achieving a Sustainable Transportation System ..................19 2.4 Typical Levels of Aggregation of Performance Measurement .............................22 2.5 Attributes of a Good Performance Measure ..........................................................24 2.6 Objectives and Performance Measures for sustainable Transportation.................26 4.1 Performance Measures for a Transportation Corridor...........................................56 4.2 MAPE Between Link-Based and corridor-Based Travel Time Estimations.........63 4.3 MAPE Between Individual Observations and the Two Levels of Aggregation....67 6.1 Vehicle Classes and VMT Mix Used in the MOBILE5a Model...........................92 6.2 MAPE Between the Interchange Links and the Two More Aggregate Scenarios ............................................................................................109 6.3 Deviation Between the Base Case and Various Levels of Aggregation..............112 7.1 Alternatives to be Evaluated................................................................................118 7.2 Criteria and Sub-Criteria Weights .......................................................................122 7.3 Normalized Utility Values in Percentage ............................................................124 7.4 Equivalency Factors for the Various Vehicle Classes.........................................128 7.5 Aggregate Costs as a Result of Vehicular Emissions..........................................129 7.6 Disaggregate Costs as a Result of Vehicular Emissions .....................................132
1
CHAPTER 1: INTRODUCTION
BACKGROUND Sustainable Transportation
Transportation is an essential social and economic activity that also results in a
number of negative externalities, which include (1): i) air pollution; ii) noise pollution;
iii) accidents; iv) energy use; v) congestion; vi) depletion of oil and other natural
resources; vii) social disruption; and viii) damage of landscape and soil. These negative
externalities are associated with all facets of the transportation lifecycle that include the
production of vehicles, their use, and ultimately their disposal. The fact that the rate of
the worldís motor vehicle growth is projected to outpace the worldís population growth
is, therefore, a major concern (2). In the United States, for example, it was estimated that
over the past twenty-five years the rate of increase in drivers was seventy-two percent
compared to an increase in population growth of only twenty-three percent. Also, during
the same period the rate of increase in household vehicles was estimated to be more than
six times the rate of population growth (3). Planners and environmentalists have
predicted that such trends will result in economic, social, and environmental needs of
both current and future generations not being met. This challenge led to the creation of
the concept of sustainable development.
The term sustainable development was introduced as early as 1980, and in 1987 the
report by the World Commission on Environment and Development (the so-called
Brundtland Commission) provided a definition for sustainable development that is still
widely used (4): ì development that meets the needs of the present without compromising
the ability of future generations to meet their own needs.î The Presidentís Council on
Sustainable Development, which President Clinton established in 1993, subsequently
adopted this definition (5). Sustainable transportation can be seen as an expression of
sustainable development in the transportation sector and it can be defined as follows (6):
ì sustainable transportation involves infrastructure investments and travel policies that
serve multiple goals of economic development, environmental stewardship, and social
equity. The objective is to optimize the use of the transportation system to achieve
2
economic and related social and environmental goals, without sacrificing the ability of
future generations to achieve the same goals.î
The concepts and principles associated with sustainable transportation are well
documented and are supported by many decision-makers. These concepts and principles
are related to the dimensions of sustainable development and include the improvement
and protection of the following aspects (7):
• employment;
• efficiency;
• livability;
• equity;
• safety and security;
• accessibility;
• mobility;
• and environmental protection.
Although these are all laudable goals, the challenge remains to insure that they are
implemented. Methodologies for their implementation in a consistent and comprehensive
manner, however, are virtually nonexistent. Sustainable transportation can be considered
as one of the most debated but least applied concepts in urban and transportation planning
(8).
Many authors have investigated possible deficiencies with regard to current
transportation planning practice and identified the following as key areas for
improvement (8-15): i) the lack of understanding and recognition of the increasingly
important social, economic, environmental, and public policy issues; ii) the lack of
practical guidelines on how to address these challenges; iii) the lack of quantified
measures so that progress can be monitored and decisions made; and iv) the lack of co-
ordination between decision-makers and other stakeholders.
These deficiencies can to a large extent be addressed if the concepts associated with
sustainable transportation are clearly defined and quantified. The reality, however, is that
the sustainability implications of transportation have not been quantified and are even
3
qualitatively unclear (10). The reasons why sustainable transportation has not been
adequately quantified can be summarized as follows (13,14):
• Sustainable transportation is a fairly new concept of which the objectives and
scope of activities are unclear;
• There is a lack of guidelines for identifying appropriate performance measures;
• The current state of the practice in terms of modeling and planning techniques is
too limited in its level of accuracy and detail to adequately quantify sustainable
transportation performance measures; and
• Even if sustainable transportation performance measures can be adequately
quantified, it is unclear how to make trade-offs and decisions in a consistent and
unbiased manner.
Performance Measures for Sustainable Transportation
The first challenge, therefore, is to identify appropriate performance measures for
sustainable transportation. Performance measures for sustainable development can be
defined as (16) ì various statistical values that collectively measure the capacity to meet
present and future needs as well as public policy goals and outcomes.î Performance
measures have a number of specific applications, but in general they are used to assist
decision-makers in making informed decisions (17,18).
The importance of performance measures related to sustainable transportation has
been widely recognized. Gardner and Carlsen state that (19) ì if we are to make good
decisions about policy relating to sustainable transportation we need reliable
information on the state of the environment and the factors that impact upon it.î The
Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) and the
Transportation Equity Act of the 21st Century (TEA-21) make reference of performance
measures and their use in monitoring different policies related to sustainable
transportation (20,21). To adhere to the requirements of these pieces of legislation,
performance measures are required to address environmental, social, and economic
objectives in addition to the general transportation objectives. Specific criteria such as
mobility, connectivity, accessibility, energy efficiency, air quality, noise, safety,
4
neighborhood impact, resource impact, and economic development will have to be
addressed (22).
A number of performance measures that have historically been used for other
purposes, such as mobility and congestion studies, can potentially be used for quantifying
sustainable transportation. These performance measures up to now have been quantified
with data that are aggregated over many vehicles. The researchers in this study postulated
that the capability of modeling travel characteristics on a disaggregate level can improve
the accuracy with which such performance measures are quantified. This is the case
because a number of negative externalities such as vehicular emissions are inherently
nonlinear. In these situations aggregate approximations may result in considerable error.
More importantly, considering only averages or aggregate information can cause analysts
to overlook a number of crucial sustainability issues. (23).
Modeling Techniques for Sustainable Transportation
The second challenge is to adequately quantify performance measures for sustainable
transportation. In the case of base year conditions, performance measures are mostly
quantified with survey data, although there are a number of performance measures or
applications for which data need to be manipulated with models. Predictions of future
conditions are exclusively made with the aid of models. Models used for sustainable
transportation analysis include transportation planning models, traffic simulation models,
transportation environmental impact models, and economic models.
The current state of the practice in terms of transportation planning modeling is the
so-called four-step travel demand model. This is a macroscopic approach that results in
aggregate vehicle flows on the selected network (24). New innovations in transportation
modeling, however, make it possible to obtain travel information at the disaggregate
level. An example of such a model is the Transportation Analysis and Simulation System
(TRANSIMS) model that is currently under development by the Los Alamos National
Laboratory (25). On the data collection front, the advent of Automatic Vehicle
Identification (AVI) has also made it possible to monitor travel characteristics on a
disaggregate level (23). Transportation environmental impact models include air
5
pollution models, noise pollution models, and energy consumption models. Economic
models are used to determine the economic implications of transportation.
Decision-Making for Sustainable Transportation
The third challenge is to use performance measures for measurement and decision-
making in the context of sustainable transportation. Because the transportation system is
comprised of a complex system with conflicting economic and environmental objectives,
it is necessary to use a decision-making technique that can consider the multiple and
conflicting objectives (26). Single-objective decision-making techniques such as benefit-
cost analysis (which is based on monetary values) are not adequate to deal with the
complexities associated with sustainable transportation (27). Various multi-criteria
decision-making techniques have been developed to deal with this complex problem.
STATEMENT OF THE PROBLEM
The actual implementation of sustainable transportation concepts up to now have
been very disappointing, and successes are few and far between (28). The reason for this
is that the concept of sustainable transportation is still unclear and it has not been
adequately quantified. There is, therefore, a need to clearly define sustainable
transportation and to show how performance measures can assist in quantifying it.
Because sustainability requires a more integrated view of the world, traditional
performance measures that just look at specific transportation related aspects are often
not very useful as indicators for sustainable transportation (29). The challenge is to assess
the outcomes of transportation programs and policies in terms of the broader
sustainability goals of economic, social, and environmental sustainability. There is a
myriad of possible indicators that can fall into the realm of sustainable transportation
(30). The selection of appropriate performance measures is very important because they
direct the focus of planners and decision-makers. Because a poor selection of
performance measures can lead to poor decisions and outcomes, there is a need to
propose an approach for identifying appropriate performance measures for sustainable
transportation (30,31).
6
Decision-makers need accurate information on performance measures to be able to
make informed decisions. Some of these performance measures can be quantified on an
aggregate level, whereas others can only be accurately quantified on a disaggregate level.
Performance measures for sustainable transportation based on disaggregate data currently
are virtually nonexistent. This is because, until recently, there has been a lack of adequate
data, tools, and techniques to quantify performance measures at a disaggregate level (32).
The latest state-of-the-art transportation simulation models and data collection
techniques, however, are able to provide travel-related information at a disaggregate
level. There is, therefore, a need to develop procedures with the latest transportation
modeling and data collection techniques to quantify additional performance measures and
to improve the accuracy with which some existing performance measures are quantified.
The process by which alternatives are to be formulated, evaluated, and selected is
becoming more constrained in terms of required procedures and outputs (33). Multi-
criteria decision-making techniques have the potential to deal with the complexities
associated with sustainable transportation (27). It is, therefore, necessary to investigate
existing multi-criteria decision-making techniques to determine whether they are suitable
for dealing with the decision-making problems regarding sustainable transportation, and
to propose a suitable technique.
RESEARCH OBJECTIVES
This research begins with the hypothesis that the accuracy with which performance
measures for sustainable transportation are quantified can be improved by quantifying
such measures on a more disaggregate level. The objective of this research is to develop
and apply a methodology through which disaggregate travel information can be used to
supplement traditional aggregate travel information in quantifying performance measures
for sustainable transportation and to use such quantified measures in the decision-making
process. The research will focus on the following elements:
• To define sustainable transportation and propose a mechanism for identifying
appropriate performance measures for sustainable transportation;
7
• To use AVI data and the state-of-the-art in transportation simulation models to
quantify travel related performance measures at aggregate and disaggregate
levels;
• To use traffic simulation models and environmental impact models to quantify
environmental related sustainable transportation performance measures;
• To conduct various comparisons between the aggregate and disaggregate
approaches; and
• To illustrate the application of sustainable transportation performance measures
through the use of a multi-criteria decision-making technique and the allocation of
responsibility through equivalency factors.
The scope of the research will be such that the methodologies that are developed will
be of a generic nature that can be applied at both the local and network-wide levels, as
well as for a wide range of sustainable transportation performance measures. The
applications, however, will focus on mobility and environmental related performance
measures for freeway corridors.
CONTRIBUTION OF THE RESEARCH
Performance measures for sustainable transportation up to now have only been
quantified in very limited cases. Even when such measures have been quantified, it was
based on aggregate datasets. This research will illustrate how to obtain disaggregate
travel information and use it to improve quantification of performance measures for
sustainable transportation. The following are some of the individual contributions of the
research: i) sustainable transportation is defined and a framework is proposed for
identifying, quantifying, and using performance measures in the decision-making
process; ii) the shortcomings of the current aggregate-based practice and the benefits of
the proposed methodology for quantifying performance measures for sustainable
transportation at a disaggregate level are demonstrated; and iii) a methodology for using
performance measures for sustainable transportation in the decision-making process is
proposed.
8
ORGANIZATION OF THE REPORT
The report has been divided into eight chapters. Chapter 1 includes an introduction to
the research and covers aspects such as background, statement of the problem, research
objectives, methodology, contribution of the research, and organization of the report.
Chapter 2 provides a literature review of the state of the art of the main topics of this
research. It includes a review of sustainable transportation, legislative and policy
frameworks, performance measures for sustainable transportation, modeling techniques,
and decision-making for sustainable transportation.
Chapter 3 provides a framework for achieving sustainable transportation. It contains a
proposed definition for sustainable transportation, the decision-making process for
sustainable transportation, and some candidate performance measures. Chapter 4 contains
an illustration on how travel time and travel time variability can be quantified at various
levels of aggregation by using AVI data. Chapter 5 illustrates how a wide range of
mobility related performance measures can be quantified at various levels of aggregation
by using a transportation planning model, TRANSIMS.
Chapter 6 illustrates how environmental related performance measures such as
vehicular emission, noise pollution, and fuel consumption can be quantified at various
levels of aggregation by using a traffic simulation model and environmental models. The
implication of quantifying all the above-mentioned performance measures at the various
levels of aggregation is discussed. Chapter 7 includes two applications of performance
measures for sustainable transportation, namely: using performance measures in a multi-
criteria decision-making technique; and allocating responsibility to motorists for
generating negative externalities.
Chapter 8 contains the conclusions and a proposal for future research.
9
CHAPTER 2: LITERATURE REVIEW
In Chapter 1 this report identified a number of needs that have to be addressed in
order to ensure the effective and efficient implementation of the concepts of sustainable
transportation. This chapter contains a literature review on the state of the practice with
respect to identifying and addressing these needs. The main focus areas of the literature
review are: sustainable transportation; legislative, planning, and policy frameworks;
performance measures; and modeling techniques.
SUSTAINABLE TRANSPORTATION Evolution of the Concept of Sustainable Transportation
In order to obtain a thorough understanding of the concept of sustainable
transportation it is instructive to explore its evolution. While the term sustainable
development is fairly recent, some principles associated with it date back to the
eighteenth century economist and philosopher Thomas Malthus. He theorized that
temporary improvements in human living standards would trigger population surges,
which would outpace technological growth and resource availability (34). These theories
were rekindled during the early 1960s when there was a growing concern over the human
impact on the environment (2). In the 1970s scientists identified some specific concerns
such as global warming, acid rain, depletion of the ozone layer, excessive population
growth, loss of tropical forests, and biological diversity (2). The term sustainable
development was first used by the World Conservation Strategy (WCS) in 1980. They
stressed the interdependence of conservation and development and emphasized that
humanity is part of nature and has no future unless people conserve nature and natural
resources (2).
In 1987 the report by the World Commission on Environment and Development (the
so-called Brundtland Commission) re-emphasized the importance of sustainable
development and provided the widely used definition for sustainable development, as
included in Chapter 1 (4). The United Nations Conference on Environment and
Development (UNCED), which was held in Rio de Janeiro in 1992, gave the concept of
10
sustainable development the status of a global mission through the adoption of the so-
called Agenda 21 (35).
The momentum for achieving sustainable development accelerated during the 1990s
and there are currently numerous initiatives of sustainable development across the world,
particularly in Europe, Canada, and the United States. Important initiatives in the United
States include the Presidentís Council on Sustainable Development and the Livability
Agenda of the President and Vice President. The mandate of the Presidentís Council on
Sustainable Development is to advise the president on key sustainability issues (5). The
Livability Agenda focuses on strengthening the Federal role in support of state and local
efforts to build livable communities for the twenty-first century (36).
Definitions for Sustainable Development and Sustainable Transportation
The concept of sustainability has been much debated and argued over. A number of
authors have provided definitions for sustainable development and sustainable
transportation (4,17,37-42). The definitions for sustainable development are fairly wide
ranging although they all include some type of reference to intergenerational equity,
where the goal is to ensure a quality environment for current and future generations.
Sustainability, therefore, refers to long-term availability of adequate resources that are
necessary for the achievement of pre-specified goals. Development and growth should
also be maintained within the ecological boundaries and should not extend beyond the
carrying capacity of the natural environment. Sustainable development is, therefore, a
dynamic concept that takes into consideration the expanding needs of a growing world
population, including its entire social, economic, ecological, geographic, and cultural
dimensions (28). It should also be noted that the concept of sustainability should be
viewed as a continuum, representing varying degrees of sustainability and
unsustainability (2).
Sustainable transportation is an expression of sustainable development in the
transportation sector. The challenge is to make transportation sustainable by addressing
its consumptive nature of renewable and non-renewable resources, as well as its
environmental impacts. Large institutions such as the World Bank and the Organization
for Economic Co-operation and Development (OECD), as well as various other authors
11
have provided definitions for sustainable transportation (6,17,32,35,39,42,43,44). These
definitions are all based on the broader concept of sustainable development and are
concerned with meeting current and future mobility and accessibility needs without
resulting in undue negative externalities. Table 2.1 is a description of what is understood
with each of the dimensions of sustainability (7,17).
Table 2.1 shows that the sustainability dimensions cover a broad range of issues that
affect the quality of life of current and future generations. It is important to realize that
true sustainability can only be achieved if all three dimensions are simultaneously
addressed (43). This is a huge challenge because transportation results in a number of
negative externalities along with its economic and social benefits. These effects will be
discussed in the following chapter.
TABLE 2.1 Defining the Dimensions of Sustainability
Sustainability Dimension
Description of Sustainability Dimensions
Social equity
• People must be able to interact with one another and with nature. • A safe and secure environment must be provided. • There must be equity between societies, groups, and generations. • It includes issues such as equity, safety, security, human health, education, and quality of life.
Economic development
• Resources need to be adequately maintained. • Financial and economic needs of current and future generations must be met. • It includes issues such as business activity, employment, productivity, tax issues, and trade.
Environmental stewardship • Use renewable resources at below their rates of regeneration and non-renewable resources at below the rates of development of renewable substitutes. • Provide a clean environment for current and future generations. • It includes issues such as pollution prevention, climate protection, habitat preservation, and aesthetics.
12
Negative Externalities
The economic system takes renewable and non-renewable resources from the
environment, processes them to derive some benefits and then discards what is left as
different forms of waste into the environment. The only continuous external input into the
global system is solar energy, and the only output leaving the system is low-level heat.
The dumping of waste streams may lead to substantial and sometimes irreversible
damage to the environment. The interests of future generations are damaged: if non-
renewable resources are used without enabling the production of full substitutes; if
renewable resources are used faster than they can be reproduced; or if more waste is
dumped into the environment than the ecological systems can safely absorb (45).
Figure 2.1 shows how the world economy interacts with the global ecological
subsystem. In this figure the economic subsystem is represented as the inner circle in the
diagram and the global ecological system as the outer circle. In an unsustainable situation
the size of the economic subsystem continues to increase up to a point that the ecological
system is not able to accommodate it anymore (45). Transportation plays a key role in the
economic system and, therefore, has a major impact on the ecological system. Results
showed that transportation can typically represent ten percent of a nationís gross
domestic product and is responsible for twenty-two percent of the global energy
consumption and twenty-five percent of fossil fuel burning across the world (2,46). Table
2.2 provides a brief description of each of the negative externalities associated with
transportation.
13
FIGURE 2.1. Interaction of the World Economy with the Global Ecological System (Adopted from 45). TABLE 2.2 Negative Externalities Associated with the Transportation Sector Air pollution Noise pollution Traffic accidents Global climate change Energy use Congestion Social disruption Resource use
Water pollution Consumption of land Urban sprawl Loss of habitat Hazardous materials Vibration Visual intrusion and aesthetics Waste disposal problems
SUN
Economic Subsystem
Non Renewable
Renewable Resources
Waste
Damages
Recycling
Waste Heat
Solar Energy
Time
Global Ecological System
14
LEGISLATIVE, PLANNING, AND POLICY FRAMEWORKS Legislative Framework
Legislation forms the basis for transportation planning practice. It is, therefore,
necessary to understand the relevant legislation when attempting to plan for a sustainable
transportation system. The following are Federal laws in the United States that can have
an affect on sustainable transportation (47):
• Urban Mass Transportation Act of 1964;
• National Historic Preservation Act of 1966;
• Department of Transportation Act of 1966;
• Housing and Urban Development Act of 1966;
• National Environmental Policy Act of 1969;
• Noise Control Act of 1972;
• Federal Aid to Highways Act (Various years);
• Clean Water Act (with major amendments in 1972, 1977, and 1987);
• Clean Air Act (with major amendments in 1965, 1970, 1977, and 1990);
• Transportation Equity Act of the 21st Century (1997).
The watershed legislation in terms of transportation planning in the United States was
the Intermodal Surface Transportation Efficiency Act of 1991. This act implicitly
supports the goals of sustainable transportation, and its three-part philosophy is stated as:
i) decentralization; ii) friendlier to the environment; and iii) more responsive to the needs
of increasingly diverse populations and businesses (48). This philosophy can be achieved
by promoting transportation systems that maximize mobility and accessibility and
minimize transportation related negative externalities. The Transportation Equity Act for
the 21st Century, which builds on the initiatives of ISTEA, was signed into law in June
1998. Some of the significant features of TEA-21 include: i) a guaranteed level of
Federal funds for surface transportation through fiscal year 2003; ii) extension of the
15
Disadvantaged Business Enterprises (DBE) program; iii) strengthening of the safety
programs; iv) continuation of the highways and transit initiatives under ISTEA; and v)
investing in research and its application to maximize the performance of the
transportation system (21). Apart from these pieces of legislation, the Clean Air Act
Amendments (CAAA) of 1990 and the National Environmental Policy Act (NEPA)
process ensure that air pollution associated with transportation is addressed (20).
Planning Framework
ISTEA and TEA-21 also outline transportation planning requirements of state
departments of transportation and metropolitan planning organizations (MPOs). These
requirements must be followed in order for these levels of government to receive Federal
funding for transportation projects. Metropolitan areas are required to develop long-term
(twenty-year) Metropolitan Transportation Plans (MTPs) and short-term (three-year)
Transportation Improvement Plans (TIPs). The metropolitan areas provide their TIPs to
the state so that it can prepare a Statewide Transportation Improvement Plan (STIP) (14).
Where the planning process identifies a problem in a corridor or sub-area that suggests
the possible need for a major investment using Federal funds, a Major Investment Study
(MIS) may be required. The purpose of a MIS is to analyze solutions to address
substantial transportation problems and to present this information to decision-makers
(20).
The NEPA process focuses on projects after they have been included in the MTP or
TIP. This process can be performed in conjunction or at the end of a MIS. There are three
classes of action that prescribe the level of documentation required in the NEPA process.
These actions relate to the type of transportation investments and their anticipated
impacts on the environment, and can be summarized as follows (20):
• Class I: Environmental Impact Statement: These are actions that significantly
affect the environment and require an Environmental Impact Statement (EIS).
• Class II: Categorical Exclusions: These are actions that do not have a significant
effect and are excluded from the requirements to prepare environmental
assessments.
16
• Class III: Environmental Assessment: These are actions in which the significance
of the environmental impact is not clearly established. An Environmental
Assessment (EA) needs to be prepared to determine the appropriate
environmental document required.
The Clean Air Act Amendments of 1990 set forth specific air quality goals to be
achieved by certain dates. Once an area reaches attainment, it is classified as a
maintenance area for twenty years past the attainment date and must still fulfill CAAA
requirements. The United States Environmental Protection Agency (EPA) is the Federal
agency charged with implementing the CAAA. The EPA established National Ambient
Air Quality Standards (NAAQS) in 1970, with the purpose of protecting human life. In
terms of the NAAQS the EPA has set national air quality standards for six principal
pollutants, namely CO, lead, NOx, ozone, particulate matter and SO2. The CAAA require
that the EPA review the NAAQS every five years to determine if the standards are still
adequate. The EPA relies on the states for preparing State Implementation Plans (SIPs) to
submit to the EPA, detailing how they intend to reduce vehicular emissions (49).
If the EPA classifies an area as non-attainment for air quality, those transportation
plans and programs must conform to air quality goals or Federal funding may be
withheld. The plans demonstrate conformity if the planís forecasted emission estimates
are less than or equal to that areaís on-road Motor Vehicle Emissions Budget (MVEB)
listed in the SIP. A MVEB is generally required for each transportation related pollutant
and/or pollutant precursor for which the area is in non-attainment. If a non-attainment
area cannot demonstrate conformity within the required timeframe, a transportation
conformity lapse occurs. During such a lapse, the EPA allows only certain transportation
projects to proceed. Conformity determination must be performed each time a SIP is
revised that adds, deletes, or changes emission budgets, or when Transportation Control
Measures (TCMs) are submitted to the EPA, detailing how those reductions will occur
(49).
ISTEA requires non-attainment area MTPs to be reviewed and updated at least every
three years, whereas TIPs in non-attainment areas must be updated at least every two
years. The CAAA state that conformity to a SIP means conformity to the planís purpose
17
of eliminating or reducing the severity and number of violations of the NAAQS. In
addition, the activities must not cause or contribute to a new violation, increase the
frequency or severity of an existing violation, or delay timely attainment of any standard
interim milestone (50).
The procedure of showing that the transportation plans, programs and projects are
conforming to air quality goals is a long and detailed process that requires many skilled
personnel and a sizable budget. It is, however, a very necessary process to assist in
achieving sustainable transportation. Failure to adhere to the CAAA and the
ISTEA/TEA-21 requirements can have serious consequences, both due to Federally
imposed sanctions in the form of funding that is withheld, as well as all the negative
effects related to poor air quality.
Policy Framework
As in the case of legislation, policies can play a pivotal role in achieving the goals of
sustainable transportation. The concept of policy may be defined as (51) ì a purposeful
course of action followed by an actor or set of actors in dealing with a problem or matter
of concern.î This definition of policy links it to a goal oriented action rather than to
random behavior or chance occurrences. Public policies are those developed by
governmental bodies, and they are designed to accomplish specified goals or produce
definite results (51). Because policies are linked to goals, they can be developed as part
of a strategic planning exercise. The transportation sector is, however, a particularly
difficult sector to address due to its high dependence on fossil fuels and the fact that
objectives associated with an effective and efficient transportation system are often not
compatible with environmental objectives (52). The development of appropriate policy
for transportation is, therefore, a complex issue.
The main focus of a policy for sustainable transportation should be to achieve long-
term sustainability based on the understanding that the world economy is a sub-system of
the global ecological system, which is restricted by its capacity (45). The World Bank
defines a policy for sustainable transportation as follows (39): ì it identifies and
implements the win-win policy instruments and explicitly confronts the tradeoffs so that
the balance is chosen rather than accidentally arrived at. It is a policy of informed,
18
conscious choices.î A number of authors have investigated policy options to support
sustainable transportation. The following are some of the most important policy
categories that can be used in the context of sustainable transportation, whereas Table 2.3
shows a detailed listing of the specific policies that can be utilized under each category
(17,28,43,53,54,55):
• Pricing policies: Transportation systems and services must be priced to result in
the optimal allocation of resources. This entails the inclusion of external social
costs into the pricing of transportation.
• Technology policies: Technology plays a vital role in providing transportation
options, making information available to users, and reducing environmental
damage.
• Non-motorized transportation policies: Among the different modes of
transportation, walking and cycling rank highest on the sustainability scale, and
the single-occupant automobile ranks the lowest. It is, therefore, necessary to have
policies in place that promote the utilization of non-motorized modes of
transportation.
• Regulatory or prohibitive policies: In some instances it is necessary to regulate
and prohibit certain actions.
• Traffic management policies: Traffic flow conditions can be improved through a
number of traffic management techniques, and improved traffic flow can assist in
making transportation more sustainable.
• Behavioral and educational policies: Users of the transportation system need to
change their transportation behavior in order to facilitate the achievement of a
more sustainable transportation system.
• Land use and transportation policies: Without adequate land use reforms and an
integrated land use and transportation approach, the goals of sustainable
transportation are not likely to be met.
19
TABLE 2.3 Policies to Assist in Achieving a Sustainable Transportation System
Pricing policies: Taxes Subsidies User fees Area licensing Parking Allowances Buyback programs Technology policies: ITS for transit and ridesharing ITS for car users Resource efficient vehicle technology Telecommuting and teleconferencing Non-motorized transportation policies: Investment in bicycle and walking Statewide promotion campaigns Regulatory or prohibitive policies: Emission and noise standards Speed limits Parking regulations Carrier regulations
Traffic management policies: Efficient signal timing Freeway ramp metering High occupancy vehicle lanes Route guidance Traffic calming measures Incident management Travel demand management Behavioral and educational policies: Promoting voluntary ì no driveî days Statewide promotion campaigns Training for the general public Education for children Land use and transportation policies: Improve access Improve mobility Employ mixed-use developments Limit sprawl Reconfiguring zoning ordinances Land use investment strategies Regulatory land use strategies Parking requirements
Table 2.3 shows that there are numerous policies that can support the goals of
sustainable transportation. Decision-makers will select the specific policies based on the
goals and objectives developed through a strategic planning exercise. Once appropriate
policies have been selected and implemented, they need to be monitored with the aid of
performance measures (6).
20
PERFORMANCE MEASURES The Role of Performance Measures
Performance measures or indicators are very important in the context of sustainable
development and sustainable transportation. Agenda 21 of the United Nations Conference
on Environment and Development considers the function of performance measures as
follows (56): ì indicators of sustainable development need to be developed to provide
solid bases for decision-making at all levels and to contribute to a self-regulating
sustainability of integrated environment and development systems.î Performance
measures are broadly used for simplification, quantification, and communication. They
are able to translate data and statistics into succinct information that can be readily
understood and used by several groups of people including scientists, administrators,
politicians, and the general public (57,58). A comprehensive performance measure would
include measurements of the condition, trends over time, and the share attributed to the
different agencies and/or actors (56).
ISTEA and TEA-21 recognize performance monitoring as a critical part of
transportation planning and have called for a more performance-based approach. This
requires that the performance of transportation systems must be quantitatively measured
for a variety of modes and criteria (22). Apart from the requirements of legislation,
performance measures can be very powerful planning and management tools. The
following are some of the most important uses of performance measures (31,59): provide
a broad perspective; assess facility or system performance; calibrate models; identify
problems; develop and assess improvements; formulate programs and priorities; educate
a wide range of interest groups; and set policies. Although performance measures have a
wide range of applications, there are instances where they should not be used, such as to
(59): isolate the effects of individual regulations; provide a full economic analysis; define
acceptable levels of impact; and set final priorities. Performance measures are, therefore,
able to provide the decision-maker with the quantitative information necessary to make
informed decisions.
21
Levels of Aggregation
Performance measures are quantified with information that is prepared from various
data sources. The quantified performance measures can be aggregated and weighted in
order to produce composite measures known as indices (56). Indices are often used to
measure trends and to track progress toward a goal. They have been developed for a
number of applications such as for infrastructure conditions and congestion. The
advantages of indices are as follows (60):
• Easy to use;
• Simple to interpret; and
• Ability to reduce information overloads that can often result from individual
performance measures.
The problems with indices, however, are as follows (44):
• Can mask information;
• Their robustness can be limited by different spatial and temporal scales; and
• It is not always clear how and by whom the indices were developed.
Very few authors have looked into indices for sustainable transportation. Litman
proposes a sustainable transportation index that is based on fourteen performance
measures that range from personal travel characteristics to transportation system
performance (29). Black proposes an index that is based on principal component analysis
and that uses the following measures (61):
• Dependence on petroleum fuels;
• Impact of emissions on local air quality and human health;
• Number of injuries and fatalities due to road accidents;
• The effect of congestion; and
• Availability of other modes.
22
The typical levels of aggregation of performance measures, as well as an example of
each, are shown in Table 2.4 (18,44,56,57,62). This table shows that the two highest
levels of aggregation, namely goals and objectives, are direct products from a strategic
planning exercise, whereas the two lowest levels, namely data and information, are the
products from operational management and data collection.
TABLE 2.4 Typical Levels of Aggregation of Performance Measurement
Level of Performance Measurement
Types of Performance Measures Required
Examples of Relevant Measures
Goal
Overall goal for sustainable transportation
To have a sustainable transportation system
Objectives
Social, environmental, and economic related objectives
To have a safe transportation system
Indices
Aggregated or integrated performance measures
Safety index
Performance Measures
Input, output, or outcome measures Fatalities per 100 million miles of travel
Information
Manipulated data Vehicle miles of travel and number of fatalities
Data Raw data Volume counts and accident records
Qualities of a Good Performance Measure
In order to make good decisions about aspects relating to sustainable transportation,
decision-makers need reliable information. Table 2.5 includes a summary of the attributes
of a good performance measure as proposed by a number of authors (17-
19,27,44,56,58,63). These attributes can be grouped into the following broad categories
as proposed by the OECD, namely relevance, utility, analytical soundness, and
measurability (44).
23
It should be noted that the fifteen attributes of a good performance measure as
suggested in Table 2.5 are in effect a wish-list for which the planner strives. It will be
very rare for a performance measure to possess most of the attributes listed in Table 2.5.
There are instances where certain attributes of a good performance measure are not
compatible and a particular performance measure will, therefore, not comply with both
such characteristics. As an example, it is very difficult for a performance measure to be
simple (understandable at the community level) and also able to address certain complex
multidimensional aspects. It is often necessary, therefore, to have a variety of indicators
for different applications.
24
TABLE 2.5 Attributes of a Good Performance Measure
Quality
Explanation
1. Able to discriminate Must be able to differentiate between the individual components that are affecting the performance of the system.
2. Able to integrate Must be able to integrate the sustainability aspects of environmental, social, and economic sustainability.
3. Acceptable The general community must assist in identifying and developing the performance measures.
4. Accurate Must be based on accurate information, of known quality and origin.
5. Affordable Must be based on readily available data or data that can be obtained at a reasonable cost.
6. Appropriate level of detail
Must be specified and used at the appropriate level of detail and level of aggregation for the questions it is intended to answer.
7. Have a target Must have a target level or benchmark against which to compare it.
8. Measurable The data must be available, and the tools need to exist to perform the required calculations.
9. Multidimensional Must be able to be used over time frames, at different geographic areas, with different scales of aggregation, and in the context of multimodal issues.
10. Not influenced Must not be influenced by exogenous factors that are difficult to control for, or that the planner is not even aware of.
11. Relevant Must be compatible with overall goals and objectives.
12. Sensitive Must detect a certain level of change that occurs in the transportation system.
13. Show trends Must be able to show trends over time and provide early warnings about problems and irreversible trends.
14. Timely Must be based on timely information that is capable of being updated at regular intervals.
15. Understandable Must be understandable and easy to interpret, even by the community at large.
New Trends in Performance Measures
Because sustainability requires a more integrated view of the world, traditional
performance measures that have a very narrow focus are often not very useful as
indicators for sustainability (29). The challenge is to assess the outcomes of
25
transportation programs and policies in terms of the broader goals of economic, social,
and environmental sustainability. Subordinate to this goal are the more operational
questions of how well the transportation system is performing in supporting these goals.
To date the focus of performance measures has been on the operational aspects of
transportation because transportation programs traditionally have focused on enhancing
the supply side of transportation (64). ISTEA and its requirements have demanded a
paradigm shift in terms of how performance measures are defined and used. A number of
authors have identified these paradigm shifts, and Figure 2.2 provides a
conceptualization.
Figure 2.2. Old and New Paradigms for Performance Measures.
Integration
Mobility
Accessibility
Demand
Outcome
Effectiveness
Product
Results
Disaggregate
Top-down
Old Paradigm
Zoning
Speed
Mobility
Supply
Output
Efficiency
Process
Activities
Aggregate
Bottom-up
New Paradigm
26
Possible Performance Measures
Table 2.6 shows the most common objectives and the related performance measures
that can be used as input to the proposed framework to achieve a more sustainable
transportation system (18,22,27,29,44,55,57,65-70).
TABLE 2.6 Objectives and Performance Measures for Sustainable
Number of travel objectives that can be reached within an acceptable travel time, ability of non-drivers to reach employment centers and services, land use mix, % employees within x miles of major services, highway system supply, transit supply, and time devoted to non-recreational travel.
2. Maximize comfort and convenience
Walking distance to transit services, trip distance, comfort and convenience, and frequency of service.
3. Maximize economic benefit
Jobs added, value added to goods produced, wages added to job payrolls, tax revenues, net present worth, and change in growth domestic product (GDP).
4. Maximize equity
Point-to-point travel cost, point-to-point travel time, population within walking distance to transit, percentage of disadvantaged travelers with alternatives, affordability of public transit, percentage of income devoted to transportation, percentage of day devoted to commuting, and percentage of residents participating in land use and transportation decision-making.
5. Maximize livability
Average vehicle speed, mode split, per capita land area paved for roads and parking, and number of major services within walking distance of residents.
6. Maximize mobility
Mobility index, total delay, delays per person, person throughput, volume/capacity ratio, travel time, travel rate, link capacity, and link usage.
7. Maximize pedestrian and bicycle usage
Mode split, bicycle counts, pedestrian counts, and quality of pedestrian and bicycle environment.
8. Maximize productivity
Passengers per vehicle revenue, vehicle hours, and operating cost per passenger trip.
9. Maximize reliability
Variance of point-to-point travel time, reliability of service, schedule adherence, and freeway incident delay.
10. Maximize safety
Accident rate, accident fatality rate, freeway incident rates, total value of damages as a result of accidents, traffic violations, average response time for emergency services, tons of hazardous materials spilled due to accidents, percent of vehicles exceeding speed limit, percent of motorists driving under influence, and percent of motorists using seat belts.
27
TABLE 2.6 Objectives and Performance Measures for Sustainable
Transportation (Continued) Objective Performance Measures 11. Maximize security Incidents of crime, transportation security related losses, and crime rate. 12. Maximize transit usage
Mode split, passenger-miles of travel, number of transit passengers, quality of service, and portion of residents within walking distance of service.
13. Minimize air pollution
Concentration of HC, NOx, and CO emissions, percentage of population exposed to threshold levels, tons of HC, NOx, and CO vehicular emissions, and emission rates.
14. Minimize auto usage
Vehicle-miles of travel, vehicle occupancy, mode split, traffic volume, annual miles of automobile travel per capita, person miles of travel, vehicle miles of travel, and telecommuting.
15. Minimize capital costs
Capital cost, right of way cost, and mitigation cost.
16. Minimize congestion
Travel rate, delay rate, total delay, average speed, mobility index, hours of congestion, LOS, volume/capacity ratio, duration of heavy congestion, vehicles per lane mile, and percentage of corridor congested.
17. Minimize displacement
Acres of land acquired, and structures displaced.
18. Minimize ecosystem impacts
Area of wetlands taken, area of agricultural land taken, area of forest land taken, area of habitat taken, ecological footprint, and pollutant run-off.
19. Minimize energy consumption
Per capita transportation energy consumption, energy consumption per time period, technological innovations, gasoline and diesel sales, vehicle miles traveled per gallon of fuel, and vehicle occupancy.
20. Minimize noise impacts
Noise levels, percentage of population exposed to threshold levels, and noise standards for new vehicles.
21. Minimize operating costs
Operating cost, maintenance cost, cost of accidents, costs associated with pollution, operating deficits, and operating revenue.
22. Minimize travel cost
Point-to-point out of pocket travel cost, point-to-point transit fares, and parking cost.
23. Minimize travel time
Point-to-point travel time, person-hours of travel, vehicle hours of travel, delay, per capita automobile use, and number of stops.
28
MODELING TECHNIQUES Once the appropriate performance measures have been identified, modeling
techniques are often used to quantify such measures over space and time (17). Models for
quantifying sustainable transportation include transportation models, transportation
environmental impact models, and economic models. In addition to output from the
various models, data collected through Intelligent Transportation Systems (ITS)
applications can be used to quantify performance measures for sustainable transportation.
The following is a discussion of aggregate and disaggregate approaches to modeling, as
well as the appropriate data collection and modeling techniques for quantifying
performance measures for sustainable transportation.
Aggregate versus Disaggregate Approaches
The travel behavior of large groups is the manifestation of the travel choices of
numerous individual travelers. Disaggregate travel models are constructed by using data
at the level of the individual traveler, whereas with aggregate models the individuals are
placed into groups with common characteristics. The data that are based on the group are
an aggregate representation of the real underlying distribution of the individual data (71).
The axioms of disaggregate behavioral modeling are that individuals represent the basic
decision-making unit and that individuals will choose one alternative among those
available that they find most desirable or useful. The choice depends on the attributes of
the alternative and the socioeconomic characteristics of the individual (72).
The probability of a decision is considered to be a function of the utility for that
decision (73). For modeling purposes the utility is composed of two components, namely
the observed attributes referred to as the representative utility, and an unobserved
component known as the random utility. Random utility is based on the assumption that
although the individualís choice is rational, an observer cannot accurately predict a given
individualís choice because of the influence of unobserved determinants of choice as
reflected in the random component (74).
The most common way of representing the aggregate data is by a measure of central
tendency such as the mean. Every aggregate representation of the underlying detailed
individual data, however, results in a loss of information. If the underlying disaggregate
29
model is linear over the range of interest, the aggregate forecasting model will have the
same linear specifications. In this case, the averages for the variable can be substituted for
the individual values. However, if the disaggregate model is nonlinear, the disaggregate
functional specification, in which averages will be substituted for individual values, will
give a biased forecast of the average of the dependent variable (75). The average of the
function, therefore, is not equal to the function of the averages, and this bias is widely
known as aggregation bias (73). Clustering the market into groups or segments of
homogeneous characteristics can limit the problem of aggregation bias (74).
The forecasting approach, which employs group means as independent variables, is
known as the ì naÔve methodî or the ì direct methodî of aggregation. The most reliable
method of making predictions with disaggregate models is to use the values for each
individual in the forecasting model as independent variables and make the prediction over
all individuals. This method has been referred to as the ì enumeration method.î The
disaggregate method of analysis indicates that changes in the travel choice environment
affect different market segments in substantially different ways.
A number of crucial sustainability issues, therefore, can be overlooked by only
considering averages or aggregate information. Disaggregate information on the other
hand has the potential of considerably improving the accuracy by which certain
sustainable transportation performance measures are quantified. Various authors have
over a period of more than thirty years studied the application of disaggregate travel
demand models as compared to aggregate travel demand models. They found that apart
from the additional detail, a number of other benefits can be obtained through a
disaggregate approach.
The following are some of the benefits identified, and it is postulated that these
benefits can also be of relevance for disaggregate traffic supply models, which are the
focus of this research (24,71-74,76): calibrating a model on a disaggregate level will
reduce aggregation bias; there is potential to reduce the data requirements, and therefore
the cost, if models are calibrated on individual data; disaggregate models are more
sensitive to changes in individual behavior and changes in policy; recommendations
based on disaggregate models have more credibility, because the results are based on the
effects of individuals, which are considered to be more intuitive; disaggregate models can
30
be applied at any level of aggregation; and there is an improved possibility for
transferring modeling results from disaggregate models from one geographic area to
another.
There are very few examples where aspects related to sustainable transportation are
calculated on a disaggregate level. One application showed better estimates of air
pollution and energy consumption using acceleration noise instead of average speed (77).
In another study it was shown that relatively short segment lengths are needed to detect
localized traffic effects. This study found that traffic disturbances become visible only
when segment lengths are at most half the length of the associated disturbance (78).
Intelligent Transportation System (ITS) Data
Intelligent Transportation Systems are becoming very prevalent in a variety of
Standard deviation of travel time, or coefficient of variation of travel time Total delay, travel rate, travel time, % corridor congested, and LOS Net present worth
• It was illustrated that forty percent of the regular commuterís travel times are
statistically different to the state of the practice aggregate estimates. This result
has significant implications for sustainability analysis and ATIS applications.
136
• In the case of travel time variability it was found that in approximately twenty
percent of the cases the travel time standard deviations of the regular commuters
are statistically different to the state of the practice aggregate estimates.
• On a link basis it was found that travel times between links are almost exclusively
positively correlated with only five percent of the observations indicating negative
correlations. The results from analyzing link travel times of individual commuters
on a trip-by-trip basis, however, revealed that individual commuters show great
variability in travel behavior as evidenced by the fact that large tendencies to
having both faster and slower link travel times than the aggregate estimates were
observed.
• A number of different types of disaggregation can be considered, namely spatial,
temporal, combined spatial and temporal, and the individual level. The individual
level can be used as a separate level of disaggregation or in combination with
spatial and/or temporal disaggregation. Spatial and temporal disaggregation can
each be applied at different levels of detail and can be defined by the segment
lengths and time interval lengths, respectively.
• The TRANSIMS simulation model was used to quantify a wide range of mobility
related performance measures at various levels of spatial and temporal
disaggregation. The uniform kernel estimator was used to smooth the erratic
individual speed profiles produced by the TRANSIMS micro-simulator.
• The smoothed speed profiles from the TRANSIMS micro-simulator were used
with the MOBILE 5a model to produce vehicular emissions of individual vehicles
on a wide range of levels of aggregation. It was shown that the effect of
aggregation bias can be as much as twenty percent in some instances.
• Applications such as loop detectors, AVI technology, and AVL technology could
be simulated with TRANSIMS output. It was found that data from loop detectors
resulted in estimates that are considerably lower than that with AVI and AVL
technology. It is hypothesized that the reason for this is that a large percentage of
high emitting low speeds are not accounted for with the conventional aggregate
approach.
137
• A comparison was made between results with smoothed and unsmoothed speed
profiles. It was found that both profiles produce very similar results with
differences of less than eleven percent for all pollutant types.
• With regard to noise pollution it was found that a twenty-two percent
improvement could be achieved by using the shorter AVI links instead of the
totally aggregate corridor level, as compared with the approach that uses
interchange links. The actual values of the deviations from the approach that uses
interchange links, however, were fairly small, with values of only 3.9 percent if
the whole corridor is used and 2.9 percent if AVI links are used.
• In the case of fuel consumption the effect of spatial aggregation can be fairly
large. Average deviations from the totally disaggregate case (calculated for each
vehicle on a second-by-second basis) of between six and sixteen percent were
observed. It was found that incorporating the effect of the individual vehicles
provides slightly better results.
• It was illustrated that project selection could be markedly different if a broad
range of sustainable transportation principles is considered instead of pure
economic benefit. The level of aggregation of the input data also made a
noticeable difference in the final project selection. The effect of analyzing the
data at the level of AVI links as opposed to the whole corridor allowed for more
flexibility in the allocation of weights, as well as greater accuracy in the results.
• It was shown that the concept of equivalency factors can be used to assign
responsibility to motorists for the effects of negative externalities. Equivalency
factors could be used to allocate costs on both an aggregate and a disaggregate
level. Such applications can be used as a basis for further research into strategies
such as congestion pricing to assist in achieving the sustainability goals of social
equity and economic development.
FUTURE RESEARCH
This research area that deals with the quantification of performance measures for
sustainable transportation is still relatively uncultivated. Numerous opportunities for
further research exist due to new modeling and data collection technologies that make it
138
possible to quantify performance measures for sustainable transportation at the
disaggregate level. The following needs for future research have been identified:
• For this research the steps of the proposed framework were applied to freeway
corridors. The techniques can, however, be applied to a broad range of
transportation infrastructure. They can also be applied to geographic areas that are
larger than corridors, such as cities and regions. A broader range of applications
can be used to confirm the findings of this research and to obtain results for
different scenarios.
• For this research, AVI data and simulated data were used for quantifying the
performance measures. There are various other types of technologies available
that can also provide travel data at the disaggregate level. These technologies
include global positioning systems, satellite surveillance, AVL technology,
cellular phone technology, and various types of vehicle mounted transponders.
The implications and benefits of using such technologies for quantifying
performance measures for sustainable transportation should, therefore, be tested.
• The current state of the practice in calculating vehicular emission is based on
aggregate data sets that are used in emission models. Current simulation models
make it possible to produce disaggregate data sets for use in emission models. The
effect of aggregation bias, therefore, can be determined for applications in major
metropolitan areas. This is important because such areas are currently classified in
terms of attainment based on approaches that are prone to aggregation bias.
• It was illustrated how equivalency factors and disaggregate data sets can be used
to determine the costs imposed by vehicles as a result of vehicular emission.
These techniques need to be expanded by using more comprehensive data sets and
should also be expanded to a broader range of externalities. Such applications can
then form the basis for further research into congestion pricing strategies.
• Very little work has been done on developing an index for sustainable
transportation. There is a need to augment existing work to develop an index or
indices that would make it possible to compare the degree of sustainability
between projects, communities, cities, and even countries.
139
• It was shown in this research that the travel time and the travel time variability of
regular commuters were mostly lower than that of the aggregate estimates. These
findings can have important implications in quantifying sustainable transportation
performance measures for such commuters. A detailed study needs to be
conducted by comparing the travel characteristics of regular commuters versus
that of the general travelers.
• It was found in this research that link travel times on an aggregate level are
mostly positively correlated, whereas link travel times for individual commuters
on a trip-by-trip basis were found to be mostly negatively correlated. This slow-
fast travel pattern of individual commuters needs to be studied further, because it
has implications for modeling various effects such as emissions, noise, and fuel
consumption.
140
141
CHAPTER 9: REFERENCES
1. Environmental Externalities and Social Costs of Transportation Systems: Measurement, Mitigation and Costing: An Annotated Bibliography. Federal Railroad Administration, U.S. Department of Transportation, Office of Policy, Washington D.C., August 1993.
2. Haq, G. Towards Sustainable Transportation Planning: A Comparison Between Britain and the Netherlands. Avebury, Aldershot, England, 1997.
3. Nationwide Personal Transportation Survey (NPTS). Federal Highway Administration, U.S. Department of Transportation, Office of Information, Washington D.C., 1995.
4. World Commission on Environment and Development (Brundtland Commission). Our Common Future. Oxford University Press, Oxford, England, 1987.
5. Presidentís Council on Sustainable Development. http://www.whitehouse.gov/PCSD/CSD. Accessed June 1999.
6. Spaethling, D. Sustainable Transportation: The American Experience. Proceedings of Seminar C: Planning for Sustainability of the 24th European Transport Forum, PTRC Education and Research Services Ltd., London, England, September 1996.
7. Litman, T. Issues in Sustainable Transportation. Victoria Transportation Policy Institute, Victoria, Canada, December 1999.
8. Lindquist, E. Moving Toward Sustainability: Transforming a Comprehensive Land Use and Transportation Plan. In Transportation Research Record 1617, Transportation Research Board, National Research Council, Washington D.C., 1998, pp. 1-9.
9. Masser, I., O. Sviden, and M. Wegener, Transport Planning for Equity and Sustainability. Transportation Planning and Technology, Vol. 17, Gordon and Breach Science Publishers, Loughborough, England, 1993, pp. 319-330.
10. Peake, S., and C. Hope. Sustainable Mobility in Context: Three Transport Scenarios for the UK. Transport Policy, Vol. 3, Butterworth-Heinemann Ltd, Oxford, England, 1994, pp. 195-207.
11. Reid, D. Sustainable Development: An Introductory Guide. Earthscan Publications Ltd., London, England, 1995.
12. Dittmar, H. Thinking Like a System: Sustainability Through Transportation Technology. ITS Quarterly, Vol. 4, No. 1, Washington D.C., Winter 1996, pp. 19-26.
13. Akinyema, E. O. A Total Performance Approach to Traffic Management for Sustainable Development of Communities in Third World Cities. Proceedings of the National Conference held in Tampa Florida on Transportation and Sustainable Communities, Institute of Transportation Engineers, Washington D.C., 1997.
142
14. Mickelson, R. P. Synthesis of Highway Practice 267: Transportation Development Process. National Cooperative Highway Research Program, Transportation Research Board, National Research Council, Washington D.C., 1998.
15. Gatersleben, B. Sustainable Transportation and Quality of Life. Proceedings of the Conference on Social Change and Sustainable Transportation (SCAST), held at the University of California at Berkeley, March 1999.
16. Transportation and Sustainable Communities Initiative: Overview of Federal Sustainable Transportation Activities. National Science and Technology Council, Subcommittee on Transportation Research and Development, Washington D.C., June 1998.
17. Sustainability Indicators: The Transportation Sector. Ontario Roundtable on Environment and Economy, IndEco Report 94029, Toronto, Canada, September 1995.
18. National Transportation System Performance Measures. Report Prepared for the Office of the Secretary of the U.S. Department of Transportation, Washington D.C., April 1996.
19. Gardner, K., and J. Carlsen. Performance Indicators for Sustainable Transport in London. Proceedings of Seminar C: Planning for Sustainability, PTRC European Transport Forum, Brunel University, England, September 1996.
20. A Guide to Metropolitan Transportation Planning Under ISTEA ñ How the Pieces Fit Together. Federal Transit Administration, U.S. Department of Transportation, Washington D.C., 1997.
21. TEA-21: Moving Americans into the 21st Century, U.S. Department of Transportation. http://www.fhwa.dot.gov/tea21/index.htm. Accessed January 1999.
22. Codd, N., and C. M. Walton. Performance Measures and Framework for Decision Making Under the National Transportation System. In Transportation Research Record 1518, Transportation Research Board, National Research Council, Washington D. C., 1996, pp.70-77.
23. Zietsman, J. and L.R. Rilett. Analysis of Aggregation Bias in Vehicular Emission Estimation Using TRANSIMS Output. In Transportation Research Record 1750, Transportation Research Board, National Research Council, Washington D.C., 2001, pp 56-63.
24. Ortuzar, J de D., and L. G. Willumsen. Modelling Transport: Second Edition. John Wiley and Sons, New York, December 1995.
25. Smith, L., R. Beckman, D. Anson, K. Nagel, and M.E. Williams. TRANSIMS: Transportation Analysis and Simulation System. Proceedings of the Fifth National Conference on Transportation Planning Methods and Applications held in Seattle, April 1995.
143
26. Alexander, E. R., E. A. Beimborn, C.V. Patton, and L. P. Witzling. Multi-Objective Decision Making for Transportation. Bureau of Policy & Planning, Wisconsin Department of Transportation, Milwaukee, Wisconsin, November 1985.
27. Meyer, M. D., and E. J. Miller. Urban Transportation Planning: A Decision-Oriented Approach. McGraw-Hill, New York, 1984.
28. Nijkamp, P. Roads Towards Environmentally Sustainable Transport. Transportation Research Part A. Vol. 28A, No. 4, Elsevier Science Ltd., Oxford, England, 1994, pp. 261-271.
29. Litman, T. Sustainable Transportation Indicators. Victoria Transportation Policy Institute, Victoria, Canada, July 1997.
30. Ewing, R. Beyond Speed: The Next Generation of Transportation Service Standards. Annual meeting of the Institute of Transportation Engineers, Compendium of Technical Papers, Washington D.C., 1992.
31. Pratt, R. H., and Lomax T. J. Performance Measures for Multimodal Transportation Systems. In Transportation Research Record 1518, Transportation Research Board, National Research Council, Washington D. C., 1996, pp. 85-93.
32. The ì Sî Factor: Increasingly, Companies are Viewing Sustainability as a Business Issue. The Hands-on Journal for Environmentally Conscious Companies, Washington D.C., June 1998, pp. 1-8.
33. Mckee, M., T. W. Morgan, R. Narayanan, and A. B. Bishop. A Methodology for Public-Planner Interaction in Multiobjective Project Planning and Evaluation. Water Resources Planning Series, Utah State University, Logan, Utah, December 1981.
34. Toward a Sustainable Future: Addressing the Long-Term Effects of Motor Vehicle Transportation on Climate and Ecology. Special Report 251, Transportation Research Board, National Research Council, Washington D.C., 1997.
35. Towards Sustainable Transportation. Proceedings of OECD Conference, Vancouver, Canada, March 1996.
36. Livable Communities. http://www.house.gov/blumenauer/livable.htm. Accessed November 1999.
37. Ruckelshaus, W. D. Toward a Sustainable World. Scientific American, No. 261, New York, 1989, pp.166-170.
38. Pearce, D. Blueprint 3: Measuring Sustainable Development. Earthscan, London England, 1993.
39. Sustainable Transportation: Priorities for Policy Reform. The World Bank, Washington D.C., 1996.
144
40. Towards a Sustainable Future. Special Report 251, Transportation Research Board, National Research Council, Washington D.C., 1997.
41. European Foundation for the Improvement of Living and Working Conditions. http://susdev.eurofound.ie. Accessed January 2000.
42. Moving on Sustainable Transportation (MOST). http://www.tc.gc.ca/envaffairs/most. Accessed January 2000.
43. Sperling, D., and S. A. Shaheen. Transportation and Energy: Strategies for a Sustainable Transportation System. Institute of Transportation Studies, University of California, Berkeley, 1995.
44. Houghton, N. Ecologically Sustainable Development: Indicators and Decision Process. Report 319, ARRB Transportation Research, Vermont South, Australia, March 1998.
45. Hohmeyer, O. Social Costs of Climate Change Strong Sustainability and Social Costs. Proceedings of a Conference Held in Ladenburg, Springer, Berlin, Germany, May 1995.
46. Pocket Guide to Transportation. Bureau of Transportation Statistics, U.S. Department of Transportation, Washington D.C., December 1998.
47. Schiller, P., and B. de Lille. Green Streets: The 1991 Intermodal Surface Transportation Efficiency Act and the Greening of Transportation Policy in the United States. Surface Transportation Policy Project, Washington D.C., 1997.
48. McDowell, B. D. Reinventing Planning Under ISTEA: MPOís and State DOTís. Transportation Research Board, National Research Council, Washington D.C., November 1994.
49. Air Quality Demands on Transportation Planning in Texas. A White Paper Prepared by the Technical Working Group for Mobile Source Emissions, Austin, Texas, August 1998.
50. Bass, P., D. Perkinson, B. Keitgen, and G. Dresser. The Context of Analysis (ISTEA and the Clean Air Act). Report 1235-14, Texas Transportation Institute, College Station, Texas, 1994.
51. Anderson, J. E. Public Policymaking: An Introduction: Second Edition. Houghton Mifflin Company, Boston, 1994.
52. Mason, D. Subsidiarity Wins Over Sustainability Every Time. Surveyor, Vol. 181, No. 5280, Hemming Group Ltd, London, England, February 1994, p. 12.
53. Richardson, E., D. Rice, and C. Jelly. Urban Transport for a Vital and Sustainable Future. Road and Transport Research, Vol. 2, No. 2, Victoria, Australia, June 1993, pp. 58-68.
54. Deakin, E. Policy Responses in the USA. Transport, the Environment, and Sustainable Development. Contained in Transport, the Environment, and
145
Sustainable Development. Edited by Banister, D., and K. Button, E & FN Spon, London, England, 1993.
55. Ewing, R., Measuring Transportation Performance. Transportation Quarterly. Vol. 49, No. 1, Washington D.C., Winter 1995, pp. 91-104.
56. Better Understanding our Cities: The Role of Urban Indicators. Organization for Economic Co-operation and Development, Paris, France, 1997.
57. Poister, T. H. Performance Measures in State Departments of Transportation. National Cooperative Highway Research Program, Synthesis of Highway Practice, No. 238, Transportation Research Board, National Research Council, Washington D. C., 1997.
58. Hart, M. Hart Environmental Data: Indicators of Sustainability. http://www.subjectmatters.com/indicators/index.html. Accessed December 1998.
59. Indicators of the Environmental Impacts of Transportation: Highway, Rail, Aviation, and Maritime Transport. Report 230-R-96-009, Environmental Protection Agency, Washington D.C., October 1996.
60. Lomax, T. J., S. M. Turner, and G. Shunk. Quantifying Congestion: Final Report and Userís Guide. Report 398, National Cooperative Highway Research Program, Washington D.C., 1997.
61. Black, W. R. Toward a Measure of Transport Sustainability. Proceedings of the 79th Annual Meeting of the Transportation Research Board, National Research Council, Washington D.C., January 2000.
62. Stuart, D. G. Goal-Setting and Performance Measurement in Transportation Planning and Programming. Journal of Transportation, Vol. 1, No. 2, Tampa, Florida, Winter 1997, pp. 49-72.
63. Rennings, K. Economic and Ecological Concepts of Sustainable Development: External Costs and Sustainability Indicators. Proceedings of a Conference Held in Ladenburg, Springer, Berlin, Germany, May 1995.
64. National Transportation System Performance Measures. Report prepared for the Office of the Secretary of the U.S. Department of Transportation, Washington D.C., April 1996.
65. Lomax, T. J., and D. L. Schrank. The Keys to Estimating Mobility in Urban Areas, Applying Definitions and Measures that Everyone Understands. Texas Transportation Institute, The Texas A&M University System, College Station, Texas, January 1999.
66. Abrams, C. M., and J. F. DiRenzo. Measures of Effectiveness for Multimodal Urban Traffic Management, Volume 2: Development and Evaluation of TSM Strategies. Report FHEA-RD-79-113 Federal Highway Administration, U.S. Department of Transportation, Washington D.C., 1979.
67. Turner, S. M., M. E. Best, and D. L. Schrank, Measures of Effectiveness for
146
Major Investment Studies. Report SWUTC/96/467106-1, Southwest Region University Transportation Center, Texas A&M University System, College Station, Texas, 1996.
68. Guyton, J. W. Presentation of Comparative Data for Transportation Planning Studies. In Transportation Research Record 1617, Transportation Research Board, National Research Council, Washington D.C., 1998, pp. 44-49.
69. McLeod, D. S. Special Features of Floridaís Mobility Management Process. In Transportation Research Record 1552, Transportation Research Board, National Research Council, Washington D.C., 1996, pp. 42-47.
70. Florida Recommended Mobility Performance Measures. Memorandum prepared by the Florida Department of Transportation, Tallahassee, Florida, July 1998.
71. Landau, U. Aggregate Prediction with Disaggregate Models: Behavior of the Aggregation Bias. In Transportation Research Record 673, Transportation Research Board, National Research Council, Washington D. C., 1974, pp. 100-105.
72. McFadden, D., and F. Reid. Aggregate Travel Demand Forecasting from Disaggregate Behavioral Models. In Transportation Research Record 534, Transportation Research Board, National Research Council, Washington D. C., 1975, pp. 24-37.
73. Koppelman, F. S. Prediction with Disaggregate Models: The Aggregation Issue. In Transportation Research Record 527, Transportation Research Board, National Research Council, Washington D. C., 1974, pp. 73-80.
74. Tye, W. B., L. Sherman, M. Kinnucan, D. Nelson, and T. Tardiff. Applications of Disaggregate Travel Demand Models. Report 253, National Cooperative Highway Research Program, Transportation Research Board, National Research Council, Washington D. C., 1982.
75. Benjamin J. R., and C. A. Cornell. Probability, Statistics, and Decision for Civil Engineers. McGraw-Hill, New York, 1970.
76. Reid, F. A. Minimizing Error in Aggregate Predictions from Disaggregate Models. In Transportation Research Record 673, Transportation Research Board, National Research Council, Washington D.C., 1974, pp. 59-64.
77. Eisele, W. L., S. M. Turner, and R. J. Benz. Using Acceleration Characteristics in Air Quality and Energy Consumption Analysis. Report SWUTC/96/465100-1, Southwest Region University Transportation Center, Texas A&M University System, College Station, Texas, August 1996.
78. Quiroga, C. A., and D. Bullock. Travel Time Studies With Global Positioning and Geographic Information Systems: An Integrated Methodology. Transportation Research Part C, Vol. 6, Elsevier Science Ltd, Exeter, England, 1998, pp. 101-127.
79. Bush, B. W. The TRANSIMS Framework. Report LA-UR-99-7, Los Alamos
147
National Laboratory, Los Alamos, New Mexico, 1999.
80. Rilett, L. R. Transportation Planning and the TRANSIMS Micro-simulation Model: Prepared for the Transition, Preprint, 80th Annual Meeting of the Transportation Research Board, Washington D. C., 2000.
81. Nagel, K., P. Stretz, M. Pieck, S. Lecky, R. Donnelly, and C. L. Barrett. TRANSIMS Traffic Flow Characteristics. Report LA-UR-97-3530, Los Alamos Nation Laboratory, Los Alamos, New Mexico, July 1997.
82. United States Environmental Protection Agency (EPA). http://www.epa.gov/. Accessed January 1999.
83. Crawford, J. A., C. Jordan, and G. B. Dresser. Modal Emissions Modeling with Real Traffic Data. Report 1358-3F, Texas Transportation Institute, The Texas A&M University System, College Station, Texas, January 1999.
84. Jordan, D. C. Measures of Responsibility for Pollution and their Application in Road Pricing. Master of Science Thesis, University of Alberta, Canada, 1996.
85. New Highway Traffic Noise Prediction Model Almost Complete, Federal Highway Administration, U.S. Department of Transportation. http://www.tfrc.gov/trnsptr/rttmar96/rd960065.htm. Accessed January 1999.
86. McTrans: Center for Microcomputers in Transportation. http://www.mctrans.ce.ufl.edu/. Accessed April 2000.
87. Highway Capacity Manual. Special Report 290, Transportation Research Board, National Research Council, Washington D.C., 1997.
88. Dixon, M. P. Incorporation of Automatic Vehicle Identification Data Into the Synthetic OD Estimation Process. Ph.D. Dissertation, Texas A&M University, College Station, Texas, May 2000.
89. Fu, L., and L. R. Rilett. Expected Shortest Paths in Dynamic and Stochastic Traffic Networks. Transportation Research Part B, Vol. 32, No. 7, 1998, Elsevier Science Ltd., Exeter, England, 1998, pp. 449-516.
90. Rilett, L. R., K. Kim, and B. Raney. A Comparison of the Low Fidelity TRANSIMS and High Fidelity CORSIM Highway Simulation Models Using ITS Data. Preprint 00-0678, Transportation Research Board 79th Annual Meeting, Washington D. C., January, 2000.
91. Cleveland, W.D., and S.J. Devlin. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting. Journal of the American Statistical Association, Vol. 83, No. 403, New York, September 1988.
92. Perkinson, D. G., C. Bell, and G. B. Dresser. Development of Gridded Mobile Source Emissions Estimates for the Houston-Galveston Nonattainment Counties FY 2007 in Support of the COAST Project. Report 402001, Texas Transportation Institute, The Texas A&M University System, College Station, Texas, December 1998.
148
93. Jordan, D. C. Measures of Responsibility for Pollution and their Application in Road Pricing. Master of Science Thesis, University of Alberta, Canada, 1996.
94. Jordan, D. C., J. Zietsman, and L. R. Rilett. Development of Sustainable Transportation Metrics for Vehicular Pollutants. Transportation Planning and Technology, Volume 24, No. 3. Leicestershire, England, 2001, pp 86-94.
95. Turner, S.M., B. J. Gajewski, and G. B. Dresser. Sensitivity of Analysis Time Intervals for Mobile Source Emissions Estimation: Using Archived ITS Data to Determine Effects on VKT-Speed Distributions and Emissions Estimates. Preprint, Transportation Research Board 80th Annual Meeting, Washington D. C., January, 2001.
96. Zietsman, J., and L. R. Rilett. Using TRANSIMS and ITS Data to Quantify Aspects of Sustainable Transportation. Proceedings of the 9th World Conference on Transportation Research, Seoul, Korea, July 2001.
97. Guidelines for Analysis and Abatement of Highway Traffic Noise. Texas Department of Transportation, Environmental Affairs Division, Austin, Texas, June 1996.
98. Rao, K. S., and R. A. Krammes. Energy-Based Fuel Consumption Model for FREFLO. In Transportation Research Record 1444, Transportation Research Board, National Research Council, Washington D. C., 1994, pp. 36-43.
99. Olson, D. L. Decision Aids for Selection Problems. Springer, New York, 1996.
100. Rilett, L. R. Allocating Pollution Costs Using Noise Equivalency Factors. In Transportation Research Record 1498, Transportation Research Board, National Research Council, Washington D. C., 1996, pp. 102-107.
101. Rilett, L. R., B. G. Hutchinson, and R .C. G. Haas. Cost Allocation Implications of Flexible Pavement Deterioration Models. In Transportation Research Record 1215, Transportation Research Board, National Research Council, Washington D.C., 1988, pp. 31-42.
102. AASHTO Road Test: Report 5- Pavement Research. Special Report 615. American Association of State Highway Transportation Officials (AASHTO), Highway Research Board, Washington D.C., 1962.
103. Irwin, N. Transportation Pricing Strategies to Recover Environmental Costs. Annual Conference on Building Environmental Externalities into Full-cost Accounting, Transportation Association of Canada, Prince Edward Island, Canada, October 1996.
104. Litman, T. Transportation Cost Analysis for Sustainability. Victoria Transport Policy Institute, Victoria, Canada, July 1996.
105. Whitelegg, J. Transportation for a Sustainable Future: The Case for Europe. Belhave Press, London, England, 1993.
106. Zegras, C., and T. A. Litman. Cost Estimates of Transportation Air Pollution in
149
Santiago, Chile. In Transportation Research Record 1587, Transportation Research Board, National Research Council, Washington D.C., 1997, pp. 106-112.
107. Tellis, R. and C. J. Khisty. Congestion Pricing: The Actual Cost to Drive an Automobile on Urban Highways. Proceedings of the 1995 ASCE Conference, American Society of Civil Engineers, Reston, Virginia, 1995, pp. 946-957.
108. Irwin, N. Transportation Pricing Strategies to Recover Environmental Costs. Annual Conference on Building Environmental Externalities into Full-cost Accounting, Transportation Association of Canada, Prince Edward Island, Canada, October 1996.
109. Delucchi, M. A. Total Cost of Motor-Vehicle Use. Access No. 8, University of California Transportation Center, Berkeley, California, Spring 1996.