ASSESSING INVESTMENT ANALYSIS STRATEGIES FOR INFRASTRUCTURE
RENEWAL IN REGIONAL TRANSPORTATION PLANNING
by
Yukun Dong
A dissertation submitted to the Faculty of the University of Delaware in
partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil
Engineering
Spring 2008
Copyright 2008 Yukun Dong
All Rights Reserved
ASSESSING INVESTMENT ANALYSIS STRATEGIES FOR INFRASTRUCTURE
RENEWAL IN REGIONAL TRANSPORTATION PLANNING
by
Yukun Dong
Approved:
__________________________________________________________
Sue McNeil, Ph.D.
Professor in Charge of dissertation on behalf of the Advisory Committee
Approved:
__________________________________________________________
Harry Shenton III
Chair of the Department of Civil and Environmental Engineering
Approved:
__________________________________________________________
Michael J. Chajes, Ph.D.
Dean of the College of Engineering
Approved:
__________________________________________________________
Carolyn Thoroughgood, Ph.D.
Vice Provost for Research and Graduate Study
I certify that I have read this dissertation and that in my opinion it meets the
academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.
Signed:
__________________________________________________________
Sue McNeil, Ph.D.
Professor in charge of dissertation
I certify that I have read this dissertation and that in my opinion it meets the
academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.
Signed:
__________________________________________________________
David L. Ames, Ph.D.
Member of dissertation committee
I certify that I have read this dissertation and that in my opinion it meets the
academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.
Signed:
__________________________________________________________
Nii O. Attoh-Okine, Ph.D.
Member of dissertation committee
I certify that I have read this dissertation and that in my opinion it meets the
academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.
Signed:
__________________________________________________________
James J. Corbett, Ph.D.
Member of dissertation committee
ACKNOWLEDGMENTS
I wish to thank all of my dissertation committee members, David L. Ames,
Ph.D., Nii O. Attoh-Okine, Ph.D., James J. Corbett, Ph.D., and especially my academic
advisor Sue McNeil, Ph.D., for her continuous advice, guidance, and academic support
during the past several years. I also wish to thank Robert Mooney from U.S. Department
of Transportation for his continuous data and information supports, and to all my friends
and colleagues, who have supported and helped throughout my graduate education.
i
TABLE OF CONTENTS
LIST OF TABLES ........................................................................................................................iv
LIST OF FIGURES ....................................................................................................................... v
GLOSSARY AND ACRONYMS ................................................................................................vi
ABSTRACT ……………………………………………………………………………...............x
EXECUTIVE SUMMARY .........................................................................................................xii
CHAPTER 1 ................................................................................................................................... 1
INTRODUCTION ......................................................................................................................... 1
1.1
Problem Statement ..................................................................................................... 1
1.2
Motivation .................................................................................................................. 4
1.3
Scope of the Study ..................................................................................................... 4
1.4
Objectives................................................................................................................... 6
1.5
Overview of the Methodology ................................................................................... 7
1.6
Research Structure ..................................................................................................... 8
1.7
Outline of the Dissertation ......................................................................................... 8
CHAPTER 2 ................................................................................................................................. 12
BACKGROUND: PROBLEM CONTEXT ............................................................................... 12
2.1
Regional Transportation Planning............................................................................ 12
2.1.1
History ............................................................................................................. 13
2.1.2
Overview of RTP Methodology ...................................................................... 19
2.1.3
Opportunities and Challenges.......................................................................... 26
2.2
Asset Management as a New Approach................................................................... 29
2.3
Integrating Asset Management into the Metropolitan Planning Process ................. 33
CHAPTER 3 ................................................................................................................................. 38
BACKGROUND: ASSET MANAGEMENT APPROACHES AND TOOLS ....................... 38
4.1
BCA as an Essential Element in Asset Management ............................................... 38
3.2
HERS-ST as a BCA Tool for Highway Planning .................................................... 42
3.3
MCA Framework for Transportation Investment Decision-Making ....................... 47
3.4
AHP as an Evaluation Procedure ............................................................................. 48
3.4.1
Alternative MCA Techniques.......................................................................... 48
3.4.2
The Analytical Hierarchy Process (AHP) ....................................................... 50
3.5
Investment Packages as a Solution Approach.......................................................... 55
ii
CHAPTER 4 ................................................................................................................................. 60
METHODOLOGY ...................................................................................................................... 60
4.1
BCA Case Studies .................................................................................................... 60
4.1.1
Data ................................................................................................................. 61
4.1.2
Methods ........................................................................................................... 64
4.2
MCA Case Study...................................................................................................... 68
4.2.1
Data ................................................................................................................. 69
4.2.2
Methods ........................................................................................................... 69
ChAPTER 5.................................................................................................................................. 76
BCA CASE STUDIES USING HERS-ST.................................................................................. 76
5.1
Overview of the Case Study Regions....................................................................... 76
5.1.1
Northeastern Illinois Region............................................................................ 77
5.1.2
WILMAPCO Region....................................................................................... 79
5.1.3
Delaware Valley Region.................................................................................. 82
5.2
Case Study Findings................................................................................................. 88
5.2.1
System Improvement Costs............................................................................. 88
5.2.2
System Conditions........................................................................................... 91
5.2.3
Investment Returns.......................................................................................... 95
5.2.4
Investment Structure by Investment Type..................................................... 100
5.3
HERS-ST Case Studies: Observation and Conclusion .......................................... 100
CHAPTER 6 ............................................................................................................................... 108
CONCLUSIONS AND FUTURE WORK ............................................................................... 108
7.1
Conclusions ............................................................................................................ 108
7.2
Limitations ............................................................................................................. 113
7.2.1
Limitations of the Research........................................................................... 113
7.2.2
Limitations of the Method ............................................................................. 114
7.2.3
Limitations of the Data and Tools ................................................................. 115
7.3
Future Work ........................................................................................................... 117
7.4
Contributions.......................................................................................................... 118
BIBLIOGRAPHY ...................................................................................................................... 122
APPENDIX I .............................................................................................................................. 127
ASSESSING INVESTMENT STRATEGIES ......................................................................... 127
AI.1 Survey Report......................................................................................................... 127
AI.2 AHP Evaluations.................................................................................................... 134
AI.3 AHP Synthesis Results........................................................................................... 141
AI.4 Discussions............................................................................................................. 146
APPENDIX II: SAMPLE AHP SURVEY ............................................................................... 153
iii
LIST OF TABLES
Table 1: Benefit and Cost Components in HERS-ST and Associated Stakeholders........ 44
Table 2: AHP Value Scale ................................................................................................ 54
Table 3: Pros and Cons of Transportation Investment Packages Seen from the
Perspectives of Instrumental and Communicative Rationality........................... 58
Table 4: HPMS Data Items ............................................................................................... 62
Table 5: Input Data Processing Error - Regional Expansion Factor Calculation ............. 64
Table 6: Example of Simple Paired Comparison Matrix.................................................. 74
Table 7: Characteristics of the Three Case Study Regions............................................... 87
Table 8: Investment Statistics for All Scenarios............................................................... 90
Table 9: Investment Returns on Total Benefits for All Scenarios by Pair Comparison ... 96
Table 10: Pairwise Comparison Weighting for Performance Measures with Respect to
their Covering Objectives ............................................................................... 133
Table 11: Normalized Numerical Values for all HERS-ST Alternatives ....................... 139
Table 12: Synthesized Results for the Second Level in Hierarchy................................. 144
Table 13: Synthesized Results for the Third Level in Hierarchy.................................... 144
Table 14: Solution Package Characteristics with respect to four Regional Objectives.. 151
iv
LIST OF FIGURES
Figure 1: Research Flow Chart ......................................................................................... 10
Figure 2: Research Structure............................................................................................. 11
Figure 3: Functional Features of the Analytical Tools Employed.................................... 11
Figure 4: Asset Management Framework......................................................................... 31
Figure 5: Major Components of Benefit-Cost Analysis ................................................... 39
Figure 6: Simplified Representation of HERS/HERS-ST Modeling Process .................. 45
Figure 7: HERS/HERS-ST Modeling Logic..................................................................... 45
Figure 8: Hierarchy of Multi-Criteria Package Solutions to the Regional Transportation
Investment Strategies ........................................................................................ 71
Figure 9: The Cost Hierarchy ........................................................................................... 72
Figure 10: The Benefit Hierarchy ..................................................................................... 73
Figure 11: System Deficiencies for All Do Nothing Scenarios........................................ 92
Figure 12: System Deficiencies for All CATS Scenarios................................................. 93
Figure 13: System Deficiencies for All WILMAPCO Scenarios ..................................... 94
Figure 14: System Deficiencies of All DVRPC Scenarios............................................... 94
Figure 15: Investment Returns on Maintenance Cost Savings for All Scenarios by Pair
Comparison ...................................................................................................... 97
Figure 16: Investment Returns on User Benefits for all Scenarios by Pair Comparison.. 98
Figure 17: Investment Returns on Pollution Damage Savings for all Scenarios by Pair
Comparison ...................................................................................................... 99
Figure 18: Decision-Making Plot for CATS................................................................... 102
Figure 19: Decision-Making Plot for WILMAPCO ....................................................... 102
Figure 20: Decision-Making Plot for DVRPC................................................................ 103
Figure 21: General Format of Planning Hierarchies....................................................... 112
Figure 22: Pairwise Comparison Weightings for Stakeholders with Respect to Regional
Goal................................................................................................................ 129
Figure 23: Pairwise Comparison Weightings for Objectives with Respect to Users ..... 131
Figure 24: Pairwise Comparison Weightings for Objectives with Respective to Agencies
......................................................................................................................................... 131
Figure 25: Pairwise Comparison Weightings for Objectives with Respective to the
General Public................................................................................................ 131
Figure 26: Illustration of AHP Evaluation Process......................................................... 135
Figure 27: Synthesized Results for the Entire Hierarchy................................................ 142
v
GLOSSARY AND ACRONYMS
AHP
Analytic Hierarchy Process - The AHP is one of the most
used and well-known MCA techniques that was developed
by Saaty in the late 1970s. The AHP is a mathematical
decision making technique that allows consideration of
both qualitative and quantitative aspects of decisions. It
reduces complex decisions to a series of one-on-one
comparisons, then synthesizes the results.
Asset Management
A systematic process of maintaining, upgrading, and
operating physical assets cost-effectively, which combines
engineering principles with sound business practices and
economic theory, and provides tools to facilitate a more
organized, logical, and comprehensive approach to
decision-making.
BCA
Benefit-Cost Analysis - The process involves, whether
explicitly or implicitly, weighing the total expected costs
against the total expected benefits of one or more actions in
order to choose the best or most profitable option. It also
often refers to as CBA (Cost-Benefit Analysis) in the
United States.
FHWA
Federal Highway Administration – The agency of the U.S.
Department of Transportation that funds surface
transportation planning and programs, primarily highways.
FTA
Federal Transit Administration – The Agency of the U.S.
Department of Transportation that funds surface
transportation planning and programs, primarily transit.
Infrastructure
The Physical structures supports a community, such as
roads, sidewalks, sewers, rail lines, and bridges.
vi
ITS
Intelligent Transportation Systems – Technologies that
improve the management and efficiency of the
transportation system, such as electronic toll collection,
timed traffic signals and on-board navigation systems.
Intermodal
The term “mode” refers to the various forms of
transportation options including driving, riding a bus or
train, bicycling and walking. Intermodal refers to the
connection between modes.
ISTEA
The acronym for the federal Intermodal Surface
Transportation Efficiency Act of 1991, landmark
legislation that restructured programs for all methods of
transportation. Subsequent legislation has been referred to
as TEA 21 and SAFETEA-LU.
MPO
Metropolitan Planning Organization – The organization
required by the federal government, designated by states,
and operated by local officials, for developing
transportation programs in urban areas of 50,000 or more
people. Their purpose is to plan and coordinate
transportation investments in the region and to involve the
public in transportation decisions.
Mobility
The movement of people or goods throughout the
communities and across the regions. Mobility is usually
measured in terms of travel time, comfort, convenience,
safety and cost.
Multi-modal
The term “mode” refers to the various forms of
transportation options including driving, riding a bus or
train, bicycling and walking. Multi-modal refers to the
availability of several transportation options.
MCA
Multi-Criteria Assessment (MCA) or Multi-Criteria
Decision Analysis (MCDA) – MCA/MCDA is a discipline
aimed at supporting decision makers who are faced with
making numerous and conflicting evaluations. It aims at
highlighting these conflicts and deriving a way to come to a
compromise in a transparent process.
M&I
Maintenance and Improvements of the transportation
systems. M & I of existing transportation infrastructure is
vii
often in competition for funding with new construction
projects that are aimed at capacity expansion.
Package Solutions
A variety of transport policy measures including both
investments in different modes and demand management
(Jones 1991). In this research, the solution packages consist
of highway system investment decisions, improvement
projects, and management recommendations.
RTP
Regional Transportation Plan – A blueprint to guide the
region’s transportation for a minimum of a 20-year period.
Federal law requires the RTP be updated every four years
(in areas that do not meet air quality standards) to ensure
that the plan remains current and effective at achieving the
goals. Formerly known as the Metropolitan Transportation
Plan (MTP) or Long-range Regional Transportation Plan
(LRTP)
SAFETEA-LU
The acronym for most recent, transportation reauthorization legislation. Enacted into law in July of 2005,
the bill authorizes $284 billion of federal funding through
2009.
TEA-21
The acronym for the 1998 federal Transportation Equity
Act for the 21st Century. TEA 21 replaced ISTEA, but
continued and expanded ISTEA’s restructured programs for
all modes of transportation. It provides guidelines to
authorized federal funding of transportation projects.
TIP
Transportation Improvement Program, which is the
regionally agreed upon list of priority projects to be
advanced during a short (3-4 year) timeframe. As required
by federal law (ISTEA and TEA-21), the TIP document
must list all projects that intend to use the federal funds,
along with non-federally funded projects that are regionally
significant. These projects are multimodal. The TIP usually
represents the implementation of recommendations from
the RTP into a short term program of improvements.
However, the TIP is not a financial schedule of project
implementation, and is not a guarantee of project
implementation either.
viii
Transportation Trust
Funds
These are State and Federal accounts to which all revenues
dedicated exclusively for transportation are deposited.
Revenues are derived from gasoline and other taxes, tolls,
user fees, and investment income and used to fund both
capital and operational transportation expenses.
VMT
Vehicle Miles Traveled – A standard area-wide measure of
travel activity, calculated by multiplying average trip length
by the total number of trips.
ix
ABSTRACT
Financial constraints are the most commonly recognized and major
constraints identified in Regional Transportation Plans (RTPs). However, fully
functioning benefit-cost analysis (BCA) procedures that lend credibility and
responsibility to the transportation investment decisions have not been widely adopted for
RTPs. Therefore, there is a recognized value in illustrating the gap between projections of
our transportation needs and the financial resources available to meet them from a BCA
perspective. On the other hand, increasing claims have been advanced for asset
management as a promising new approach to infrastructure management, and asset
management has been seen as an effective response to the fiscal challenges confronting
the United States’ transportation infrastructure. Realizing all these opportunities and
challenges, this thesis describes and defines the financial analysis gaps in the current
regional long-range transportation planning practices and demonstrates the application of
a hierarchical decision analysis framework for managing existing regional transportation
infrastructure cost-effectively. Specifically, this research uses the concepts of BenefitCost Analysis (BCA), Multi-Criteria Assessment (MCA), and package solutions,
Highway Economic Requirements System (AHP) and Analytical Hierarchy Process
(AHP) as asset management tools, and uses case studies as research method, to achieve
x
the objective of providing a framework for rigorous analysis of investment strategies for
maintaining and improving the existing regional highway infrastructure.
xi
EXECUTIVE SUMMARY
Financial constraints are the most commonly recognized and major
constraints identified in Regional Transportation Plans (RTPs). Most current long-range
planning efforts employ a generalized approach to project revenues for transportation
investment and produce a very long list of desired capital improvements in the plan.
Balancing these projections against the estimated cost of implementing the plan’s
recommendations functions as the principal constraint. However, fully functioning
benefit-cost analysis (BCA) procedures that lend credibility and responsibility to the
transportation investment decisions have not been widely adopted for RTPs. Therefore,
there is a recognized value in illustrating the gap between projections of our
transportation needs and the financial resources available to meet them from a BCA
perspective.
On the other hand, increasing claims have been advanced for asset
management as a promising new approach to infrastructure management, and asset
management has been seen as an effective response to the fiscal challenges confronting
the United States’ transportation infrastructure. Basically, asset management refers to a
systematic process of maintaining, upgrading, and operating physical assets cost-
xii
effectively, and provides tools to facilitate a more organized, logical, and comprehensive
approach to decision-making.
Realizing all these opportunities and challenges, this thesis describes and
defines the financial analysis gaps in the current regional long-range transportation
planning practices as identified above and demonstrates the application of a hierarchical
decision analysis framework as a research design for managing existing regional
transportation infrastructure cost-effectively. Specifically, this research uses the concepts
of Benefit-Cost Analysis, Multi-Criteria Assessment, and package solutions, the Highway
Economic Requirements System (HERS) and the Analytic Hierarchy Process (AHP) as
asset management tools, and case studies as the research method, to achieve the objective
of providing a framework for rigorous analysis of investment strategies for maintaining
and improving the existing regional highway infrastructure.
The Benefit-Cost Analysis case studies for the Northeastern Illinois region,
the Delaware Valley region, and the Wilmington region provide insights into the process.
The results show that compared with the traditional funding constrained scenarios
suggested in the Regional Transportation Plans, given the same available funds, the
Benefit Cost Ratio oriented scenarios give better system conditions over each funding
period, reduce system reconstruction deficiencies by up to 34% of the initial deficiencies,
and obtain up to 21% more overall investment returns on user benefits and up to 24%
more returns on system maintenance costs savings.
xiii
Interestingly, the application of AHP case study of the Wilmington region
incorporating both the HERS modeling results and stakeholder preferences obtained
through surveys, shows that among the six AHP adjusted HERS scenarios, the “Maintain
Current Condition” and the “WILMAPCO Funding Constrained” scenarios should be
given the highest priorities over the BCR oriented scenarios.
Through further investigation, this research recognizes that the results from
Benefit-Cost Analysis using tools like HERS should be used selectively and analytically.
One solution option suggested in the thesis is to generate a solution package for each of
the BCA scenarios, which is a combination of programs, projects, and implementation
strategies together with their strategic aims and policy concerns. Any single change in the
package profile will be analytically adjusted, while still keeping the major part of the
package profile intact. This research takes advantage of the package solution approach in
shifting from project level analysis to strategic planning level analysis. The final
argument is that we are not simply involving BCA into the regional transportation
planning process in this research, but we are also trying to support the notion that a
region needs a healthy, functioning network of physical assets and also needs well
defined goals and policies to direct future development.
HERS and HERS-ST were originally developed as a national and state
level economic analysis computer model for highway system. This research makes the
xiv
first attempt to apply the HERS-ST analysis tool to the regional level and proposes the
integration process of the application of HERS-ST with the existing regional
transportation planning process. Furthermore, a benefit-cost analysis based multi-criteria
investment analysis procedure is not only about financing regional transportation
infrastructure, but also provides the possibility of simplifying dimensions of measures,
taking into account network effects, involving qualitative factors in decision-making, and
improving accountability in the transportation planning practices. The research
contributes a solution to all the concerns addressed above. From the practical perspective,
the traditional transportation modeling process only takes into account narrow economic
measures such as travel delay for measuring congestions. Ideally the transportation
models should incorporate broader economic measures such as overall economic utility
of travelers in assessing road congestion. This thesis also takes several steps forward in
contributing to this aspect of solution discussion for regional transportation planning.
xv
CHAPTER 1
INTRODUCTION
1.1
Problem Statement
Over the past decade, Metropolitan Planning Organizations (MPOs) have
played a more central role in transportation decision making, even though these
organizations do not maintain or operate transportation assets. During the same period in
most developed regions, there is a greater emphasis on maintaining and operating
existing facilities rather than building new facilities. The application of asset management
concepts and tools offer a new way for MPOs to address these issues. This thesis explores
ways to address problems with aging infrastructure, pressures from increasing demand
and trade-offs among different objectives.
Transportation systems play a vital role in the nation’s economy, and the
United States has made significant investments in its transportation infrastructure.
Efficient and well-maintained infrastructure systems are essential for economic
competitiveness and sustainable growth, because existing infrastructure systems cannot
be simply replaced. Among all the infrastructure related concerns, a major challenge for
1
the transportation decision makers is finding adequate funding to maintain and enhance
the nation’s current transportation system. Despite the past decades’ growth in public
funding allocated to transportation systems, available public resources are not expected to
be adequate to fully address the actual needs. In recent years, increasing claims have been
advanced for asset management as a promising new approach to infrastructure
management (McNeil et al. 2000), and asset management has been seen as an effective
response to the fiscal challenges confronting the United States’ transportation
infrastructure. Effective management of transportation infrastructure depends in part on
reliable methods for estimating the amount of continuing investment required for
maintaining and improving the nation’s existing transportation system.
From a transportation planning perspective, urban transportation has long
been recognized as a regional issue. Early in the 20th century, states and the federal
government recognized the regional nature of transportation infrastructure. “Regional
planning became official when the federal government mandated the comprehensive,
cooperative, and continuing (“3C”) process in 1962 as a condition for receipt of federal
transportation funds” (Frederickson, 1999). “The regional transportation planning process
is institutionalized through planning and environmental review requirements” (Giuliano,
2007). Institutionally, it has been a federal requirement that every urbanized area with a
population of 50,000 or more form a Metropolitan Planning Organization (MPO) that is
responsible for the region’s long-range transportation plan and short-range transportation
improvement programs to guide the transportation investments within the region. In
1
addition, many key planning factors were defined in the Intermodal Surface
Transportation Efficiency Act of 1991 (ISTEA), the intent of which guides the structure
of most regions’ transportation plans. At the same time, “pressures from growth in traffic
in general and heavy vehicles in particular, aging infrastructure and constrained budgets,
have placed increasing emphasis on asset management in state DOTs” (Pagano et al,
2005). While MPOs are aware of asset management strategies, the principles and
concepts of asset management have not been explicitly integrated into the regional
planning process; neither have a well defined financial and economic analysis procedure.
The northeastern Illinois region, for example, with its unique geographic features and
highly populated urban centers, has defined and continued to evolve its own systematic
and comprehensive transportation planning procedures. Chicago Area Transportation
Study (CATS) as the region’s transportation planning MPO published in 2003 its most
recent plan (25-year) – CATS 2030 Regional Transportation Plan (2030 RTP). As one of
the final plan recommendations, $47 billion out of a total of $65 billion capital
investments is allocated to system maintenance and reconstruction, which accounts for
about 72% of the total projected revenue (CATS, 2006). There is no doubt that the
maintenance and improvement of existing regional transportation system is a major
proportion of the overall plan recommendations, but so far the corresponding analysis
and research work regarding how to allocate these system maintenance and
reconstruction revenue has not yet been comparable.
Like many other RTPs, one of the major constraints identified in the CATS
2030 RTP is the financial constraint. Although the 2030 RTP claims not to be a financial
2
plan or a financial planning exercise, the plan calls for preparation of a long-range
financial plan for the northeastern Illinois region. Similarly, many other metropolitan
regions are also in need of such long-range financial plans as essential supplements to
their 20-year RTPs and a key component in their regional Asset Management Systems
(AMS). Most current long-range planning efforts employ a generalized approach to
projecting revenues for transportation investment. Balancing these projections against
estimated cost of implementing the plan’s recommendations functions as the principal
constraint and produces a very long list of desired capital improvements in the plan.
However, fully functioning benefit-cost analysis (BCA) procedures which lend credibility
and responsibility to the transportation investment decisions have not been seen widely
adopted for RTPs. Therefore, as also addressed in 2030 RTP, there is a recognized value
in illustrating the gap between projections of our transportation needs and the financial
resources available to meet them from a BCA perspective.
Realizing all these opportunities and challenges, this research describes
and defines the financial analysis gaps in the current regional long-range transportation
planning practices as identified above and to seek an alternative investment decisionmaking framework for managing existing regional transportation infrastructure costeffectively.
3
1.2
Motivation
Introduced to the transportation arena in early 1990s, the “package approach”
is considered as one of the most favorable solution approaches in assessing system-wide
transportation investment strategies (Jones P., 1991). One of the solution alternatives to the
financial analysis gaps identified in RTPs could be identified as transportation investment
package solutions, which are combinations of improvement projects and timing strategies
that could be eventually integrated into the typical regional transportation planning
process. Ideally, the process of generating package solutions should be able to be further
developed into a functioning procedure for RTPs, and the procedures should also have
the ability to be generalized across geographical regions. Ultimately, these investment
packages should be generated by performing both quantitative and qualitative analyses
for roads, bridges, public transit, and other transportation modes, and could be integrated
into the Asset Management System (AMS) as an overall solution to the regional
transportation planning in guiding transportation investment decision-making.
1.3
Scope of the Study
This study defines and illustrates financial analysis gaps in current regional
transportation plans via comparative case studies for three geographical regions:
Northeastern Illinois Region, Delaware Valley Region, and Wilmington Region. The
need for a systematic BCA approach for RTPs is discussed accordingly. The research
4
does not focus on looking for additional financial resources. Instead, it emphasizes the
development of alternative investment strategies under current funding constraints. In
seeking solutions to the financial analysis gaps identified, the Highway Economic
Requirement System – State Version (HERS-ST) is used as a BCA tool to generate initial
inputs to the multi-criteria hierarchy framework proposed in this study, whose outputs are
regional transportation investment packages. Realizing the fact that road projects have
been the starting points of most of the investment packages, the renewal of the existing
regional highway systems is the main focus. As part of the research design, a specific
case study is developed at the end of this study (Appendix I) to demonstrate the
formulation of package solutions hierarchy and how the hierarchy functions in regional
transportation planning context. A sample set of investment packages for the Wilmington
region is also presented.
Future work beyond this research will be to incorporate new construction, to
investigate the integration of other transportation modes in the each package solution, to
analyze intermodal effects from both engineering and economic perspectives, to
quantitatively incorporate region economic impacts factor, and to evaluate the impacts of
the transportation investment package solutions on achieving the financial and economic
analysis goals of RTPs.
5
1.4
Objectives
Asset management systems and regional transportation planning processes
are quite complicated, and the question of how to allocate financial and economic
resources effectively to the regional transportation infrastructure network is even harder
to answer. This study mainly focuses on existing regional highway infrastructure systems.
Specifically, the study proposes to address three major objectives:
1.
To provide a framework for rigorous analysis of investment strategies for
maintaining and improving the existing regional highway infrastructure.
2.
To describe and define the financial analysis gaps identified in the current
RTPs. This research does comparative case studies of the Northeastern Illinois
Region, Delaware Valley Region, and Wilmington Region, using HERS-ST
model.
3.
To develop a strategy to address these financial and economic analysis gaps
in RTPs focusing on system renewal. Based on the case study results, a
systematic hierarchy framework that generates regional transportation
investment packages will be developed in order to fill the financial analysis
gaps identified in RTPs. An in-depth case study for Wilmington region is
developed, and application and implementation issues related to the hierarchy
framework are discussed accordingly.
6
1.5
Overview of the Methodology
Emphasizing asset management as a new approach to regional transportation
planning, this research investigates several commonly-used asset management tools and
approaches, namely benefit-cost analysis approach, HERS-ST computer tool, multicriteria analysis approach, and the analytical hierarchy process (AHP) as a Multi-Criteria
Assessment (MCA)
procedure. The methodology integrates these four tools and
approaches. These tools and approaches are related in the sense that HERS-ST is an
implementation of BCA, and BCA is a specific type of MCA.
In terms of output of this research, the package solutions concept is
employed in making final suggestions on financial analysis strategies for regional
transportation planning. Basically, the term “package solutions” refers to a variety of
transport policy measures including both investments in different modes and demand
management (Jones 1991). The research is structured around the following steps:
(1) Using HERS-ST computer model to describe and define the financial
analysis gaps identified in the current RTPs;
(2) Formulating financial analysis hierarchy for RTPs focusing on system
renewal using MCA approach;
(3) Generating inputs of the hierarchy using BCA approaches;
7
(4) Following AHP procedure to quantify and solve the hierarchy and
approach the final recommendations. The MCA analysis is presented in the form of a
research design for future research.
1.6
Research Structure
An illustration of the research flow in this dissertation is shown in Figure 1.
Figure 2 and Figure 3 illustrate the logical structure of this research with a separate layer
of the functioning features of the tools employed. These summaries are developed to
provide a picture of the structural linkages among all the research elements included in
this dissertation. The research uses the concepts of BCA, MCA, and package solutions,
HERS-ST and AHP as tools, and case studies to identify the issues and test the
methodology.
1.7
Outline of the Dissertation
Chapter 2 summarizes the problem background and key concepts related to
regional transportation planning and asset management. Asset management approaches
and tools used in this research are introduced in Chapter 3, along with an interpretation of
the interrelationships among them. Chapter 4 further explores the research methodology
of this thesis, including detailed description of data and methods used in each of the four
8
case studies. The three BCA case studies are presented in Chapter 5. One MCA case
study for WILMAPCO region is presented as a research design in Appendix I and II. The
thesis is summarized in Chapter 6 which includes conclusions, limitations, future work,
and contributions of the research.
9
Problem
Competing Demands:
Aging Infrastructure,
Increasing Travel.
Constraints: Limited
Funding
Seeking Solutions
Asset Management at
the Regional Level
BCA as an Essential Approach
in Asset Management
All Transportation Modes,
Transportation Network
Highways
Analysis
Process
HERS-ST as
a BCA Tool
Case Studies
Legend
Solution
Package Solutions
Case
Study
MCA Hierarchy
Figure 1: Research Flow Chart
10
Asset Management
Tools and Approaches
Applications
Objective
Framework
Components
Tools
Package Solutions
AHP
MCA
RTP Objectives
/ Attributes
BCA
Modules
HERS-ST
Figure 2: Research Structure
Information Flow
Approach
Case Studies
HERS-ST
Scenario 1
Scenario 2
Scenario 3
…
Case Study
AHP
Package
Solution 1
Package
Solution 2
Package
Solution 3
…
Figure 3: Functional Features of the Analytical Tools Employed
11
CHAPTER 2
BACKGROUND: PROBLEM CONTEXT
This research requires an understanding of the RTP process, asset
management, and asset management tools. The final recommendations build on the
concept of package solutions, and the Analytical Hierarchy Process (AHP) is selected as
a tool to assess investment packages in an asset management framework. BCA and MCA
are incorporated into the package solutions hierarchy framework as asset management
tools. This chapter reviews these concepts and tools including a description, usage,
history, and where appropriate connections to the research are explicitly identified.
2.1
Regional Transportation Planning
At the regional level, the transportation planning process begins with a
representation of the current and future land use and then projects future traffic volumes.
In terms of planning process and techniques, regional transportation planning has only
changed a little over the past three decades. Yet, the regional transportation planning
12
practices have evolved noticeably over these years in response to changing issues,
conditions, values, and a better understanding of the relationships among transportation
investments, economic growth, and the demand for transportation. Because highway and
transit facilities and services are owned and operated largely by the state and local
agencies, urban transportation planning in the United States has always been conducted
by state and local agencies. The federal government has been responsible for setting
national policies, providing financial aid, supplying technical assistance, and conducting
training and research. From a planning perspective, the most important federal financial
assistance requirement has been that transportation projects in urbanized areas of 50,000
or more in population are proposed based on an urban transportation planning process.
“This requirement was first incorporated into the Federal-Aid Highway Act of 1962, and
other requirements have been incorporated into federal legislations over the years”
(Weiner, 1997). These federal legislated requirements are serving as the guidance of
current regional transportation planning practices.
2.1.1
History
In order to better understand the problem context of this study, a historical
overview summarizes major benchmarks in the development of urban and regional
transportation planning.
13
The need for highway planning was recognized in the 1930s. During late
1920s and early 1930s, a number of studies were conducted to determine the capacity of
highways to carry traffic, and the publication of the first edition of the Highway Capacity
Manual (U.S. Department of Commerce, 1950) first defined the standards for highway
design and planning in the United States. In the 1940s, the interregional character of
highway planning was recognized and first emphasized, which led to the beginning of
urban transportation planning. During the period of the late 1940s and early 1950s, the
concepts and standards of urban transportation planning were advanced, and the need to
coordinate highway with other modes of transportation and for cooperation at all levels of
governments was recognized. More rigorous urban transportation planning began in the
U.S. with the development of travel demand models in the 1950s and then became
formalized in the 1960s. During the 1960s, intergovernmental coordination was
noticeably improved, and consideration of issues such as safety, historical preservation,
reserved bus lanes were integrated into the transportation planning process. The other two
important elements that were also emerged during late 1960s and early 1970s were
environmental concerns and public participation, and they are still critical elements of the
current regional transportation planning process. The importance of multi-modal
transportation planning was widely realized in 1970s, and legislation was passed that
increased capital funds available for mass transportation and provided federal assistance
for operating costs. “These provisions placed transit on a more equal footing with
14
highways and considerably strengthened multi-modal planning and implementation”
(Weiner, 1997).
The transition from exclusively long-range transportation planning at all
levels to the inclusion of short-term planning was started in late 1970s. During the midto-late 1970s, the federal government took steps to better integrate urban transportation
planning at the local level, and to require shorter-range capital improvement programs
along with long-range plans. Also during late 1970s, the nation’s economy was in a
transition from a predominantly manufacturing base to one that had larger share
concentrated in service, communication, and high technology industries, and people were
moving to those areas of the country where the new jobs were being created, especially
the South and the West. Under this social and economic context, transportation planning
at the state and local level required cooperation among agencies and the private sectors in
order to alleviate problems such as economic distress. At the regional level, the regional
economic effects of transportation planning were first emphasized during this period.
After the decades of involvement, the complexity of transportation planning was
dramatically increased. When it came to the early 1980s, the federal government ushered
in a new mood in the nation to decentralize control and authority in urban transportation
planning. This transition had helped reduced the regulationary burdens that regional
transportation agencies were experiencing. By early 1990s, the era of major highway
construction was over in most urban areas. With only limited highway expansion possible,
new approaches needed to be found to serve the continuing travel demand. Driven by this
15
urgent need, many transportation agencies entered into strategic management and
planning processes to identify the scope and nature of these social and geographical
changes, and to develop strategies to address these issues in developing their new
transportation planning procedures. In addition, the transportation agencies also shifted
their focuses toward longer term time horizons from 20-year to 30-year in the 1990s. The
most recent development in transportation planning was the concept of sustainable
development, which was advocated as one of the biggest concerns of urban transportation
planning in the 21st century. “The term ‘sustainable development’ became popularized in
1987 when the World Commission on Environment used it describe a process of
economic growth” (Weiner, 1997), thus, the term “sustainability” in transportation
planning was born with a global perspective. The concern for environment quality and
sustainable development brought renewed interest in the relationship between land use
development patterns and transportation demand.
Three key Acts authorized in the 1990s helped urban transportation planning
evolve into the new era and shaped the highway program to meet the nation’s changing
transportation needs. The Intermodal Surface Transportation Efficiency Act 1991 (ISTEA)
is a U.S. federal law that posed a major change to transportation planning and policy, as
the first U.S. federal legislation on the subject in the post-Interstate Highway System era.
It presented an overall intermodal approach to highway and transit funding with
collaborative planning requirements, giving significant additional powers to MPOs.
Signed into law on December 18, 1991, it expired in 1997. It was followed by the
16
Transportation Equity Act for the 21st Century (TEA-21) and the most recently in 2005,
the Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users
(SAFETEA-LU).
The TEA-21 authorized the Federal surface transportation programs for
highways, highway safety, and transit for the 6-year period 1998-2003. The
transportation equity act requires that seven planning factors be included in the regional
transportation plans: the plans must: 1). support the economic vitality of the metropolitan
planning area, especially by enabling global competitiveness, productivity and efficiency;
2). increase the safety and security for the transportation system for motorized and nonmotorized users; 3). increase the accessibility and mobility options available to people
and for freight; 4). protect and enhance the environment promote energy conservation
and improve the quality of life; 5). enhance the integration of connectivity of the
transportation system, across and between modes, for people and freight; 6). promote
efficient system management and operation; 7). emphasize the efficient preservation of
existing transportation system (TEA-21).
The SAFETY-LU, which governs U.S. federal surface transportation
spending through 2010, was signed into law by President Bush in Montgomery, Illinois,
on August 10, 2005. The $286.4 billion measure contains a host of provisions designed to
improve and maintain the transportation infrastructure in the U.S., especially the highway
and interstate road system. It also contained funding for over six thousand earmarks for
17
home-district projects. SAFETEA-LU addresses the many challenges facing our
transportation system today – challenges such as improving safety, reducing traffic
congestion, improving efficiency in freight movement, increasing intermodal
connectivity, and protecting the environment – as well as laying the groundwork for
addressing future challenges. SAFETEA-LU promotes more efficient and effective
Federal surface transportation programs by focusing on transportation issues of national
significance, while giving State and local transportation decision makers more flexibility
for solving transportation problems in their communities (FHWA).
All these Acts emphasize planning and public participation. Encouraging
public participation in regional transportation issues has become a key element in
regional transportation plans. Design and implement strategic public outreach / input
processes have tailored to the specific long-range work program priorities identified in
many RTPs, including forums, discussion groups, electronic communications, and media.
According to SAFETEA-LU, participation of walkers, cyclists, and people with
disabilities are encouraged in the public involvement process, and private sector is also
invited to participate the regional transportation and land use planning process.
In short, the early urban transportation planning studies were primarily
system-oriented with a 20-year time horizon and region-wide in scope. Gradually,
starting in the early 1970s, planning processes turned their attention to shorter-term time
horizons and the corridor-level scale. The emerging broadened concept of transportation
18
system management enabled transportation planning to encompass a whole range of
techniques to increase the utilization and productivity of existing transportation assets.
And this concept was further evolved into the new approach as asset management in
1990s.
2.1.2
Overview of RTP Methodology
The current regional transportation processes are widely used by regional
planning agencies, to address the needs of their regions, and recognizing the concerns of
citizens groups and of local governments. “They are the “bottom-up” needs that come
from the constituent groups, in addition to the “top-down” requirements from the Federal
laws” (Johnston, 2003). Regional transportation planning is the only regional planning
process undertaken regularly in most medium-sized and large urban areas, and changes in
transportation systems can affect not only traffic congestion, but also air quality,
economic development, equity, and land development patterns. Thus, regional
transportation planning is a vital arena to understand.
Basically, the regional planning process is to use the region-wide 20-year
projections of future land uses and demographic data to generate future trips and to
forecast future traffic. First, the RTPs have to take into account various policy and
legislation requirements, among which the air quality conformity, social equity, and
19
public involvement are the most widely known ones. “The Clean Air Act of 1971
required all states to adopt a State Implementation Plan (SIP) that includes an emissions
inventory for each region in the stat and a plan for attainment of all ambient Federal air
quality standards” (Johnson, 2003). This was considered as the first legislated
requirement for all RTPs to show attainment of the vehicle emissions reductions specified
in the SIP, though the modeling of travel and emission from on-road vehicles. Another
important policy that advanced in transportation planning in recent decades was the Clean
Air Act of 1990, which greatly strengthened the previous requirement by providing that
Federal transportation funds can be withheld from regions that adopt transportation plans,
but cannot show attainment through modeling emissions reductions. Generally, the
emission modeling is governed by the US Environment Protection Agency (USEPA), and
was adopted by most MPOs in making their RTPs.
Equally as another important concern, the Civil Rights Act of 1964 has
resulted in a series of transportation discrimination cases that are making MPOs consider
the equity effects of their plans more seriously. Since then, some states have adopted
their own statutes requiring the analysis of such equity issues. Moreover, citizens were
concerned that the changes were being made across their surrounding neighborhoods,
without their views being considered. “In late 1969, the basic guidelines for the 3C
(comprehensive, cooperative, and continuing) planning process were amended to require
citizen participation in all phases of the planning process from the setting of goals
through the analysis of alternatives” (Weiner, 1997). Consequently, it became the
20
responsibility of the planning agency to seek out public views. It is worth noting that, to
get theoretically complete economic equity measures, the MPOs have to run economic
models within their travel model or land use model. This is the least understood and least
developed area of MPO practice, even though such models are in widespread use in other
developed disciplines.
Regional transportation models use a twenty-to-thirty-year planning horizon.
From a modeling perspective, some of the Jonson’s (1994, 2003) work discusses the
MPOs’ capabilities for modeling transportation control measures, and Miller, Kriger, and
Hunt (1999) presents recommendations for research on travel models and land use
models. The following is a brief summary of this work.
Technically, the Pre-Analysis Phase consists of: 1). problem identification; 2).
formulation of objectives; 3). data collection; 4). generation of alternatives; and 5).
definition of evaluation measures. The most important aspect of this phase is to define
problems and identify broad objectives, which are also critical factors that are considered
in the investment analysis framework proposed in this study. The definition of key
problems, goals, and objectives brought the regional transportation planning focus to a
higher level, involving a comprehensive view of providing transportation service, not just
increases in road capacity. For example, a “roadway widening” in a RTP should be
defined under the objective to reduce congestion as “maximize accessibility in the
region”. Furthermore, a second objective could also be downplayed in this analysis by
21
defining the objective to focus on those travelers with few options and so encompasses
equity. Given the broad policy charge in the most recent transportation acts and the need
to examine TCM in air quality law, MPOs need to define transportation planning
objectives in a holistic fashion, in order to not bias their studies.
Another important but weak step in regional transportation planning is the
generation of alternatives. Most MPOs initially analyze several roadway and transit
investment schemes, but do this in-house, not in published documents. In their official
RTPs, the MPOs usually analyze only the Preferred Plan and the No Action Alternative,
which is required by National Environmental Policy Act (NEPA). The preferred plan, in
many instances, seems to be the maximum investment in roads where the plan can just
meet the future emissions requirements. This study focuses on these concerns and
involves BCA as a supplemental modeling process to generate more investment
alternatives by setting up a rigorous qualitative and quantitative analytical framework.
The third step, also a weakness in this phase, is inadequate plan evaluation
criteria. Travel models output measures of congestion, such as level-of-service and
personal travel delay. Later, the Vehicle Mileage Traveled (VMT) by speed class
projections output is sent to emission models. However, MPOs seldom evaluate equity
outcomes well and they seldom evaluate aggregate economic welfare at all. This is
because the traditional four-step travel demand modeling process (trip generation, trip
distribution, mode choice, and trip assignment) and its input-output entities as described
22
above are not well suited to measuring these criteria. These measures are encouraged, but
not required by the ISTEA. They are, however, essential to an informed and accountable
planning process. In this study, these measures are connected with the tradition
transportation modeling process by developing an alternative investment framework,
where travel costs calculated using BCA models that employ travel time and distances
from the travel demand model. Furthermore, these measures can also be calculated for
travelers by income class and so can give a vertical equity measure and economic impact
measure, which will be an extension of this study. All these, again, emphasize the
problem addressed in this study. Since few MPOs in the U.S. use these economic and
financial measures, economic impact and social equity are not given much weight in the
evaluation of RTPs.
The next phase of regional transportation planning is the Technical Analysis
Phase, which focuses on travel demand modeling, but may also include a land use model.
One of the case study areas in this study – the Chicago metropolitan region – used a
combination of transportation and land-use modeling. In 2006, the region’s transportation
planning agency Chicago Area Transportation Study (CATS) merged with the region’s
land-use planning agency Northeastern Illinois Planning Commission to form a new
regional planning agency – the Chicago Metropolitan Agency for Planning (CMAP). One
of the major purposes of this merge is to better integrate transportation and land-using
planning at the process and modeling levels. Since one of the objectives of this study is to
incorporate economic and financial measurements to the current transportation modeling
23
process in making investment decisions, a brief summary of the transportation modeling
process in RTPs follows.
First, it is recognized important to include land use model in the
transportation model, not only because the land use model is a key connector between
transportation forecast and economic impacts, but also because the broader transportation
planning perspective requires so. Wegener (1994) suggests that urban models should
recognize all subsystems in terms of the rate of changing: 1). Slow Change Systems
include transportation networks and land uses with permitted economic activities; 2).
Medium-Speed Change Systems include workplaces and housing; 3). Fast Change
Systems include employment and population, meaning the movement of workers and
households among existing buildings; 4). Immediate Change Systems include goods
transport and personal travel. It is widely accepted that changes in the transportation
networks will affect travel and goods movement and, subsequently employment and
population locations, and, finally, the construction of workplaces and housing. This
broader urban modeling perspective is now becoming accepted in transportation planning
and modeling. Hundreds of papers have been published and presented at the
Transportation Research Board annual meetings over the last 20 years on how to improve
travel models.
As for the traditional transportation modeling process, the four-step
transportation model (trip generation, trip distribution, mode split, and traffic assignment)
24
is still the widely-used procedure among MPOs. Before the four-step model is run,
Activity Forecasts are done by the MPOs to gather households and employment data for
the base year and for the forecasted years. For large urban areas, these data are usually
generated by the regional land-use planning agencies based on U.S. census data. Then,
the MPO must distribute the firms and households to planning zones. This process gives
the maximum statistical power to the transportation models.
Trip Generation is the first step of the four-step modeling process. Based on
the household and employment data gathered in the Activities Forecast step, this simple
statistical model – usually developed as cross-classification look-up table or regression
model – projects the number of weekday trips a household will produce. It should be
noted that this process is not sensitive to congestion or transit accessibility. Trip
Distribution is the second step which matches trip origins at households to trip
destinations at firms (by zone1). The trips are distributed across the origin and destination
pairs according to a gravity-type model, in which the number of trips falls off as a
function of trip time. An equilibrated model with fixed trip distance regardless of travel
speed is reached after many repeated iterations, a logical and legally defensible “fullfeedback” model is achieved. In this step, all trips are considered separately, even though
in reality trips are linked in tours, e.g. home, to work, to shop, then to home etc. In the
Mode Choice step, the trips that are in the trip distribution table are allocated to modes
1
After the demographic and employment equations are estimated for each household and employment type, the model
is applied in an aggregate fashion, on categories of households and employment in each zone. The four-step model is
sometimes called an aggregate travel model, due to the zonal averages method of application, but the submodals are
estimated in a disaggregate fashion (Johnson, 2003).
25
with a statistical model that includes trip cost (time, fares, tolls, and parking charges),
household income, household auto ownership, and accessibility. The choice of mode is
then determined by the available travel mode to the household with the lowest travel cost.
The major weakness of this step is that it does not include non-motorized modes, such as
walk and bike. The last step of the four-step model is the Trip Assignment. This step
involves assigning the vehicles to the network of roads and the transit passengers to the
rail and bus lines. Most MPOs use capacity-restrained assignment where a computer
program assigns vehicles to the shortest-distance routes for each origin-destination pair of
zones. The trips are loaded onto the network and the travel speeds are then calculated
from the traffic volumes, related to the capacity of each link. After the four-step model is
run, one then calculates the on-road vehicle emissions. The last phase follows the fourstep modeling phase is the Post-Analysis Phase, which includes plan evaluation, plan
implementation, and monitoring of the results. The process is interlinked to recognize
environments in capacity expansion, changes in land use, and proposed policies.
2.1.3
Opportunities and Challenges
To summarize, the overall quality of RTPs produced by MPOs has improved
over the past several decades, however, critical issues still exist. The regional
transportation process is criticized for its inadequate treatment of the social, economic,
and environmental impacts of transportation facilities and services. The planning process
26
is still not satisfactorily multimodal and does not adequately evaluate a wide range of
alternatives. For example, MPOs almost never identify and evaluate all-transit
alternatives, and the main reason for this is considered to be that transit investments
generally focus on the city centers in a region and it appears to violate the “geographic
equity” objective (in this sense, this objective implies spreading money around rather
than the social welfare of low-income population). In some ways, planning is indeed
politics. In some states such as California, according to Johnson (2003), transportation
funds are legally allocated by population, and so outlying counties get their “share” of
funds.
In addition, planning is focused almost exclusively on long-range time
horizons, ignoring more immediate problems. And, the technical procedures to carry out
planning are criticized for being too cumbersome, time-consuming, and rigid to adapt to
new issues quickly. There is also concern expressed about RTPs’ theoretical validity. The
combination of requirements and regulations is burdensome and counter-productive for
most MPOs, and organizations and techniques seem unable to adapt with sufficient speed.
It is impossible to analyze all of the trade-offs that were required, or, to do any rigorous
benefit-cost analysis.
Specially, current urban transportation planning practice is considerably more
sophisticated, complex, and costly than its highway planning predecessor, and involves a
wider range of participants in the process. However, issues such as narrow objectives, a
27
narrow range of alternatives, inadequate evaluation criteria, and insufficient data
gathering dominate the regional transportation planning debates. The data gathering and
data quality issue are part of the consequence of inadequate MPO budgets for
transportation planning.
For all the other issues mentioned above, the financial and economic analysis
needs could be considered as one of the key solution components. In the Post-Analysis
phase of the transportation modeling process, the plan evaluation should be
comprehensive, that is, it should include all major impact categories including
quantitative analysis. These many measures can be summarized as: economic, equity, and
environmental issues. “With respect to economic measures, MPOs generally evaluate
congestion levels with level-of-service on links and also with personal travel delay for all
travelers” (Johnson 2003). These are described as narrow measures, as they depict only
road congestion and do not include the overall economic utility of the travelers, which
could be derived from most mode choice models. The full set of related costs occurred in
the modeling process should include not only the congestion cost, but also emission cost,
safety cost, system maintenance cost, initial system improvement cost, and consumer
surplus, which are all the entities in calculating the economic utility of the travelers from
a regional economic activity perspective. Therefore, a broader economic measure of
transportation planning and modeling could be developed by formulating a BCA oriented
economic and financial analysis framework that links the four-step model and the
evaluation phase. This framework is sophisticated than the existing RTP evaluation
28
measures in that it includes a larger range of social and environmental costs and benefits
and offers immediate opportunities in assessing regional economic impacts of
transportation planning.
2.2
Asset Management as a New Approach
Economic and financial analysis for transportation planning at a benefit-cost
level or even an analytical framework level could only serve as solution components to
the challenges concluded in the previous section. The total solution has to be a systematic
approach that is able to provide a comprehensive view of the dynamics of the entire
transportation system. Asset Management is considered as such an approach that we have
been looking for. The introduction of asset management in this section builds the
contextual foundation of the transportation investment analytical framework developed in
this study.
Asset management is recognized as an effective supporting tool for
transportation planning at different levels. In urban areas, adequate, properly functioning
infrastructure is the key to economic growth and quality of life. Problems of deferred
maintenance and lack of accountability for infrastructure management persist. At the
same time, “the US highway system continues to deteriorate at a time when economic
growth has spurred public demand for additional highway and bridge capacity” (Dornan
29
2002). In this context, asset management represents a cost-effective way to demonstrate
prudent stewardship of highway infrastructure while satisfying the intent of GASB 34’s
infrastructure reporting requirements. The FHWA and AASHTO define asset
management as “a systematic process of maintaining, upgrading, and operating physical
assets cost-effectively. It combines engineering principles with sound business practices
and economic theory, and provides tools to facilitate a more organized, logical, and
comprehensive approach to decision-making” (FHWA, 2006).
The Asset Management process is represented by the flowchart in Figure 4.
According to FHWA (USDOT, 2001) the goals and policies are influenced by inputs
from both the agencies and customers, which guide how the assets are managed at all
levels of the organization. An inventory of the assets and a means to assess their
condition and model their performance enable the agency to identify investment
requirements for improvement in the short and long term. Next, using a host of analytical
and optimization tools, options or alternatives for addressing the investment requirements
are analyzed and evaluated, where budget and resource allocation constraints are
incorporated into the evaluation criteria. The selected alternatives are a list of projects
that will go into the agency’s short and long range plans. Finally, implementation and
monitoring will follow.
30
Goals and Policies
(Reflect Customer
Input)
Asset Inventory
Condition Assessment
and Performance
Modeling
Alternatives
Evaluation and
Program Optimization
Budget/
Allocations
Short and Long-Range
Plans
(Project Selection)
Program
Implementation
Performance
Monitoring
(Feedback)
Figure 4: Asset Management Framework
Source: U.S. DOT 2001
31
How can asset management systems support regional transportation planning?
As transportation planners and MPOs evaluate the current system conditions and
alternate future scenarios to make informed decisions on allocating resources, they must
balance funding realities with mobility needs; public expectations; and community,
legislative, and environmental considerations. Asset management systems (AMS) serve
as a valuable tool to maximize system performance, improve customer satisfaction, and
minimize life-cycle costs (FHWA, 2006). Basically, the goal of an AMS is to minimize
the life-cycle costs for managing and maintaining transportation assets, which obviously
supports the need for sound financial and economic analysis in the regional transportation
planning process. In addition, AMS aim to integrate economic and engineering practice
for resource allocation and utilization, and through the use of AMS and other tools,
MPOs can gain a more comprehensive view and a bigger picture in transportation
investment decision-making. Asset management at the MPO level needs MPOs to take a
set of actions including defining performance measures for assets through public
involvement, serving as a repository for asset data, and promoting standard data
collection and technology applications. The first action, defining performance measures,
is a critical activity in developing RTPs, and has received a lot of attention.
So what value could an AMS possibly add to the current regional
transportation planning process? Six benefits are identified by FHWA (2006):
32
1. Track system condition, needs, and performance,
2. Clearly identify costs for maintaining and preserving existing assets,
3. Clearly identify public expectations and desires,
4. Directly compare needs to available funding, including operating and
maintenance costs,
5. Define asset conditions so that decisions can be made on how best to
manage and maintain assets,
6. Determine when to undertake action on an asset such as preservation,
rehabilitation, reconstruction, capacity enhancement, or replacement.
Most of these actions that are valuable in terms of the process, but are not
clearly stated in current RTPs. This gap is a major focus of this study. In order to address
financial analysis needs in regional transportation planning process and to provide
package solutions to these needs, AMS are identified and utilized as a new, yet effective,
approach. An investigation of asset management tools and their implementation issues
will be performed and discussed in this study.
2.3
Integrating Asset Management into the Metropolitan Planning Process
On July 18-19, 2006, a peer exchange on Integrating Asset Management into
the Metropolitan Planning Process was organized by the Federal Highway
33
Administration’s (FHWA) Office of Asset Management and Office of Planning in
Traverse City, Michigan (FHWA Peer Exchange, 2006). The goal of the peer exchange
was to bring representatives from state DOTs together with representatives of MPOs to
discuss the use of Asset Management techniques in the metropolitan planning process. In
this peer exchange, several key themes are identified regarding the integration of Asset
Management into metropolitan planning process.
The role of MPOs in Asset Management may vary. MPOs are primarily
involved in capacity expansion and currently do not have much involvement in
preservation and maintenance. Some MPOs do consider preservation and maintenance,
but only at a program level, not including review of specific projects. Those MPOs who
have the most developed Asset Management programs are those who take active roles in
both preserving and maintaining existing assets and planning for new capacity. The three
case study MPOs in this thesis are of this type. It should also be noted that unlike state
DOTs, most MPOs do not have direct ownership of assets and are therefore not involved
in most maintenance decisions. Rather, they work with state and local governments who
have sizable assets to coordinate planning for the overall transportation system. Most
MPOs’ formal Asset Management systems are in early stages of development; however,
MPOs have been involved in related activities and becoming more interested in
implementing Asset Management programs. In terms of future roles of MPOs in Asset
Management, MPOs could choose to implement Asset Management in ways similar to
DOTs, to adopt Asset Management strategies developed on the state level, or to act as
34
Asset Management champion or facilitator, encouraging and supporting the use of Asset
Management among the local governments in their jurisdictions.
The key benefits of implementing Asset Management in the metropolitan
planning process include: 1) allowing investment decisions to be more data driven than in
the past; 2) depoliticizing the allocation process and winning support; 3) using Asset
Management principles to set quantifiable performance targets and goals; 4) getting
measurable results in longtime usage of Asset Management programs. Besides all these
identifiable benefits, however, marketing Asset Management among MPOs can still be
challenge. For example, “Asset Management efforts do not include ‘ribbon-cutting’
ceremonies and therefore often are not as attractive to elected officials as are new capital
projects” (FHWA Peer Exchange, 2006).
Three major challenges in integrating Asset Management into the
metropolitan planning process are also discussed in the peer exchange.
1. Since Asset Management requires significant training of staff, especially in
areas of data collection and analysis, staff turnover is a common problem
among organizations.
2. Choosing what data to collect and how to manage the data is important. The
lack of uniformity among information technology systems is also important,
35
because DOTs and MPOs must coordinate data from a number of local
agencies.
3. The nature of Asset Management programs is to allocate investment choices
among asset classes.
However, the integration of Asset Management with planning for capacity
expansion and safety requires a unified system. These challenges are also challenges in
this study in relation to investment analysis strategies in metropolitan planning process,
and most of these challenges are discussed in Appendix I.
The peer exchange advocates that researchers and professionals in the field
research ways to involve MPOs in Asset Management, and to document and publicize the
benefits of Asset Management. In order to improve the practice of Asset Management
through research, six major next steps are outlined:
1. Refine methods for cross-asset analysis;
2. Integrate new capital and safety projects into asset management;
3. Include non-financial goals in Asset Management programs;
4. Incorporate replacement needs into Asset Management;
5. Refine performance measures;
6. Develop an economic justification for Asset Management
36
This study, specifically, is proposed to contribute to the transportation field in
these aspects.
In short, because Asset Management is still a new concept, there have not
been many papers published in the metropolitan transportation planning field. The
FHWA’s peer exchange is considered as one of the most up-to-date surveys on the
current state in integrating Asset Management into metropolitan planning process. The
peer exchange, defined some of the key themes that the representatives from DOTs and
MPOs identified. This thesis explores some of the research topics outlined in the peer
exchange report.
37
CHAPTER 3
BACKGROUND: ASSET MANAGEMENT APPROACHES AND TOOLS
Three major asset management tools and approaches are introduced and
discussed in this study: Benefit-Cost Analysis (BCA), HERS-ST as a ready-to-use
computer model, and Multi-Criteria Assessment (MCA). These three tools and
approaches are related in the sense that HERS-ST is an implementation of BCA, and
BCA is a specific type of MCA. This study develops a synthesized framework that
produces package solutions takes advantage of the above three approaches and tools.
4.1
BCA as an Essential Element in Asset Management
The ultimate goal of AMS in regional transportation planning is to maximize
system performance and minimize life-cycle costs under the agency goals and polices.
Benefit-Cost Analysis (BCA) is the essential tool in achieving this goal. Simply speaking,
a typical BCA consists of three phases: alternative identification, impact estimation, and
evaluation, as presented in Figure 5 (Lee, 2000).
38
ALTERNATIVES
(Descriptive)
IMPACTS
(Positive)
EVALUATION
(Normative)
BASE
(Alternative)
COSTS
EFFICIENCY
INVESTMENT
(Options)
BENEFITS
EQUITY
SUPPORTING
(Actions)
TRANSFERS
Figure 5: Major Components of Benefit-Cost Analysis
Although the benefit-cost framework has come to dominate other methods of
evaluation in the United States, BCA is by no means supreme, and any kind of technical
evaluation faces an uphill struggle in the decision process. “BCA is strongest at the
federal level, but states and, to a lesser extent, localities accept the appropriateness of
BCA format while not necessarily doing a lot of it” (Lee, 2000). Under ISTEA, localities
have more discretion to choose among different modes and thus have somewhat more
motivation for applying BCA. However, there still exist barriers that are described below.
39
1. Political Limitations
Politically, “the process by which transportation projects are selected in the
US is tied up in intergovernmental relations and financing mechanisms” (Wiener, 1999).
“One drawback of this process is the spawning of demonstration projects that are
earmarked by congressional jurisdiction on a pork-barrel basis” (Lee 2000). In addition,
funding incentives also generate gaps between the BCA and maintenance implementation.
For example, localities are encouraged to substitute capital for maintenance, and for
highways this means that surface treatments are postponed until the improvement can be
treated as capital. “Full life-cycle costs can be readily assessed within a BCA, but such
evaluations are less likely to contribute as much to decisions as they would without the
funding incentives” (Lee 2000). Infrastructure reporting requirements recently
promulgated by the Government Accounting Standards Board (GASB) suggest how
transportation agencies without bond covenants can demonstrate long-term stewardship
of their highway infrastructure (GASB, 1999). Also from the political perspective, MPOs
have difficulty in reaching consensus or addressing regional problems of congestion and
air quality, and the context is not receptive to the discipline required for BCA (Lyons et
al., 1993, van der Wilden et al., 1996).
40
2. Technical Limitations
Technically, it is generally accepted that the typical analytic methodology
that the MPOs have been concentrating on – known as “four-step” models, for trip
generation, trip distribution, traffic assignment, and mode choice – is not well suited to
BCA (Lee, 2000). Although ISTEA made many major contributions in reallocating funds
among different localities, there is no particular requirement to use BCA and the analytic
methods are left open. Instead, more attention has been paid to the technical certification
process, and major MPOs were reviewed with respect to their technical resources and
organizational capabilities for carrying out the intentions of ISTEA (FHWA/FTA, 1994;
Siwek 1995).
3. Inherent Limitations in Transportation Application
In applying BCA to transportation asset management, there are also
inherent limitations of BCA itself. They include: (1) interrelations between transportation
investments and land use (regional economic impacts) are difficult to assess (Still, 1995);
(2) the magnitude of induced traffic is usually uncertain (DeCorla-Souza and Cohen
1999); (3) user responses to road user charges are hard to predict and the benefits and
costs distribution effects are hard to assess (Langmyhr, 1999); (4) BCA is inherently
project-based (however, forms of multi-criteria policy assessments are used in several
41
countries) (Jones, 1994) and the transportation system interdependences are hard to
quantify and assess; (5) attributes and impacts involved in the transportation planning
process are often difficult to quantify or to evaluate in monetary terms; (6) the use of
common assessment techniques may be hampered by institutional barriers, e.g., between
planning agencies responsible for different modes, and different sources of funding for
different modes (Langmyhr, 2001); (7) in applying BCA to transportation networks on
the regional level, the region-wide economic impacts, which is important both politically
and technically, are hard to accurately assess.
Despite all the barriers, BCA is still a valuable decision framework for
governmental agencies to use in considering the desirability of taking alternative actions,
whether investment, operations, or regulations for regional transportation systems.
3.2
HERS-ST as a BCA Tool for Highway Planning
Highway
Economic
Requirement
System
(HERS)
is
a
highway
investment/performance model that considers engineering and economic concepts and
principles in reviewing the impact of alternative highway investment levels and program
structures. HERS was developed to provide guidance to congress in setting budgets for
highway improvements and maintenance. HERS is basically a simulation program which
is used to simulate highway system deterioration and selectively choose investments for
implementation to correct current and projected deficiencies. The simulations are based
42
on analyst-specified constraints such as funding levels and deficiency criteria.
The
HERS-ST is an enhanced version of the national HERS model developed for use by
states. Specifically, the HERS-ST model simulates highway condition and performance
levels and identifies deficiencies through the use of engineering principles. However,
when it simulates the selection of improvements for implementation, it relies on
economic criteria (FHWA, 2002).
Ideally, the asset management framework supports investment decisions and
resource allocation recommendations that span several asset classes. HERS-ST is a
highway investment/performance-modeling tool, and only makes improvement
recommendations for a given highway section independently of the highway network.
Thus for this reason, HERS-ST will not support the multi-asset or multimodal trade-off
analyses required for comprehensive asset management. It can, however, inform such a
framework. Furthermore, “HERS-ST is of value in that its overall approach systematizes
cost-benefit analysis” (FHWA, 2002).
The basic unit of analysis in HERS-ST is a highway section (ranging from
0.1 miles to several miles long). HERS identifies and evaluates the impact of various
treatments and improvements on each section across the jurisdiction being analyzed over
a fixed planning period. Basically, engineering principles are applied in analyzing
relationships among traffic volumes, capacity, pavement deterioration, speeds, crashes,
travel time, curves and grades, emissions, and other highway attributes. The economic
43
impacts of investment are represented in terms of benefits and costs derived from the
models in HERS-ST and associated with different stakeholders as listed in Table 1. The
simplified representation of the HERS/HERS-ST modeling process is shown in Figure 6,
and HERS/HERS-ST internal modeling logic are presented in Figure 7.
Table 1: Benefit and Cost Components in HERS-ST and Associated Stakeholders
Source: FHWA 2002
User Benefits
Agency Costs
General
Public *
BENEFITS
Vehicle operating cost
savings
Safety cost savings
Travel time cost savings
Incremental consumer
surplus
Highway maintenance cost
savings
Residual value
Emissions reduction
X
X
X
X
X
X
X
COSTS
Initial improvement cost
X
* Externalities also include social costs of noise, air and water pollution; loss of wetlands, and
disturbance of historical sites.
44
Assemble highway
data (traffic forecasts,
highway conditions,
etc.)
Forecast future
condition and
performance of
highway sections
Identify deficient
sections
Combine section
improvement costs to
develop needs
estimates
Select section
improvements that
satisfy investment
criteria
Identify economically
justified
improvements for
deficient sections
Figure 6: Simplified Representation of HERS/HERS-ST Modeling Process
Source: FHWA 2002
Travel Forecast
Crash Rates & Costs
(User Safety Costs)
Predicted Pavement
Condition
Maintenance Costs
User Operation Costs
Speed Prediction
Model
User Travel Time
Costs
Capacity Calculation
Emission Costs
Notes: The dashed line reflects the influence of user costs on travel forecasts due to demand
elasticity. The Capacity Calculation is entered only after implementing an improvement.
Figure 7: HERS/HERS-ST Modeling Logic
Source: FHWA 2002
45
Specifically, the HERS-ST model has the following limitations (FHWA,
2002):
z
Only highways are considered explicitly;
z
No interdependencies among highway sections are addressed in the model;
z
New construction on new alignment is not explicitly included;
z
Initial improvement costs include typical capital expenditures, and the cost of
delay associated with implementing improvement options is not considered;
z
The only user charges included are fuel taxes (tolls are excluded).
z
The model is not designed to quantify the uncertainties.
z
Three classes of roads are not analyzed in the model: rural minor collectors,
rural local roads, and urban local roads.
Although HERS-ST is not a complete solution to the challenges proposed in
developing regional plans, it can play a primary role in the transportation decisionmaking. Besides, HERS-ST can also be used as an efficient communication tool for
responding to the general public, especially to the highway users. It will make important
contributions to the regional transportation planning practice in combining HERS-ST
with the region-wide traffic four-step forecast model. The region-wide traffic forecast
model addresses the changes in transportation system in terms of both policy-driven and
infrastructure changes in the network. However, HERS-ST is not sensitive to the land-use
policies and laws, and the region-wide traffic forecast model could complement the
HERS-ST application (FWHA, 2002).
46
3.3
MCA Framework for Transportation Investment Decision-Making
BCA has been widely used to support the decision making process in
transport. However, including non-monetary variables such as noise, accidents, air
pollution into the analysis, has been troublesome for the application of BCA. In addition,
“transportation investment decision-making processes have always involved politicalbalancing of stakeholder demands, equity and availability of resources. These are all
strong dimensions not readily modeled in BCA” (Berechman et al, 2005). Multi-criteria
assessment (MCA) has appeared as an alternative to BCA to deal with these problems.
The two major advantages of building up the MCA transportation investment framework
are that the decision making process is able to incorporate formally other aspects, apart
from the economic ones, and public opinion could be taken into account explicitly in the
decision making process, particularly when information regarding the projects can be
provided by the agency accurately and timely. It must be mentioned that BCA is indeed a
MCA method itself. “The main difference with respect to the proper MCA approaches is
that BCA uses monetary values as the aggregation unit, whereas MCA measure attributes
with different units and then uses a set of weights based upon people’s responses to
aggregate the data. The respondents might be citizens, experts or political actors” (Tudela,
2005).
47
3.4
AHP as an Evaluation Procedure
This section introduces the Analytic Hierarchy Process (AHP). AHP is one of
the several multi-criteria analysis techniques that could be used to assemble solution
packages. The following subsection reviews alternative techniques and the subsequent
subsection describes the AHP technique.
3.4.1
Alternative MCA Techniques
When there are implicit constraints, the MCA will consist of choosing an
alternative among a set of finite and known options. The analysis will be based on the
attributes that describe the options, and the importance of the criteria involved in the
decision process. There are several methods that might be applied to this type of discrete
multi-criteria problems: the Multi-Attribute Utility Theory (MAUT) such as ELECTRE
and PROMETHEE, and outranking methods such as the Analytical Hierarchy Process
(AHP) (Tudela, 2005).
MAUT is a structured methodology designed to handle the trade-offs among
multiple objectives. Utility theory is a systematic approach for quantifying individual’s
preferences, and the MAUT procedure is widely used by public sectors. Outranking
methods consist of a pairwise comparison of alternatives based on the degree to which
48
evaluations of the alternatives and the preference weights confirm or contradict the
pairwise dominance relationship between alternatives. Specific methods such as
ELECTRE and PROMETHEE are widely used in solving environmental and natural
resource problems. Numerous practical applications of the PROMETHEE method have
shown that it is very easily accepted and understood by the practitioners by considering
simultaneously extended criteria and outranking relations. However, the extension is
based on the introduction of a preference function, giving the preference of the decisionmaker for an action “a” with regard to “b”. Sometimes, there are problems in using veto
thresholds and defining preference thresholds, have led to the development of the
PROMETHEE II method. To certain extent, the AHP method provides the opportunity of
taking advantages of both the MAUT and the PROMETHEE methods by combining the
utility measures with pairwise comparisons.
Thus in this research, in order to generate package solutions for regional
transportation investments, the Analytical Hierarchy Process (AHP) is adopted as the
evaluation procedure to the solution. A hierarchy is developed specifically in the regional
transportation planning context. Major input elements (asset management components) in
this hierarchy are outputs of sub-models featuring asset management approaches and
tools introduced before, i.e. BCA and MCA approaches, HERS-ST model, and tools for
other transportation modes. In addition, a survey is conducted to assess major
stakeholders’ utility measures. The survey is presented in Appendix II.
49
3.4.2
The Analytical Hierarchy Process (AHP)
The AHP, developed by Saaty in the late 1970s, is one of the most used
and well-known MCA techniques. The AHP is a mathematical decision making
technique that allows consideration of both qualitative and quantitative aspects of
decisions. It reduces complex decisions to a series of one-on-one comparisons, then
synthesizes the results. Compared to other techniques like ranking or rating techniques,
the AHP uses the human ability to compare single properties of alternatives. It not only
helps decision makers choose the best alternative, but also provides a clear rationale for
the choice. AHP relies on three fundamental assumptions (Wikipedia, 2006):
1. Preferences for different alternatives depend on separate criteria which can be
reasoned about independently and given numerical scores.
2. The score for a given criteria can be calculated from sub-criteria. That is, the
criteria can be arranged in a hierarchy, and the score at each level of the
hierarchy can be calculated as a weighted sum of the lower level scores.
3. At a given level, suitable scores can be calculated from only pairwise
comparisons.
The decision making process is structured as a hierarchy. At the top of the
hierarchy will be the main goal, followed by the criteria and attributes (Saaty, 1990).
50
From these criteria and attributes might be hanging other sub criteria and sub attributes.
The decomposition procedure goes until it reaches the penultimate level of the hierarchy.
At this level will be located the attributes that describe better and in detail the decision
process. The bottom level of the hierarchy contains the discrete scenarios and alternatives
under consideration. “The construction and decomposition of this hierarchy might require
the involvement of experts, decision-makers, and the general public” (Tudela, 2005). A
set of weights which represent the relative importance of the criteria and attributes, sub
criteria and sub attributes is required in proceed with the analysis. Saaty’s original
method to calculate the weights was based on the pairwise comparison matrix. This
comparison gives the relative importance of the elements belonging to the same nest in
the hierarchy. Let C1 , C 2 ,...C n be the set of nest elements, the quantified judgments on
pairs of nest C i , C j are represented by an n-by-n matrix:
A = (ai , j ) (i, j = 1,2,...n)
The entries ai , j are defined by the following entry rules:
Rule 1: If ai , j = α , then a j ,i = 1 , α ≠ 0 .
α
Where α is a relative preference of C i compared with C j .
Rule 2: If Ci is judged to be of equal relative importance as C j , then
ai , j = a j ,i = 1 , in particular, ai ,i = 1 for all i .
Thus, the matrix A has the following form:
51
a11 a12 … a1n
a 21 a 22 … a 2 n
A=
. . . … .
. . . … .
a n1 a n 2 … a nn
(1)
Having recorded the quantified judgments on pairs ( C i , C j ) as numerical
entries ai , j in the matrix A , the problem now is to assign to the n contingencies
C1 , C 2 ,...C n a set of numerical weights w1 , w2 ,...wn that would “reflect the recorded
judgments.” Through interviews with stakeholders or decision-makers, the consensus that
comes out of the interviews corresponds to the calculation of a normalized vector of
weights. There would be a vector of weights for each nest in the hierarchy. The
dimension of the vector would be n, which is the number of elements in the nest (Tudela,
2005). These weights, wi , can be calculated through a normalization process of any of the
columns in matrix A, as shown below:
n
wi =
∑
k =1
ai ,k
n
n
i =1
j =1
∑ ∑a
, ∀i = 1,2,...n
(2)
i, j
52
After all weights for all nests have been estimated, the calculated weights
correspond to the collapsed hierarchy tree using a folding back procedure for every
option under study (Tudela, 2005).
In short, the basic steps of AHP are as follows (Fraser, 2000):
•
Identify the decision to be made, called the goal. Structure the goal, criteria,
and alternatives into a hierarchy. The criteria may be more than one level to
provide additional structure to very complex problems.
•
Perform pairwise comparisons for the alternatives. Pairwise comparison is an
evaluation of the importance or preference of a pair of alternatives.
Comparisons are made for all possible pairs of alternatives with respect to
each criterion. The comparison is done by giving each pair of alternatives a
value according to Table 2. These values are placed in a pairwise comparison
matrix (PCM).
•
Priority weights for the alternatives are calculated by normalizing the
elements of the PCM and averaging the row entries.
53
•
Perform pairwise comparisons for the criteria with respect to the goals and
objectives. Similar to the process in step 2 of comparing the alternatives, all
pairs of criteria are now compared using the AHP value scale. Similar to step
3, a PCM is determined, and priority weights are calculated for each of the
criteria.
•
Alternative priority weights are multiplied by the corresponding criteria
priority weights and summed to give an overall alternative ranking.
Table 2: AHP Value Scale
Value
0.111
0.143
0.2
0.333
1
3
5
7
9
Interpretation
Extreme preference/importance B over A
Very strong preference/importance B over A
Strong preference/importance B over A
Moderate preference/importance B over A
Equal preference/importance A and B
Moderate preference/importance A over B
Strong preference/importance A over B
Very strong preference/importance A over B
Extreme preference/importance A over B
54
3.5
Investment Packages as a Solution Approach
It is common knowledge among transport planners that a system view is
necessary in urban transport policy and in impact assessment analyses. If goals
concerning urban transport policy (e.g., transportation efficiency, sustainability) are to be
achieved, a multitude of measures concerning different travel modes is required. These
include investments in road infrastructure, as well as in public transportation, walking
and cycling. The total effects stemming from a package of several carefully planned
measures may be larger than the sum of the parts. Thus, “goal achievement efficiency
calls for package solutions in most urban areas” (Langmyhr, 2001).
Basically, the term “package solutions” refers to “a variety of transport
policy measures including both investments in different modes and demand
management” (Jones 1991). Any single investment package recognizes changes in
demand, modal shifts, uncertainty, and polices etc. The end format of any single package
solution is a combination of improvement projects and its timing strategies. The “package
approach” is considered as one of the most favorable solution approaches in assessing
system-wide transportation investment strategies. It has many advantages comparing to
the typical transportation planning process, for example, a package approach that
includes investments in several modes (also demand management elements) appears
robust against shifting political preferences. Changes in package profile, e.g., a rise in the
55
percentage of funds earmarked for public transport, can make way for a new political
compromise, while still keeping the major part of the package intact. Furthermore, a
share of the package may be allocated to other sectors if this becomes necessary to secure
a political consensus. In other words, a package approach to transportation planning is
compatible with upholding higher-order goals for urban development, while still follows
planning principles and techniques closely. In the words of Bull and Seale (1994):
“If local authorities are expected to produce ‘packages’ it must follow that
there will be some guiding principles to tie them together. A package without a strategic
aim is merely a random collection of schemes.”
The shift from project level to strategic planning level is one of the most
appealing features of package solutions in applying BCA procedure to regional
transportation planning, which provides us opportunities in filling the financial analysis
gaps in most current RTPs. On the other hand, “the challenge that comes with the
opportunities has also to be noted: such a shift may pose great challenges to the
consensus building capacity (and thus communicative rationality) of the local political
system” (Langmyhr, 2001). These opportunities and challenges bring to this study the
argument that we are not simply involving BCA in the regional transportation planning
process, but we are also trying to agree that “a region needs an adequate and complete
transportation network and needs goals and policies for future development, and in big
investment packages all projects are so interlinked with each other that any single
56
benefit-cost analysis for one part of the package would not give relevant results”
(Ahlstrand 1998).
From the technical perspective of the package solution argument, according
to Langmyhr (2001), two rationalities exist in transportation investment packages:
instrumental rationality and communicative rationality. The instrumental rationality
focuses on goal achievement efficiency, and the planners’ legitimacy rests upon the need
for efficient market production. This planning paradigm plays a dominant role in the field
of transportation planning, which is basically a top-down approach. The concept of
communicative rationality serves to indicate other possible sources of uncertainty in
planning and decision-making. The communicative rationality concept is relevant when
discussing possibilities for consensus building and commitment to common goals, which
is basically a more bottom-up approach. The transportation modeling process done in the
agencies are basically instrumental, and the decisions related to the goals and objectives
as well as the public involvement process are basically communicative in typical regional
transportation planning process. The pros and cons of this two investment package
rationalities are summed up in Table 3. In this study, these two rationalities is one of the
discussion focuses in connecting RTP, AMS, and AMS approaches and tools (BCA,
MCA, HERS-ST etc.).
57
Table 3: Pros and Cons of Transportation Investment Packages Seen from the
Perspectives of Instrumental and Communicative Rationality
(Langmyhr, 2001)
Instrumental Rationality
Pros
Cons
Communicative Rationality
A carefully planned package may be
necessary to significantly improve the
performance of an urban transportation
system.
Interdependencies in demand and supply
between different modes call for package
solutions.
Package planning may encourage search for
solutions among a wide set of alternatives.
This may increase the probability of
choosing the most efficient measures.
There are inherent uncertainties concerning
the dynamics of the transportation system
and its surroundings. Thus, big investments
entail bit risks.
Investment packages are likely to be less
efficient than a set of measures also
including operating subsidies and demand
management
Lacking methods for developing alternative
packages
Packages may facilitate transportation
policy compromises by “fairly” distributing
some benefits to all interesting parties.
Flexible packages may help to sustain a
core of political commitment during periods
of economic fluctuations and shifting
political preferences.
A package approach may be compatible
with a strategic and consensus seeking
urban policy.
A package approach may be utilized to
avoid public scrutiny of controversial
schemes.
Complexity of impact assessment methods
may hinder public participation, and entice
manipulative use of results.
If a package agreement does not open up for
revision, controversies over a single project
may jeopardize the whole package.
In some cases, the two kinds of rationality may seem contradictory; however,
it is important to acknowledge that the two rationality types may complement each other
in “reasonably” democratic and “reasonably” effective procedures. A natural assumption
is that the lack of established methods for impact assessments and the lack of standard
methods for evaluation of large-scale transportation investments may increase the
58
importance of non-instrumental rationality in planning and decision-making. The point is
to aim for an appropriate mix of the two approaches. For example, public involvement in
the policy formulation phase may be followed by feasibility studies relying heavily on
instrumental rationality (Langmyhr, 2001).
The above two rationalities build the theoretical foundation of
transportation investment package solution concept, whose purpose is to find out new
ways for better connecting and integrating the bottom-up and top-down processes in the
regional transportation planning practice. This study will put some weights on the
instrumental side in the regional transportation planning process, and strengthen the
rationale in transportation investment decisions. However, work ultimately contributes to
the goal of balancing the mixture of the two rationalities in regional transportation
investment packages.
59
CHAPTER 4
METHODOLOGY
This chapter develops the methodology used in this research. The section
begins with a summary of data and methods that are used in BCA case studies, followed
by a package solutions hierarchy formulated for assessing investment strategies in
regional transportation planning. In order to clearly demonstrate the analysis process
using AHP procedure, an example of a simple pairwise comparison matrix is also
presented in this section. Finally, the discussion is extended by investigating two
rationalities of a typical package approach in regional transportation planning practices.
4.1
BCA Case Studies
Three BCA case studies are conducted in this study to identify, define, and
describe the gaps in the financial analysis at the regional level, and for the purpose of
comparison. The three selected metropolitan regions are: Northeastern Illinois Region
(Chicago), Delaware Valley Region (Philadelphia), and WILMAPCO Region
(Wilmington). These regions were selected because of familiarity with the areas, the fact
60
that two very different (large metropolitan area and small metropolitan area) and two
very similar (Chicago and Philadelphia) scales are represented, and each region has
different jurisdictional issue.
4.1.1
Data
The Highway Performance and Monitoring System (HPMS) data is the
primary data used in the BCA analysis in this research. This data is collected by FHWA
from state highway agencies. Typically, the States maintain data inventories that are the
repositories of a wide variety of data and the HPMS data items are simply extracted from
these inventories, although some data are collected just to meet FHWA requirements. The
FHWA provides guidelines for data collection, which the States are expected to follow.
The HPMS datasets from each state are updated annually. The comprehensive database
consists of sample sections representing the nation’s highway systems. The sample size is
estimated based on the traffic volume (AADT) within each stratum. In addition to the
universe data for each section, the dataset includes about 50 additional sample data items
for each sample section. The reporting deadline for each state for each year is June 15.
Although FHWA takes several years to assemble the current data, in most cases, all
universe and sample data items are reported for each sample section. HPMS data is a
continuous data collection system, and its data unit is a highway section ranging from 0.1
miles to several miles. The HPMS data supply information regarding the highway
61
Table 4: HPMS Data Items (Source: FHWA)
No.
1
2
3
Data Items
Year of Data
State Code
No.
34
35
Data Items
Number of Through Lanes
Measured Pavement
Roughness (IRI)
Present Serviceability Rating
(PSR)
High Occupancy Vehicle
(HOV)
Electronic Surveillance
Metered Ramps
Variable Message Signs
Highway Advisory Radio
Surveillance Cameras
Incident Detection
Free Cell Phone
On-Call Service Patrol
In-Vehicle Signing
Sample Identifier
No.
67
68
Data Items
Length Class E Curves
Length Class F Curves
69
70
Horizontal Alignment
Adequacy
Type of Terrain
71
72
73
74
75
76
77
78
79
80
Vertical Alignment Adequacy
Length Class A Grades
Length Class B Grades
Length Class C Grades
Length Class D Grades
Length Class E Grades
Length Class F Grades
Percent Passing Sight Distance
Weighted Design Speed
Speed Limit
81
Reporting Units - Metric or
English
County Code
36
Section Identification
Is Standard Sample
Is Donut Sample
State Control Field
Is Section Grouped?
LRS Identification
LRS Beginning Point
LRS Ending Point
Rural/Urban Designation
Urbanized Area Sampling
Technique
Urbanized Area Code
38
39
40
41
42
43
44
45
46
47
NAAQS Nonattainment
Area Code
Functional System Code
49
50
Donut Area Sample
Expansion
Standard Sample Expansion
Factor
Surface/Pavement Type
51
SN or D
84
52
General Climate Zone
85
53
54
Year of Surface Improvement
Lane Width
86
87
Directional Factor
Number of Peak Lanes
22
23
24
Generated Functional
System Code
National Highway System
(NHS)
Planned Unbuilt Facility
Official Interstate Route
Number
Route Signing
Route Signing Qualifier
Signed Route Number
Percent Single Unit Trucks Peak
Percent Single Unit Trucks Average Daily
Percent Combination Trucks Peak
Percent Combination Trucks Average Daily
K-Factor
55
56
57
Access Control
Median Type
Median Width
88
89
90
25
Governmental Ownership
58
Shoulder Type
91
26
Special Systems
59
Shoulder Width - Right
92
27
Type of Facility
60
Shoulder Width - Left
93
28
Designated Truck Route
61
Peak Parking
94
29
30
Toll
Section Length
62
63
Widening Feasibility
Length Class A Curves
95
96
31
Donut Area Sample AADT
Volume Group Identifier
Standard Sample AADT
Volume Group Identifier
AADT
64
Length Class B Curves
97
Left Turning Lanes
Right Turning Lanes
Prevailing Type of
Signalization
Typical Peak Percent Green
Time
Number At-Grade Intersections
- Signals
Number At-Grade Intersections
- Stop Signs
Number At-Grade Intersections
- Other/No Control
Peak Capacity
Volume/Service Flow Ratio
(V/SF)
Future AADT
65
Length Class C Curves
98
Year of Future AADT
66
Length Class D Curves
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
32
33
37
48
62
82
83
system’s current condition and performance. The analytical procedures in HERS-ST rely
on a database of records in the HPMS format. The 98 required data fields from HPMS for
HERS are shown in Table 4. The HPMS data for the regions studied in this research are
provided by FHWA and corresponding DOTs in state sample format.
The most recent 2004 Highway Performance Monitoring System (HPMS)
data from the states of Illinois, Pennsylvania, New Jersey, and Delaware are obtained
from Federal Highway Administration, tailored and modified, and used in this study.
HPMS is intended to be a representative sample of highway segments by road class and
volume group in a state. The summary of the input sample and their representative
universal data is shown in Table 5. Since the standard sample expansion factor calculated
using HPMS software is based on the whole state, the regional expansion factor is recalculated as follows:
ExpansionFactor =
TotalLaneMilesInTheVolumeGroup
SampledLaneMilesInTheVolumeGroup
As shown in Table 5, overall, the expansion errors for the three regions are
4.4%, 1.1%, and 0.5%. Therefore, in terms of input data quality, the expansion factor
adjusted sample HPMS data for the region should provide reliable results.
63
Table 5: Input Data Processing Error - Regional Expansion Factor Calculation
MPO
CATS
WILMAPCO
DVRPC
4.1.2
Sample
Lane Miles
1,232
773
2,286
Universal
Lane Miles
19,668
2,453
16,217
Expanded Universal
Lane Miles
18,797
2,427
16,138
Expansion
Error
4.4%
1.1%
0.5%
Methods
The main purposes of these three BCA case studies are to test the
programmability of applying HERS-ST model to the regional level, identify the financial
and economic gaps in different RTPs, and answer the question whether this application
could be generalized across regions. Furthermore, the analytical results from the case
studies are utilized in the package solutions along with the expert inputs on nonquantitative entities in the research design presented in Appendix I. Incorporating all
these quantitative and qualitative information, a case study on package solutions on
regional transportation investment strategies will be performed for the WILMAPCO
region.
64
HERS-ST is used as the primary analytical tool in identifying RTP
financial gaps. Based on HERS-ST inherent modeling features, these case studies will be
conducted based on six assumptions and four scenarios, defined as follows.
Assumptions
1. Funding constraints: Funding constraints are used according to regional
transportation plans (RTP) recommendations and regional revenue forecast.
2. Analysis period: The analysis period is consistent with the RTP analysis
period.
3. Discount Rate: A 4% discount rate is used in accessing the time value of
money, and all the calculations are presented in 2004 dollars.
4. Network Effects: Due to the model limitations mentioned earlier, each
highway section is evaluated independently from other sections. In other
words, the model analyzes each highway segment individually and does not
look at the highway system as a functionally integrated network.
5. Timing Effects: HERS evaluates the system conditions for each of the four 5year funding period before making any improvement selections. Also due to
65
the model limitations above, the option of implementing an improvement in a
future funding period as an alternative to implementation in the current
funding period is not considered in this study.
6. Project Selection Criteria: When selecting all the candidate improvements, the
less aggressive improvement options are always considered to be more
favorable comparing aggressive improvement (reconstruction and resurface)
options with the same Benefit-Cost Ratio (BCR). Regardless of any constraint,
unacceptable conditions are first identified and corrected before selecting any
BCR-ranked potential improvement under constraints.
Using the Northeastern Illinois case study as an example, according to 2030
RTP, about 72.4% of total $64 billion funding are allocated to system management, and
about $31 billion of the $64 billion are related to highway sector. Based on these numbers,
a total planned highway system maintenance and improvement funding of $22.4 billion is
used in all CATS scenarios as input to the HERS modeling process. The most updated
2030 RTP for the region was released in 2006, and the planning horizon is set from 20042030 which is a 26-year period. In this study, a 20-year planning horizon (2005 – 2025) is
selected in order to generate the best modeling results from HERS. Assuming all the
funds recommended in 2030 RTP are equally distributed across time, four-fifth of the
fund values is considered for all 2030 RTP related scenarios.
66
Scenarios
1. “Do Nothing” Scenario: HERS-ST calculates the benefits of a candidate
relative to a “do nothing” base case, where the highway section being
considered remains unimproved for the duration of the BCA period.
2. Constrained by Funds Scenarios: The base number the constrained funds is
extracted from the published RTPs of the studied regions. This number is then
varied by 100%-200% to generate comparative scenarios. Under these
scenarios, HERS maximizes net present value subject to the funding
constraint.
3. Minimum BCR Scenarios: Accordingly, four such scenarios are developed
with matching total funds with each of the Constrained by Funds Scenarios
above. Under these scenarios, HERS implements all improvements that are
greater than or equal to the user specified BCR, without a limit on available
funding or section performance.
4. Full Engineering Needs Scenario: Without funding or minimum BCR
constraints, this scenario calculates the minimum funding required for each
funding period in order to maintain the pavement condition rates “fair” and
above.
67
Again using the Northeastern Illinois case study as an example, four
constrained by funds scenarios are developed, whose total funding constraints are the
2030 RTP recommended $17.6 billion, $21.1 billion (120%), $26.4 billion (150%), and
$35.2 billion (200%). Since the northeastern Illinois region is highly urbanized, only
total funding constraints are taken into account without differencing urban to rural
funding constraints or funding constraints among highway classes. The minimum BCRs
selected are 1, 3, 4.5 and 5.5, whose modeled total funding ended up with $35 billion,
$25 billion, $20 billion and $17 billion respectively. In this case, three comparable pairs
are created among three constrained by funds and minimum BCR scenarios, and each
pair of the scenarios have similar total amount of investment needed.
Comparative analyses of all scenarios and all three regions are performed,
and conclusions are drawn from the perspectives of system improvement costs, system
conditions, and investment returns.
4.2
MCA Case Study
A MCA case study is developed for one of the three BCA case study regions
– the WILMAPCO region, to illustrate the analytical procedure of incorporating BCA
into regional transportation planning process. The MCA case study is presented in the
form of research design, which can be found in Appendix I and II.
68
4.2.1
Data
Two major data inputs in the MCA case study for WILMAPCO region are
the outputs from WILMAPCO HERS-ST case study and a stakeholder survey conducted
in the summer 2007. Twenty--three out of over 100 variables in the HERS-ST output
were selected as performance measures of the solution packages. Six solution packages
were created based on six HERS-ST generated comparable scenarios and a weighting
system extracted from the stakeholder survey.
4.2.2
Methods
As a case study, a package solutions hierarchy through MCA approach is
developed specifically for regional transportation planning context, as shown in Figure 8.
The financial and economic analytical procedure for the regional highway system has
been performed, which is summarized in this research as an example illustrating the
analysis details. Following AHP logic, the decomposition of the hierarchy illustrated in
Figure 8 is computed by applying HERS-ST outputs, regional transportation planning
goals and policies, RTP recommendations, expert and stakeholder inputs, and the weights
derived from the stakeholder survey.
Generally speaking, a hierarchy should be structured to descend gradually
from the most general and uncontrollable factors in a level to the more concrete and
69
controllable levels below. As shown in Figure 8, the overall focus is defined first in order
to identify the stakeholders in the decision problem and their relevant objectives and to
generate creative alternative courses of action. Whenever a number of experts are
involved in the process, the discipline imposed by the requirement of structuring the
problem hierarchically can help to achieve consensus over the dimensions of the problem
(Saaty, 1995). This major feature of AHP helps us in analytically merging the BCA
approach with the regional transportation planning context.
70
Efficiently Functioning Regional Highway Systems
(e.g. 3 goals stated in WILMAPCO’s 2030 RTP)
Overall Focus
Stakeholder
Objective
Users
Mobility and
Accessibility
General Public
Transportation Agencies
Regional
Economic
Social Equity
Natural
Environment
Benefit – Cost Analysis*
Courses of Actions and Investment Strategies
RTP
Concerns
Public Health
and Safety
HERS-ST
RTP
Gaps
* As shown in Figure 9 and Figure 10.
Figure 8: Hierarchy of Multi-Criteria Package Solutions to the Regional Transportation Investment Strategies
71
In this hierarchy, for the road system specifically, the HERS-ST would
perform the BCA analysis process and generate courses of actions on the bottom two
levels. Based on the different scenarios selected in the HERS-ST program, we are able to
rank and select all the qualified projects based on benefit-to-cost ratio (BCR) under
funding constraint, performance constraint, or without any constraint. The end products
of this process are a series of BCA filtered candidate improvement projects under
different scenarios, and they will become the “courses of actions” inputs joining in the
hierarchy to produce solutions packages. Figure 9 and Figure 10 illustrate all the benefits
and costs factors considered in HERS-ST program, and these hierarchies will be solved
by the program. After the pairwise comparison matrix computation process with the
upper three levels, the final recommended scenario with its courses of actions will turn
into a series of investment strategies over the planning horizon.
Costs of Highway System Renewal
Improvement Investments
Maintenances Costs
Figure 9: The Cost Hierarchy
72
Benefits of Highway System Renewal
Operation
Cost
Reduction
Travel
Time
Saving
Incremental
consumer
surplus
Safety
Cost
Saving
Emission
Reduction
Regional
Economic
Impacts*
* The regional economic impacts analysis will be an external analysis to HERS-ST
Figure 10: The Benefit Hierarchy
Also in this hierarchy, the final investment strategies are determined by the
HERS-ST outputs and the objectives of the regional transportation plans in the third level
whose priorities are generated with respect to three different groups of stakeholders. The
priorities of the stakeholders are determined in terms of the first level single element goal
of focus. The following is an illustration of this process with a simple pairwise
comparison matrix. From the perspective of a stakeholder such as highway users in the
second level compare the four objectives in the third level with respect to it. Hypothetical
results are as shown in Table 6.
73
Table 6: Example of Simple Paired Comparison Matrix
(1) Mobility and Accessibility
(2) Regional Economic Impacts
(3) Social Equity
(4) Natural Environment
(5) Public Health and Safety
(1)
1
1/2
1/3
1/3
1/4
(2)
(3)
2
3
1
2
1/2
1
1/2.5
1
1/2.5 1/1.67
(4)
3
2.5
1
1
1/2
(5)
4
2.5
1.67
2
1
Priorities
0.388
0.254
0.134
0.141
0.082
This matrix is reciprocal so that ai , j = 1 / ai , j . It is inconsistent in that
ai , j a j ,k ≠ ai ,k which says that the entire matrix cannot be constructed from a single row
or from a spanning tree. When we compare an element on the left with itself represented
at the top of the matrix we enter the value 1 from the scale of Table 6. We have, for
example, the Mobility and Accessibility dominates Regional Economic Impacts by 2
times, and dominates Social Equity for a moderate preference/importance of 3 times. The
reciprocals are entered in the transpose positions. The priorities are obtained by solving
the equation (2) in Chapter 3. So as we can see here, for example, that with respect to the
highway users, Mobility and Accessibility is considered as the most important objective
among the five objectives defined in the hierarchy. Social Equity and Natural
Environment are considered almost equally important to highway users but about ½ less
important than Regional Economic Impacts to them.
In this study, we develop numerical entries for each of the hierarchy cells
and do similar pairwise comparison matrixes for the hierarchy shown in Figure 8. The
numerical entries are extracted from the HERS-ST outputs, and the relative weights are
74
obtained through a stakeholder survey. Due to the size of the hierarchy proposed in this
study, instead of doing all the pairwise comparisons manually as presented in the sample
matrix, the AHP analyzing process in this study is mainly performed by the computer
program Expert Choice.
Expert Choice was originally developed in 1983, and the program aims to
generate decision solutions based on AHP. In the 1990’s Expert Choice expanded its
presence in the U.S. Federal Government arena by helping officials at the Veterans
Affairs and Social Security Administrations, the Department of Defense and other
Federal agencies with strategic planning, source selection and resource allocation projects.
At present, the U.S. Federal Government uses Expert Choice to allocate more than $120
billion per year in resources (Expert Choice Inc., 2007). Thus basically, the AHP
computer software Expert Choice has demonstrated that it can provide reliable outputs
following AHP procedures, to generate outstanding sensitivity analysis, and to customize
reports. The major contribution of Expert Choice is basically the combination of the PC
computing power and the AHP analytical power, which this research also takes
advantage of.
75
CHAPTER 5
BCA CASE STUDIES USING HERS-ST
In this chapter, three BCA case studies are presented and the final
conclusions are discussed with respect to the research objectives of this thesis.
5.1
Overview of the Case Study Regions
Three BCA case studies are conducted in this paper for the purpose of
comparison. The three selected metropolitan regions are: Northeastern Illinois Region
(Chicago), Delaware Valley Region (Philadelphia), and WILMAPCO Region
(Wilmington). The main purposes of these three BCA case studies are to test the
programmability of applying HERS-ST model to the regional level, identify the financial
and economic gaps in different RTPs, and answer the question whether this application
can be generalized across regions.
76
5.1.1
Northeastern Illinois Region
In 2000, the six-county (Cook, DuPage, McHenry, Kane, Will, and Lake)
Northeastern Illinois (Chicago) Region had a total population of more than 8 million, and
the population in the city of Chicago was a little less than 3 million. It is projected that by
the year 2030 the region’s total population will be over 10 million (NIPC, 2006). This
large metropolitan region is known for its political fragmentation and central
city/suburban antagonism. “With more than 940 local governments with taxing authority,
it has the most local governments of any metropolitan area in the nation” (Hamilton,
2002).
From the 1960s to mid-1970s, Illinois invested aggressively to complete
its interstate highway system, thereby enhancing the state’s ability to take advantage of
its strategic geographic location. Although Illinois can still count upon its highway
network to contribute to its competitive advantage, efforts to maintain and improve it
must be intensified if this advantage is to be maintained. As in many states across the
nation, both the health and the future growth of the state’s economy require a quality
highway system. Consistent with the legislative objective, the initial effort of the state
will be built upon in the future years as better information about the performance of the
highway system becomes available and more sophisticated analysis tools are developed.
This system-wide planning process largely depends on a cooperative process with the
77
state’s MPOs, the IDOT districts, and other sister state agencies that share responsibilities
for the state’s continued economic growth.
Developing the 2030 RTP was a two-year project which was completed in
autumn 2003. The plan is updated every three years thereafter, with a recent update
occurring in 2006. The scope of plan is to include all surface transportation modes, but
the recommended projects are limited to highway and passenger rail infrastructure. It was
emphasized in the plan that RTP is not a financial plan, nor is the 2030 RTP a financial
planning exercise. “The plan is intended to highlight inadequate financial resources but
not dwell on specific strategies for remedying financial shortfalls” (2030 RTP, 2004).
The CATS’s 2030 RTP provides detailed projects and broad funding allocation
recommendations.
The 2030 RTP is oriented around concept scenarios. As emphasized in its
goals, the 2030 RTP places “the highest priority on maintaining transportation system
integrity by giving careful consideration to reconstruction and replacement decisions as
well as maximizing its efficiency through effective transportation management and
operations.” (CATS, 2003, p.23) In addition, the 2030 RTP also has a “Maintenance
Theme”.
This theme stands out from the plan’s goals and objectives because the
Transportation Equity Act for the 21st Century (TEA-21) planning factors require that the
planning process emphasize the efficient preservation of the existing transportation
system. The 2020 RTP recognizes the maintenance, rehabilitation and preservation needs
78
of our existing system as its overarching goal and consequently over 80 percent of the
projected resources through 2020 have been allocated for the capital maintenance of the
existing system (CATS, 2003). Thus in the 2030 plan, it is further emphasized in the
“Maintenance Theme” that the capital maintenance projects protect the safety and
efficiency of the system and extend the useful life of existing facilities.
5.1.2
WILMAPCO Region
The Wilmington Area Planning Council (WILMAPCO) is the regional
transportation planning agency for the Cecil County and New Castle County area, known
as the Wilmington Region. As the federally designated metropolitan planning
organization (MPO), WILMAPCO is charged with planning and coordinating the many
transportation investments proposed for this region. The Wilmington Metropolitan
Region has a total area of 744 square miles and a 2005 population of 620,804. New
Castle County is an urbanized county with a density of 1,229 persons per square mile
while Cecil County is largely rural, with 282 persons per square mile. Comparing to the
other two large studied regions, the Wilmington region is considered as a medium size
metropolitan region.
The first RTP for the Wilmington Region was accomplished ten years ago in
1996, in which the vision of the region’s transportation system was first established.
79
Updates, now occurring every four years, and “the 1996 plan and subsequent updates
serve as a living document, a tool for making informed transportation investment and
policy decisions”(WILMAPCO, 2030 RTP). Thus, the most current 2030 RTP (updated
in March, 2007) is not considered a “new” plan. Rather, according to WILMAPCO, this
plan builds upon the projects, policies and plans developed over the last ten years to
identify how they should continue their work during the next 20 years.
Specific challenges are also addressed by WILMAPCO. For example, from
1996-2006, the region’s population increased by 9.7%, and the Vehicle Miles Traveled
(VMT) increased by 14.1%. The 1996 Plan had an ambitious goal of shifting 10% of
drive alone trips to other modes. However, since that time more commuter trips are made
by driving alone in both counties (increasing from 77% to 81%). Across the region, the
recent data from the 2005 American Community Survey shows the number of transit
commuters in the region was below 1990 numbers (decreasing from 3% to 2%).
From an asset management perspective, in facing of these challenges, three
goals were identified in the 2030 RTP. Each goal is clarified through more specific
objectives, which in turn are carried out through specific action. These actions are a mix
of studies to be completed through WILMAPCO’s Unified Planning Work Program
(UPWP). Among all these goals and objectives, the system preservation and performance
is included and highlighted. For example, under the “Maintenance First” action, it is
emphasized that the “Maintenance First” policy places preservation, repair, and
80
restoration needs ahead of expansion needs. Limited resources should first and foremost
keep our transportation system safe, convenient, and economical. In addition,
WILMAPCO also developed several sets of clearly defined performance measures (a
stand-out feature in comparison to the other two studied RTPs) that WILMAPCO used in
the document and their respective performance targets/national averages (WILMAPCO
2030 RTP, p71-72).
Also compared to the other two study regions, overall, the Wilmington
region has a relatively newer infrastructure system, but some parts of the system are also
considerably old. It is recognized by WILMAPCO that as the size of transportation
system grows, more infrastructure has to be repaired or replaced over time. However, to
keep pace with required maintenance, the amount of transportation funds that can be used
to support new projects and services is limited. Fiscal realities in the WILMAPCO region,
particularly New Castle County, have had a dramatic impact on determining what is
financially reasonable. Simply put, not enough revenue will be available to build all
projects identified as desirable for our growing region. Therefore, the WILMAPCO
developed two financial analysis scenarios called “Financially Constrained” and
“Aspirations List”, which represent a list of projects with expected future revenue, and a
list of projects that are desired but with no secure funding available at this time,
respectively.
81
5.1.3
Delaware Valley Region
The Delaware Valley Region (Philadelphia) has an area of 3,814 square
miles, which contains 5 Pennsylvania counties and 4 New Jersey counties. The total
population in the nine-county area is about 5.4 million in 2000, an increase of 4 % since
1990. By 2030, the population is forecasted to grow by nearly 13% and reach 6 million.
The Delaware Valley Regional Planning Commission (DVRPC) has been the principle
agency charged with a mission to plan for the orderly growth of the Delaware Valley
Region for almost 40 years. Unlike CATS and WILMAPCO, the DVRPC is responsible
for the region’s transportation and land use planning. DVRPC is updating and extending
the adopted Horizons 2025 Regional Land Use and Transportation Plan (June 2002) to
the Year 2030. The three-year update and planning process (required for regions that are
in non-attainment status for ozone pollution) is entitled as Destination 2030.
It is emphasized in their plan Destination 2030 that the Delaware Valley
has a mature transportation system and the focus can no longer be on building new
highways but on making the roads they have perform better. In the transportation
planning part of their 2030 Destination (as opposed to the land use part), as listed below,
they identify one vision, six goals, and a series of policies that are organized around the
goals for “Transportation Facilities” theme; One vision and six goals for “Transportation
Operations” theme; and one vision and three goals for “Transportation Finance” theme.
82
Theme1: Transportation Facilities
Vision: A safe, convenient and seamless multimodal passenger and freight
system that is sufficient in its capacity attractive and affordable to its users, accessible
and equitable for all citizens and visitors to locations throughout the region; and
incorporating sound growth management, urban revitalization, environmental and
economic development planning principles.
Goals and Policies:
•
Improving Safety
•
Reducing Congestion
•
Improving Mobility
•
Enhancing the Environment
•
Rebuilding the Infrastructure
•
Linking Transportation Investments to Land Use and Economic
Development Goals
Theme 2: Transportation Operations
Vision:
A
well-planned,
reliable
and
safe
multi-modal,
regional
transportation system that promotes interconnectivity among systems, keeps operators
and users informed about travel conditions, responds rapidly to incident related
congestion and assures efficient delivery of goods and passengers utilizing available and
new technologies.
83
Goals and Policies:
•
Implement one regional incident management program to coordinate
with individual-incident management corridor programs,
•
Study airport growth with respect to demand, and expand capacity
where necessary,
•
Institute a central data clearinghouse for all regional transportation
operations, whether virtual via the internet or at a physical location,
where travel information is shared freely among agencies,
•
Utilize ITS technology to provide information about travel conditions
to travelers and operators,
•
Dedicate funding for operation centers into the region’s transportation
improvement programs,
•
Implement an integrated fare collection mechanism for transit
throughout the region.
Theme 3: Transportation Finance
Vision: Each mode of transportation has adequate funding to maintain,
modernize and operate its infrastructure. Money is available to provide needed
expansions within corridors designated for growth and reinvestment in existing centers.
Funding can be used to facilitate the movement of people, vehicles and goods and to
enhance important intermodal connections. A combination of user fees, tolls, regional
84
and state taxes, and other creative financing mechanisms, including public-private
partnerships, are in place.
Goals and Policies:
ƒ
Establish a funding mechanism for financing projects of regional
significance, including enactment of state enabling legislation to
permit dedicated regional revenue generation,
ƒ
Maximize the amount of state and federal transportation resources
that flow to this region, consistent with statewide mobility needs
and cognizant of the added costs associated with construction in
dense, older urban areas,
ƒ
Select projects for capital programming in the TIP based on sound
long range strategic planning considerations, life-cycle investment
analyses, and system performance and condition data (actual and
projected).
Among these visions and goals, there are several questions that the plan asks
in front of all the opportunities and challenges the region is facing, including (DVRPC,
Destination 2030):
•
How best to increase the capacity of their highway system?
•
Do they need to build new highways, and if so, where?
85
•
What facilities should be widened?
•
Which new technologies will they use to improve their existing network for
the 21st century?
•
Will public transit providers be able to significantly increase their ridership or
will the highway system have to accommodate an even larger share of
region’s travel?
•
Which portions of their existing network are adequate to meet the future
demands?
The challenge is clear that the region has much to do with limited funds.
Where they invest their transportation funds will go a long way in determining how their
future landscape looks. As a conclusion, Destination 2030 recognizes that the future of
the region will largely depend on how the highway system, particularly the arterial
network, is maintained or improved, in the face of ever increasing demand for travel
(DVRPC, Destination 2030). From this point of view, the Delaware Valley region and the
Northeastern Illinois region share a similar context in terms of aging transportation
infrastructure systems.
Similar to the other two regions, Delaware Valley region also identifies
their future expenditure level based their forecasted revenue level, and the candidate
projects selection process involves a certain set of evaluation criteria that measures
attainment of the transportation goals of the RTP. Destination 2030 defines three time
86
periods in the Plan, and each plan project has a corresponding estimated completion year
within one of these periods. Identified financial resources are distributed over the life of
the plan to ensure fiscal responsibility; and subtotals – by sub-region, by mode and by
time frame – function to control total expenditure to encourage a fiscally constrained
project set.
A detailed summary of the three regions studied can be found in Table 7,
including the regions’ physical features, key RTP elements, and the projected
transportation expenditures and revenues presented in the RTPs.
Table 7: Characteristics of the Three Case Study Regions
MPO
Size
State(s)
# of Staff
Population
Area (sq. mi)
CATS
WILMAPCO
Large
IL
95*
8,
150,000
3,749
DVRPC
Medium
DE, MD
9
629,804
Large
PA, NJ
60
5,400,000
3,814
Constrained
Scenario
744
Aspiration
Scenario
2030 Projected Investments ($m)
% of M &I **
Highway Related Investments ($m)
$64,000
72.4%
$32,000
$1,318
90.0%
$1,186
$3,358
90.0%
$3,022
$57,300
86.6%
$29,300
Highway M &I Investments ($m)
$23,500
$1,068
$2,720
$26,400
Studied Period (20-year) Highway M & I
Investments ($m)
$18,800
$1,060
$2,700
$21,100
*
**
The total staff number of Chicago Metropolitan Agency for Planning (CMAP), a merged MPO
CATS and NIPC (Northeastern Illinois Planning Commission).
Maintenance and Improvement Investments
87
from former
5.2
Case Study Findings
Using the HPMS data, HERS-ST was run for the three case study regions.
The outputs from HERS-ST are analyzed in terms of the system improvement costs, the
system conditions and the investment returns. This section presents the results of the
BCA for the various scenarios in terms of the system improvement costs, system
conditions, investment returns, and investment structure by investment type.
5.2.1
System Improvement Costs
The overall system improvement investments for all scenarios are shown in
Table 8. The bold bordered scenarios are comparable pairs with the similar total funding
projections but different modeling approaches. The “Funding Constrained” scenarios are
developed according to the 2030 RTP recommendations, and the corresponding
minimum BCR approach is developed specially for matching the total funds but through
BCR oriented screening processes. One more similar pair is developed for each MPO by
varying the 2030 RTP recommendations by 150% to 250%, and the minimum BCR from
1 to 4 for matching total funds. Specifically, all minimum BCR scenarios implement all
improvement projects that meet the user defined minimum BCR in any given FP without
a limitation on available funds; while all funding constrained scenarios are assumed to
allocate total funds evenly across four FPs. The exception here is the WILMAPCO. Since
the funding allocation schemes (mainly based on revenue forecast) are presented in
88
WILMAPCO’s 2030 RTP, their funding allocation amount for each FP is calculated
based on these schemes and further run in HERS-ST.
Table 8 indicates that when more funding become available, the urban
highways should be gaining more share of the total funds in maximizing total benefits,
and this trend is even obvious for medium sized metropolitan areas such as WILMAPCO
region where the proportion of rural area is significant. Also, except for the CMAP region,
the 2030 funding projections for the other two regions suggested by HERS-ST are not
adequate to maintain their highway system under the current condition in the long-run,
and the 20-year deficits for WILMAPCO (constrained scenario) and DVRPC are $0.8
billion and $ 7.2 billion respectively.
89
Table 8: Investment Statistics for All Scenarios ($m)
MPO
Rural
Overall
Initial Costs
Urban
Overall
Initial Costs
$603
$16,285
4%
$700
4%
$643
4%
96%
$17,770
96%
$16,591
96%
Funding Constrained
(150%)
$727
$27,319
$28,046
Minimum BCR=1
3%
$687
3%
97%
$24,855
97%
$25,542
$1,003
2%
$41,477
98%
$42,480
$164
14%
$152
13%
$1,012
86%
$975
87%
$1,176
Maintain Current
Condition
$224
$1,751
11%
89%
Minimum BCR=1
$295
12%
$287
10%
$2,138
88%
$2,679
90%
$2,434
Full Engineering Needs
$363
5%
$6,335
95%
$6,699
Funding Constrained
$715
3%
$723
3%
$19,997
97%
$21,963
97%
$20,713
$689
$27,278
Scenarios
Maintain Current
Condition
Funding Constrained
Minimum BCR=3
CATS
Full Engineering Needs
WILMAPCO Constrained
Minimum BCR=4
WILMAP
CO
WILMAPCO Aspiration
Minimum BCR=4
Maintain Current
Condition
DVRPC
Total
Initial
Costs
$16,888
$18,470
$17,235
$1,127
$1,975
$2,967
$22,686
$27,968
2%
98%
Funding Constrained
(250%)
$1,287
$49,986
$51,273
Minimum BCR=1
3%
$1,296
2%
97%
$51,555
98%
$52,852
$1,421
1%
$95,080
99%
Full Engineering Needs
90
$96,502
5.2.2
System Conditions
If no investments were made over the next 20 years, Figure 11 indicates that
at the end of the year 2025, the percentage of highway sections that will need
reconstruction for CATS, WILMAPCO, and DVRPC regions are 26%, 31% and 33%.
Technically, a reconstruction is needed when (1) Pavement Serviceability Rating (PSR)
at the beginning of the funding period is less than the threshold PSR for reconstruction
(below 1.5-2.3 for different highway classes); (2) surface type is low and deficient, and a
widening option is identified; or (3) surface type is unpaved and surface type is deficient
or a widening option is identified (FHWA, 2006). Currently, the initial system condition
for the northeastern Illinois region is that 7.76% of the total highway sections need
reconstruction, and for the Wilmington and Delaware Valley regions these numbers are
3.2% and 6.6% respectively.
91
Deficiencies - Reconstruction Level as % of
Mileage
35%
30%
25%
CATS Do Nothing
20%
15%
WILMAPCO Do
Nothing
10%
DVRPC Do
Nothing
5%
0%
Initial
FP1
FP2
FP3
FP4
Funding Periods (2005-2025)
Figure 11: System Deficiencies for All Do Nothing Scenarios
Then what are the system conditions that HERS scenarios project? Figure 12
through Figure 14 show the reconstruction levels as percentages of mileage for the all the
scenarios by region and by pairwise comparison. As we can see, for any pair of scenarios
with the similar total funds, the minimum BCR approaches gain better overall system
conditions over the funding constrained approaches. The exception is WILMAPCO’s
aspiration scenario and their minimum BCR=1 scenario. The reason for the aspiration
scenario winning out in this comparison is that the highest total funding HERS-ST
models under minimum BCR approach for the WILMAPCO case is $2.4 billion
(minimum BCR=1), which is still $0.5 billion less than the aspiration scenario (22%).
92
Therefore, it is not surprising that we see better performance of WILMAPCO’s aspiration
scenario over its counterpart. On the other hand, this also gives us the important
information that not all the projects on WILMAPCO’s aspiration list will give us positive
Deficiencies - Reconstruction Level as % of Millage
total net benefits.
9%
CATS
8%
Funding
Constrained
($18.8b)
Minimum BCR=3
($17.23b)
7%
6%
5%
4%
Funding
Constrained
($28.2b)
Minimum
BCR=1($25.5B)
3%
2%
1%
0%
Initial
FP1
FP2
FP3
FP4
Funding Periods (2005-2025)
Figure 12: System Deficiencies for All CATS Scenarios
93
Deficiencies - Reconstruction Level as % of Mileage
8.00%
7.00%
WILMAPCO
6.00%
WILMAPCO
Constrained ($1.8b)
5.00%
Minimum BCR=4
($1.13b)
4.00%
3.00%
WILMAPCO Aspiration
($2.97b)
2.00%
Minimum BCR=1
($2.43b)
1.00%
0.00%
Initial
FP1
FP2
FP3
FP4
Funding Periods (2005-2025)
Deficiencies - Reconstruction Level as % of Mileage
Figure 13: System Deficiencies for All WILMAPCO Scenarios
20%
DVRPC
18%
Funding
Constrained
($20.7b)
16%
14%
12%
Minimum
BCR=4($26.7b)
10%
8%
Funding
Constrained
($51.3b)
6%
4%
Minimum
BCR=1($52.9b)
2%
0%
Initial
FP1
FP2
FP3
FP4
Funding Periods (2005-2025)
Figure 14: System Deficiencies of All DVRPC Scenarios
94
5.2.3
Investment Returns
The total benefits for each pair of scenarios can be found in Table 9, where
these benefits are presented overtime with the overall 20-year average BCR listed at the
last column. As the numbers indicate, the total benefits for all these scenarios are not
significantly different between each comparable pair. Figure 15 though Figure 17 show
investment returns on maintenance cost savings, user benefits, and pollution damage cost
savings for all scenarios by pair comparison. For the WILMAPCO region and CATS
region, the “Minimum BCR” scenarios get better overall investment returns on both
maintenance cost savings and user benefits. However, the “Constrained by Funds”
scenarios obtain better overall investment performances on the pollution damage cost
savings. Interestingly, for the DVRPC case, unlike the other two regions, the funding
constrained scenarios get better investment returns on user benefits over the minimum
BCR scenarios for both pairs. This phenomenon actually indicates that either the average
BCR for all the candidate projects (with minimum BCR=1) under the initial condition is
not significantly high, or there are a significant number of mandatory projects identified
during the planning horizon. In other words, according to HERS standards, the candidate
projects (with a minimum BCR=1) in current DVRPC highway system on the average are
not as beneficial as those in current WILMAPCO and CATS highway systems, and many
of them are mandatory projects in order to keep the system above the cut-off threshold
which have relatively lower investment returns (BCR).
95
Table 9: Investment Returns on Total Benefits for All Scenarios by Pair
Comparison
FP1
FP2
FP3
FP4
Overall
Total
Benefits
/ Total
Costs
CATS Constrained
CATS Minimum BCR=3
$68,099
$52,248
$15,060
$24,803
$14,480
$22,641
$15,474
$19,811
$113,113
$119,503
6.12
6.93
CATS Constrained (150%)
CATS Minimum BCR=1
$63,126
$80,722
$26,714
$22,385
$20,654
$9,943
$28,780
$17,186
$142,193
$122,602
5.07
4.80
WILMAPCO Constrained
WILMAPCO Minimum BCR=4
$5,186
$7,604
$3,417
$1,116
$1,896
$1,192
$145
$928
$10,645
$10,840
9.06
9.62
WILMAPCO Aspiration
WILMAPCO Minimum BCR=1
$7,758
$8,807
$2,788
$1,510
$1,611
$1,326
$391
$932
$12,547
$12,574
4.23
5.17
DVRPC Constrained
DVRPC Minimum BCR=4
$44,078
$47,069
$35,390
$38,285
$36,085
$28,868
$32,378
$37,568
$147,932
$151,791
7.14
6.69
DVRPC Constrained (250%)
$65,918
$38,908
$31,173
$33,394
$169,392
3.30
DVRPC Minimum BCR=1
$77,022
$25,575
$26,587
$30,136
$159,321
3.02
Scenarios
96
97
DVRPC
Minimum
BCR=1($52.9b)
DVRPC Funding
Constrained
($51.3b)
DVRPC
Minimum
BCR=4($26.7b)
DVRPC Funding
Constrained
($20.7b)
WILMAPCO
Minimum
BCR=1 ($2.43b)
WILMAPCO
Aspiration
($2.97b)
WILMAPCO
Minimum
BCR=4 ($1.13b)
WILMAPCO
Constrained
($1.8b)
CATS Minimum
BCR=1($25.5B)
CATS Funding
Constrained
($28.2b)
CATS Minimum
BCR=3
($17.23b)
CATS Funding
Constrained
($18.8b)
Maintenance Cost Savings to Total Costs
Ratio
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Scenarios
Figure 15: Investment Returns on Maintenance Cost Savings for All Scenarios by Pair Comparison
98
Scenarios
Figure 16: Investment Returns on User Benefits for all Scenarios by Pair Comparison
DVRPC
Minimum
BCR=1($52.9b)
DVRPC Funding
Constrained
($51.3b)
DVRPC
Minimum
BCR=4($26.7b)
DVRPC Funding
Constrained
($20.7b)
WILMAPCO
Minimum
BCR=1 ($2.43b)
WILMAPCO
Aspiration
($2.97b)
WILMAPCO
Minimum
BCR=4 ($1.13b)
WILMAPCO
Constrained
($1.8b)
CATS Minimum
BCR=1($25.5B)
CATS Funding
Constrained
($28.2b)
CATS Minimum
BCR=3
($17.23b)
CATS Funding
Constrained
($18.8b)
User Benefits to Total Costs Ratio
7
6
5
4
3
2
1
0
Pollution Damage Savings to Total
Costs Ratio
99
DVRPC
Minimum
BCR=1($52.9b)
DVRPC Funding
Constrained
($51.3b)
DVRPC
Minimum
BCR=4($26.7b)
DVRPC Funding
Constrained
($20.7b)
WILMAPCO
Minimum
BCR=1 ($2.43b)
WILMAPCO
Aspiration
($2.97b)
WILMAPCO
Minimum
BCR=4 ($1.13b)
WILMAPCO
Constrained
($1.8b)
CATS Minimum
BCR=1($25.5B)
CATS Funding
Constrained
($28.2b)
CATS Minimum
BCR=3
($17.23b)
CATS Funding
Constrained
($18.8b)
0.00
-0.02
-0.04
-0.06
-0.08
-0.10
-0.12
-0.14
Scenarios
Figure 17: Investment Returns on Pollution Damage Savings for all Scenarios by Pair Comparison
5.2.4
Investment Structure by Investment Type
Since the HPMS data are not a 100% sample data, we are not able to extract
improvement statistics on the project level and compare the recommendations through
network BCA approach with the 2030 RTP recommended project list for any of the three
regions. However, with the data limitations, the analysis can still be done at the project
group level, e.g. investment dynamics for different improvement type mixtures. Some of
this work could be used as guidelines in dividing total investments by project type when
different amount of revenue streams differ from those that are projected to be available
over the planning horizon.
5.3
HERS-ST Case Studies: Observation and Conclusion
Based
on
the
2030
RTP
highway
investment
recommendations,
underinvested is occurring in the WILMAPCO and DVRPC regions’ highway systems
and current system conditions may not be maintained. However, the current investment
recommendations for CATS, WILMAPCO, and DVRPC are able to maintain the current
reconstruction level around 6%, 7%, and 17% respectively in the next 20 years, if the
available revenues for each 5-year period are supposed to be constant (or to meet the
WILMAPCO’s estimated on funding stream).
100
In order to make fully informed investment decisions, Figure 18 to Figure 20
illustrate the further insights of the comparative case study results. In these Figures, the
plot areas are set up using total investments as the y-axis and the system deficiencies as
x-axis. The plot function could be presented as:
y=
f ( h)
x
Where, h is a HERS internal programming parameter. The above equation
means that y is inversely related (inversely proportional when f (h ) turns out to be a
constant) to x. In Figure 18 to Figure 20, a straight line is developed for each figure, and
its slope is defined based on the decision-maker’s preference on the trade-offs between
investments and system condition. By moving these lines along y-axis and x-axis, the
worthwhile scenarios can be revealed. For example, the scenarios plotted in Figure 18 to
Figure 20 underneath the moving straight lines are considered to be favorable (less
investments and better system conditions). With the “cut-off” straight lines positioned as
shown, the minimum BCR=1 scenarios are favorable for all three regions. Particularly,
for the WILMAPCO region, the “FC2” scenario (the WILMAPCO aspiration scenario) is
also considered favorable over other scenarios.
101
Minimum Cost Objective ($m)
$45,000
FE
$40,000
$35,000
$30,000
FC 2
BCR 1
$25,000
$20,000
BCR 3
$15,000
FC 1 MC
$10,000
$5,000
$0
0%
1%
2%
3%
4%
5%
6%
7%
8%
Best System Condition O bjective (System Deficiencies)
Figure 18: Decision-Making Plot for CATS
$8,000
Minimum Cost Objective ($m)
$7,000
FE
$6,000
$5,000
$4,000
FC 2
$3,000
BCR 1
MC
$2,000
$1,000
$0
0%
BCR 4
1%
2%
3%
4%
5%
6%
Best System Condition O bjective (System Deficiencies)
Figure 19: Decision-Making Plot for WILMAPCO
102
FC 1
7%
Minimum Cost Objective ($m)
$45,000
FE
$40,000
$35,000
$30,000
BCR 1
FC 2
$25,000
$20,000
FC 1
BCR 4
MC
$15,000
$10,000
$5,000
$0
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
Best S ystem Condition Objective (S ystem Deficiencies)
Figure 20: Decision-Making Plot for DVRPC
Figure 18 to Figure 20 provide transportation investment analysts a way of
taking into account two or more factors at the same time when making trade-offs among
all HERS scenarios. Similarly, the figures illustrating the correlations between
investments and investment returns can also be made. From the three case studies shown
above, it is also interesting to note that for the CATS region, the “maintain current
condition” scenario is shown in Figure 18 as the least preferable alternative. This may
due to the fact that the current system condition of CATS region is not as good as
expected, and the revenue forecasted over the planning horizon is considered fairly
sufficient to improve the system to a significantly better condition.
103
At this point, it can be concluded from the comparative case studies that
constrained funding can also be modeled by adjusting the minimum BCR threshold until
the HERS-recommended spending meets the budget constraint; this method then reveals
the level at which worthwhile projects are not implemented. Even without any increase in
total highway investment, substantial benefits could have been gained by allocating
highway funds more efficiently. For example, instead of setting aside similar amount of
funding (discounted) for each 5-year funding period, investigations on BCR of each
possible improvement type using HERS (if 100% HPMS data is available, the
investigations could be done on the project level) are demonstrated to be significantly
beneficial. Similarly, although there might be political constraints on the amount of
highway related funds available for each funding period, deferred investment options
could be another alternative to investing heavily in early funding periods as shown in this
study. Quantitatively, with the same total available funds, the BCR oriented approaches:
•
Give better system conditions over each funding period: reducing system
reconstruction deficiencies by up to 34% of the initial deficiencies;
•
Obtain higher overall investment returns on user benefits: up to 21% more
returns; and
•
Reduce system maintenance costs by up to 24%.
104
In addition, based on this experience in applying HERS-ST to the MPO
context, we also make the following application related observations:
•
The HERS model tends to allocate the majority of total funds to the first
FP if minimum BCR scenarios are selected and the initial system
conditions are not above a significantly “good” level. In reality, this
funding scheme might not be politically feasible. For questions like this, is
there any way to combine the funding constrained scenarios with the
minimum BCR scenarios to get the best optimal and realistic results using
HERS? The answer is yes and no. This could be done one way but not
another, i.e. the funding constrained scenarios can be modeled under
certain pre-defined minimum BCR threshold, but the minimum BCR
scenario appears not be able to modeled with pre-defined funding
constraints (or a range of funding constraints for certain FP). Therefore, it
is still possible to arrive at a both optimized and practical solution from
HERS with a number of manual adjustments, though close examinations
that involve unknown information may be needed.
•
Whether or not the overall average BCR is maximized over the planning
horizon depends on which scenarios are selected and on the initial system
condition. Under the same total available funding amount, minimum BCR
approach does not necessarily produce higher overall average investment
105
returns if less candidate projects with higher potential BCRs are available
at the first FP. However, according to HERS, under the same total
available funding amount, minimum BCR approach may always provide
the region with a better performed highway network (in technical terms
without being adjusted by other influencing factors).
•
Above all, this study suggests that HERS is a useful BCA tool for MPOs
in making their long-range RTPs, however, the modeling results need to
be used selectively, manual adjustments are often required, and decisions
should be made along with the regions’ specific situations.
It is now clear that one of the major benefits HERS brings to the MPO
decision makers is that it is capable of providing a BCA perspective to the financial
analysis part of the RTPs. Without this perspective, the main body of financial
recommendations is usually derived from the projected revenues, and the financial
analysis work often starts from asking what we could do with what we are supposed to
have. Even though there is usually a candidate list of projects that are developed for
better utilize the available funding, most of the project evaluation criteria are soft criteria
which involves mainly subjective evaluations and judgments. Although, in some regions,
the Transportation Improvement Program (TIP) is done based on some BCA criteria, it is
basically a short-term project level effort instead of a long-term system level approach.
106
All these concerns with the existing process and the results using HERS-ST suggest that
the involvement of HERS-ST is an important improvement in regional planning process.
107
CHAPTER 6
CONCLUSIONS AND FUTURE WORK
Regional transportation planning has evolved in terms of its importance in
guiding investments and the need to recognize issues relating to aging infrastructure and
the cost of maintaining and operating existing systems. Many tools for traditional
planning in various specific areas have been explored, however, only few tools for
broader planning including asset management have been adequately investigated and
evaluated. This research explored this gap and developed solution options. Specifically,
HERS and HERS-ST were originally developed as a national and state level economic
analysis computer model for highway system. This research makes the first attempt to
apply the HERS-ST analysis tool to the regional level and proposes the integration
process of the application of HERS-ST with the existing regional transportation planning
process.
7.1
Conclusions
This research provides a rigorous, systematic, and replicable approach
(process) to assessing financial assessments in the regional transportation planning
108
process. A set of asset management tools and methods are explored, utilized, and
presented through case studies. An analytical multi-criteria framework is also proposed to
approach the ultimate goal of a balanced and defensible decision-making process for
RTPs.
HERS is an effective tool in bridging economic concerns with engineering
standards in the regional transportation planning process. Practically, HERS-ST is
capable of doing analysis on the regional level and generating economically justified
highway system renewal scenarios for MPOs. However, regional transportation planning
is such a complex process with multiple dimensions. Although HERS adequately brings
in the benefit-cost factor as one of the evaluation criteria, many other factors need to be
analytically considered along with the HERS recommendations in order to better inform
the decision-making process. As a research design presented in Appendix I and II, this
research demonstrates an analysis procedure for applying HERS-ST to regions with
different characteristics, and furthermore, it also proposes an analytical framework for
incorporating HERS results with other influencing factors.
One extension of the studies discussed in the previous chapters is the
overarching concept of asset management. As mentioned before, asset management is a
systematic process of combining engineering principles with economic theory in
109
maintaining, upgrading, and operating physical assets cost effectively. It also provides
tools to support cooperative decision-making. HERS is an excellent asset management
tool that focuses on system renewal and brings engineering principles and economic
theory together. But asset management system is far more than that, and it is not a
composition of one or several analytical tools. The MCA frameworks is one more step
further from the tool applications, and as one of the MCA frameworks, the hierarchical
highway renewal framework proposed in this study aims to help final decision making.
This research is mainly dealing with highway assets, and similar decision making
frameworks for other asset classes could be added into the asset management systems
(AMS). Through the use of these analytical tools and decision making approaches
embedded in AMS, MPOs can act more sophisticatedly in allocating limited resources.
The discussions in this research are also extended based upon the theoretical
concepts of package solutions. As mentioned before, compared with typical
transportation planning process, for example, a package approach that includes
investments strategies and demand management elements for multi-modes appear robust
against shifting political preferences. Instead of putting randomly collected schemes
under a certain policy headline, another key attribute of package solutions is that any
single solution package is a combination of programs, projects, and implementation
strategies together with their strategic aims and policy concerns. Any single change in the
110
package profile will be analytically adjusted, while still keeping the major part of the
package profile intact. Thus, the package solutions is capable of providing solution
alternatives under complex decision-making context, and is one of the preferable solution
approaches that are compatible with higher-order goals for urban development, while still
following planning principles and techniques closely. This research also takes advantage
of the package solution approach in shifting from project level analysis to strategic
planning level analysis. The final argument is that we are not simply involving BCA into
the regional transportation planning process in this research, but we are also trying to
support the notion that a region needs an healthily functioning physical asset network and
also needs well defined goals and policies to direct future development.
From a package solutions rationality perspective, Figure 21 shows the
general format for planning hierarchies (adopted from Saaty, 1990). As we can see in the
figure, from a transportation planning practice perspective, the forward (projected
planning) hierarchy is basically a top-down procedure and the backward (idealized
planning) hierarchy is basically a bottom-up procedure. In other words, the former
hierarchy follows instrumental rationality and the latter follows the communicative
rationality in the package solution terms. This research starts from the instrumental side
by doing BCA analysis using HERS and physical asset condition data to assess the
logical future. In this stage of the analysis, the minimum BCR scenarios gained higher
111
priorities. However, the MCA hierarchical framework analysis that involves stakeholder
inputs follows communicative planning approach. And in this stage of analysis, the
WILMAPCO case study show different priority rankings for the same solution
alternatives, and the maintain current condition and funding constrained alternatives
obtained higher overall priorities. Therefore, in package solution hierarchical analysis
terms, this research combines both top-down procedure and bottom-up procedure in
assessing optimal solutions. And it is further argued in this research that the final
solution recommendations should be made after examining analysis results come out of
each procedure.
Forward or
Projected
Planning
Backward
or Idealized
Planning
Goals
Implementation
Strategies
Present
Policies
Actors
Response
Policies
Actors
Scenarios
Scenarios
Figure 21: General Format of Planning Hierarchies
112
Logical
Future
Desired
Future
7.2
Limitations
Despite all the efforts made in this research, limitations still exist in the
overall research approach, in the method used, and in the data and tools utilized.
7.2.1
Limitations of the Research
Again, this research only focuses on highway asset and does not consider
other asset classes. Although the BCA analysis is done on the network level using
highway section data, the highway network interdependencies are not adequately
analyzed in HERS. Since multi-modal and networking are two important features of
urban transportation system, the absence of other modes or network connectivity can be
considered as the major limitations of the research. The entire study intends to work on
the direction of generating overall financial analysis solutions for regional assets, which
could be eventually integrated into regional asset management systems. Considering the
complexity of the problem context, this research only partially fulfills this goal so far. In
addition, it should also be noted that the case study selection in this research is based on
available data and local knowledge and may not fully representative of all MPOs.
113
7.2.2 Limitations of the Method
A complete BCA process consists of three components: alternatives, impacts
(benefits, costs, and transfers), and evaluation (efficiency and equity). In this research,
the first two components are generally discussed; however, the last component is still left
open. The BCA process is mainly performed by HERS here, and the evaluation of the
BCA process itself is not included in HERS’s functions. Therefore, as part of the
limitations in the research method, the usage of HERS as the only BCA tool is considered
as an insufficient solution. Ideally, the HERS modeling process should be supplemented
by other BCA procedures to minimize the bias due to the limitation of tool itself.
In addition, the MCA hierarchy framework proposed in this study is also not
complete and only for the purpose of demonstration. In the real world application settings,
depending on the specific needs of each individual MPO, the final hierarchy may consist
of more than five levels, and more influencing factors may be involved in each level.
Sub-categories may also be created if it is preferred to involve separate programs (e.g.
congestion management program and bridge management program) within a MPO as
stand-alone influencing forces. In short, there are flexibilities in the adoption of the
hierarchy framework and this research only provides a simplified solution option.
114
7.2.3 Limitations of the Data and Tools
One data limitation is that the HPMS data used in this research is not 100%
sample data, which makes it impossible to do any analysis on the project or highway
section level. Instead, this research only makes recommendations based on the entire
network. Another data limitation is the stakeholder survey inputs. A sample of 15
stakeholders were selected, however, it is not evaluated in this study whether the sample
is fully representative of the population. Processing error may also exist in using the
averaged pairwise comparison ratings among survey groups.
As one of the primary tools used in this research, HERS has its inherent
limitations in doing BCA analysis
•
Only highways are considered explicitly (other transportation modes such as
transit or other public good areas such as education are considered indirectly
through the discount rate);
115
•
No interdependencies (such as network impacts) among highway sections are
addressed in the model;
•
New construction on new alignment is not explicitly included (reconstruction is
considered);
•
Initial improvement costs include typical capital expenditures (the cost of delay
associated with implementing improvement options is not considered); and
•
The only user charges included are fuel taxes (tolls are excluded)
While the HERS model does not perform all types of analyses that might be
desirable, it does represent a significant advancement in the development of highway
investment /performance analytical techniques over previous tools.
116
7.3
Future Work
Under the scope of this research, it is far from total solutions to all the
regional transportation planning problems. Major concerns that are not discussed in this
study include regional economic impacts, social equity, multi-mode and network effects,
and infrastructure interdependencies. In reality, MPOs seldom evaluate equity outcomes
and aggregate economic welfare analytically due to various technical difficulties. There is
also lack of motivation to further argue these measures, because these analyses are not
required by the Surface Transportation Act of 1991. However, there are many programs
within MPOs that are specifically dealing with multi-mode issues, and the infrastructure
interdependency is also textured into the regional transportation planning process in
various ways.
In terms of future work, the above key issues should be integrated into the
decision-making framework, either as components of the MCA framework, or as
supplemental influencing factors. Ideally, the BCA analysis should be done on the
network level which captures multi-modal and infrastructure interdependency effects, and
also be able to calculate the social benefits and costs occurred with the impacts of
transportation improvement projects on regional economic and social equity. The
117
quantified values of these impacts are further adjusted by their corresponding policy
concerns in the MCA analysis to obtain overall package solutions for regional
transportation planning.
From a broader asset management point of view, the MCA framework should
work with management programs for other asset classes. Interrelationships should also be
analytically interpreted between asset management programs and capacity expansion
programs. And eventually, financial analysis behaves as linkages among core asset
management system and all other satellite programs.
7.4
Contributions
Through the case studies, the application of the HERS-ST tool, the
formulation of the MCA decision making framework, the collection of stakeholder
preferences through surveys, and the application of the AHP tool using data from HERSST and the stakeholder inputs, this thesis accomplished the objectives outlined in Chapter
1. Specifically,
118
1. Provided a framework for rigorous analysis of investment strategies for
maintaining and improving the existing regional highway infrastructure;
2. Described and defined the financial analysis gaps identified in the current
RTPs through three comparative case studies; and
3. Developed a strategy in addressing the financial and economic analysis gaps
identified through case studies, and provided a systematic hierarchy
framework that generates regional transportation investment packages as one
of the solution options. The application to WILMAPCO demonstrates the
relevance and value of the framework, tools and strategy by developing
solution packages that meet financial constraints or meet other goals.
The utilization of BCA measurements and multi-criteria evaluations have
long been on most MPO’s wish list. However, due to the complex nature of the regional
planning process, a well defined investment analysis procedure has not been widely
adopted on the system level by MPOs. This research recognized and contributed to the
flowing major concerns:
119
1. Measures: if all possible, a single measure is preferred in evaluating
different improvement project impacts, such as impacts on congestion, air
quality, economics, and equity. BCA provides one such option that
translates multiple regional impacts into monetary terms.
2.
Network effects: many comprehensive evaluations procedures have
been adopted by MPOs to assess impacts on the transportation project
level, but not satisfactorily on the net work level. The MPOs are still
looking for better answers in tools and methods to measure network
effects of the prioritized projects recommended by RTPs.
3. “Soft factors”: one of the key features of RTPs is that they are supposed
to be goal driven and policy rich, which is referred here as soft
influencing factors. Beyond technical analyses of the regional
transportation system, there are considerable amount of policy concerns
need to be taken into account in the actual decision-making process.
4. Accountable planning: to keep our planning process accountable and
transparent is part of our ultimate goal in financing transportation systems
throughout the nation.
120
The research contributes a solution to all the concerns addressed above. A
benefit-cost approach based multi-criteria investment analysis procedure is not only about
financing regional transportation infrastructure, but also provides possibility of
simplifying dimensions of measures, taking into account network effects, involving
qualitative factors in decision-making, and improving accountability in the transportation
planning practices.
From the practical perspective, the traditional transportation modeling
process only takes into account narrow economic measures such as travel delay for
measuring congestions. Ideally the transportation models should incorporate broader
economic measures such as overall economic utility of travelers in assessing road
congestions. Incorporating the framework proposed in this research with the existing RTP
evaluation procedures would function more sophisticated in that it includes a larger range
of social and environmental costs and benefits and offers immediate opportunities in
assessing regional economic impacts of transportation planning. This research takes
several steps forward in contributing to this aspect of solution discussion for regional
transportation planning.
121
BIBLIOGRAPHY
Ahlstrand I (1998), “The Rise and Fall of the Heroic Transport Plan for Stockholm”, Transport
Policy 5: 205-211.
Berechman Joseph et al., (2005), “Evaluation, Prioritization and Selection of Transportation
Projects in New York City”, Transportation 2005, vol. 32, pp.223-249.
Bull D. and Seale K (1994), “A Sustainable Transport Package for London.” Transport Policy
and Its Implementation, pp 29-40. Proceedings from the 22nd PTRC Summer Annual Meeting.
Warwick: University of Warwick.
DeCorla-Souza P & Cohen H (1999) “Estimating Induced Travel for Evaluation of Metropolitan
Highway Expansion”, Transportation 26(3): 249-262.
Dornan, D., (2000), “GASB 34’s Impacts on Infrastructure Management, Financing &
Reporting.” Infrastructure Management Group, Inc. White Paper.
Dornan, D. (2002), “Asset Management: Remedy for Addressing the Fiscal Challenges Facing
Highway Infrastructure”, International Journal of Transportation Management 1 (2002), p41-54.
Expert Choice Official Website: http://www.expertchoice.com/about/history.html
Oct. 6, 2007.
Accessed on
Federal Highway Administration (FHWA), and Federal Transit Administration, April 1994.
Federal Certification of the MPO (TMA) Process, Washington, DC: US DOT.
FHWA (2002), “HERS-ST Pilot Program Report”, Washington, DC: US DOT.
FHWA (2006), “Transportation and Asset Management”,
http://www.fhwa.dot.gov/infrastructure/asstmgmt/tpamb.cfm Accessed on Dec. 15, 2006.
FHWA (2006), “Integrating Asset Management into Metropolitan Planning Process – a Peer
Exchange” by PB Consult Inc.
Federal Highway Administration (FHWA), HERS-ST v.2.0 Technical Report 2005, U.S.
Department of Transportation.
Fraser, N., I. Bernhardt, E.M. Jewkes, and M. Tajima. Engineering Economics in Canada, 2nd
Edition, Prentice Hall Canada Inc., Scarborough, 2000.
122
Frederickson, G. “The Repositioning of Public Administration.” Political Science and Politics,
Vol. 32, No. 4, 1999, pp. 701-711.
GASB Statement No. 34 (1999), “Basic Financial Statements – and Management’s Discussion
and Analysis – for State and Local Governments”, Governmental Accounting Standards Board.
Norwalk, CT.
Guiliano Genevieve (2007), “The Changing Landscape of Transportation Decision Making”,
Transportation Research Board 2007 Annual Meeting, Washington D.C., January 2007.
Hamilton David K. (2002), “Regimes and Regional Governance: The Case of Chicago.” Journal
of Urban Affairs, v24 (4), 403-23.
INDOT (2003), “The INDOT Twenty-Five Year Plan as Amended November 2003. Division of
Environment, Planning and Engineering, Indiana Department of Transportation, 2003.
ISTEA Guide:
http://ntl.bts.gov/DOCS/424MTP.html accessed on Nov. 26, 2007.
Johnson Robert A. (2003), “The Urban Transportation Planning Process”,
http://www.des.ucdavis.edu/faculty/johnston/pubs.htm Accessed on Dec., 7, 2006.
Johnson Robert A. and Caroline J. Rodier (1994), “Critique of Metropolitan Planning
Organizations’ Capabilities for Modeling Transportation Control Measures in California”,
Transportation Research Record 1452.
Jones P. (1991). “Gaining Public Support for Road Pricing through a Package Approach”, Traffic
Engineering and Control, 32 (4): 194-196.
Jones P. (1994), “Methodological Issues Surrounding the Design and Evaluation of Transport
Packages”, Paper presented to the 22nd PTRC Summer Annual Meeting. Warwick, University of
Warwick.
Langmyhr Tore (2001), “The Rationality of Transport Investment Package”, Transportation 2001,
vol. 28, pp. 157-178.
Langmyhr Tore (1999), “Understanding Innovation: The Case of Road Pricing.” Transport
Reviews 19(3): 255-271.
123
Lee D.B. (2000), Methods for Evaluation of Transportation Projects in the USA, Transport
Policy 7 (2000) 41-50.
Lyons, W., et al. (1993), “Review of the Transportation Planning Process in the Southern
California Metropolitan Area”, prepared for US DOT/FTA and FHWA, Cambridge, MA: US
DOT/Volpe, August 1993.
McNeil, S., Tischer, M.L., Deblasio, A.J., Asset Management: What is the Fuss? Transportation
Research Record 1729, Transportation Management and Education. Transportation Research
Board 2000, Washington, D.C.
Miller, Eric J., David S. Kriger, and John Douglas Hunt (1999), “Research and Development
Program for integrated Urban Models”, Transportation Research Record, 1685.
Mohring, H.D. and Harwitz, I. (1962), “Highway Benefits: An Analytical Framework”.
Northwestern University Press, Evanston, IL.
ODOT (2002), “Oregon Highway Plan”, Transportation Development, Oregon Department of
Transportation, 1999.
Pagano, Anthony M., Sue McNeil and Elizabeth Ogard, “Linking Asset Management to Strategic
Planning Processes: Best Practices from State DOT’s”, Transportation Research Record, No.
1924, pp 184-191, 2005.
Saaty, T. L., (1990). “Multi-Criteria Decision Making: The Analytical Hierarchy Process AHP
Series”, Vol.1 RWS Publications, Pittsburgh.
Saaty, T. L., (1995), “Transport Planning with Multiple Criteria: The Analytic Hierarchy Process
Applications and Progress Review”, Journal of Advanced Transportation, Vol. 29, No. 1, p. 81 –
126.
SAFETEA-LU:
http://www.fhwa.dot.gov/safetealu/index.htm accessed on Nov. 26, 2007
Siwek, S. J., (1995), A Guide to Metropolitan Transportation Planning Under ISTEA: How to
Pieces Fit Together, prepared for US DOT/FHWA and FTA, Washington, DC: US DOT/FTA.
Small, K., Winston, C. and Evans, C. (1989), “Road Work: A New Highway Pricing and
Investment Policy”. Brookings Institute, Washington D.C.
124
Smith J.T. and Tighe S. L. (2005), “The Analytical Hierarchy Process as a Tool for Infrastructure
Management”. Transportation Research Board, 85th Annual Meeting, January 22-26, 2006.
Washington, D.C.
Still B (1995), “The Importance of Transport Impacts on Land Use in Strategic Planning.” Traffic
Transport Projects. Transport Policy 4(3): 141-146.
TEA-21 Official Website:
http://www.fhwa.dot.gov/tea21/index.htm accessed on Nov. 26, 2007
Treyz, F. and Treyz, G. The REMI Economic Geography Forecasting and Policy Analysis Model.
Regional Economic Models Inc., Amherst, MA, 2002.
Tudela Alejandro et al., “Comparing the Output of Cost Benefit and Multi-criteria Analysis: An
Application to Urban Transport Investment”, Transportation Research A 2005.
U.S. Department of Transportation (2001), “Data Integration Primer for Transportation Asset
Management”,
http://www.fhwa.dot.gov/infrastructure/asstmgmt/difact.htm Accessed on Dec. 15, 2006.
Van de Wilden, P., et al., (1996), Enhanced Planning Review of the Miami Metropolitan Area,
prepared for US DOT/FTA and FHWA, Cambridge, MA: US DOT/Volpe, May.
Wegener, Michael, (1994), “Operational Urban Models: State of the Art”, Journal of American
Planning Association, vol. 60, No. 1, pp. 17-29 (winter).
Wiener, E., (1999), The History of US Transportation Policy. 2nd ed., Sage Newbury Park, CA.
Weiner, Edward (1997). “Urban Transportation in the United States – An Historical Overview”,
5th edition, Washington D.C., September, 1997, http://tmip.fhwa.dot.gov/clearinghouse/docs/utp/
accessed on January, 2007.
Wikipedia: http://en.wikipedia.org/wiki/Analytic_Hierarchy_Process accessed on Oct. 15, 2006.
Wikipedia: http://en.wikipedia.org/wiki/Intermodal_Surface_Transportation_Efficiency_Act
accessed on Nov. 26, 2007
Wikipedia: http://en.wikipedia.org/wiki/Transportation_Equity_Act_for_the_21st_Century
accessed on Nov. 26, 2007
125
Wikipedia:
http://en.wikipedia.org/wiki/Safe%2C_Accountable%2C_Flexible%2C_Efficient_Transportation
_Equity_Act:_A_Legacy_for_Users accessed on Nov. 26, 2007.
126
APPENDIX I
ASSESSING INVESTMENT STRATEGIES
Using HERS-ST output as BCA inputs in the MCA framework, a case study
for the WILMAPCO region illustrating the assessment process for regional highway
investment strategies is presented in this chapter.
AI.1
Survey Report
A survey was conducted during July and August in 2007 to generate the
relative importance of the qualitative inputs to the AHP hierarchy framework. The
sample survey can be found in Appendix II. The survey requires roughly 30-60 minutes
to complete depending on knowledge level of each individual participant. As shown in
Figure 8, input from three groups of stakeholders is required in this case study: the
general public, the highway users, and the transportation agencies. The stakeholders were
identified through WILMAPCO’s Public Advisory Committee (PAC) and Technical
127
Advisory Committee (TAC). The surveys were conducted through in-person interviews
and the total number of valid participants is 15, consisting of 5 members of the general
public, 4 respondents from the highway users group, and 6 respondents from the
transportation agencies. Among those, the highway users and the general public
volunteered to participate, and the participating transportation agencies include the MPO,
FHWA, Delaware Department of Transportation, and county and municipalities.
Considering the amount of time (60-minute interview on average) committed to each
participant and the time constraint for this part of the research, the total number of
participants may not fully representative of the entire population. In the real world
application setting, a complete sample test should be done in order to better interpret the
final survey analysis results.
In processing the survey results, in each stakeholder group, an average rating
is taken for each of the pairwise comparisons. It should be noted that although the
averaged rating technique is generally acceptable in AHP frameworks, the averaged
pairwise comparison results would be more towards the middle of the scale (equal
importance) and neutralize the extreme ratings that show up in the surveys. Figures 22-25
show the results of the pairwise comparison using Expert Choice for the top three layers
in the hierarchy framework. Table 10 summarizes the pairwise comparison results for the
fourth layer. The inconsistencies for each of the nodes in the hierarchy range from 0.0003
128
to 0.08, and the overall inconsistency ratio for the entire hierarchy is 0.04, which are all
under the tolerable level of 0.1. This means that in terms of inconsistency, the pairwise
comparison survey conducted among three different groups of WILMAPCO stakeholders
are considered as reliable inputs to the AHP model.
Based on inputs extracted from the survey results, Expert Choice generated
relative weights for each element in the hierarchy. Figure 22 indicates that in order to
achieve the overarching goal of efficiently functioning regional highway system for the
WILMAPCO region, the “Transportation Agencies” is considered to play the most
important role among the three stakeholder groups, which counts 46.7% of the total
weights. At the same time, the “Highway Users” is considered to contribute more to the
ultimate goal over the “General Public” group by about 10% of the total importance.
Figure 22: Pairwise Comparison Weightings for Stakeholders with Respect to
Regional Goal
129
Figures 23-25 show that among the five objectives identified in the hierarchy,
all the stakeholders think that to protect our natural environment is the most important
objective in building an efficient regional transportation system.
Specifically, the
“Highway Users” value “Natural Environment” and “Public Health and Safety” the most,
and “Social Equity” the least. By contrast, the “Public” value “Social Equity” as the
second most important objective. Interestingly, the “Transportation Agencies” put the
most emphasis on the “Public Health and Safety” issues, while scale the “Regional
Economic Impacts” down to the bottom of their priority list. Another interesting note
here is that neither the “Highway Users” nor the “Public” puts significant weight on the
objective of “Mobility and Accessibility”, which is often the top item on most RTP
objective lists. However, this may due to the fact that the terms “Mobility and
Accessibility” appeared to be too abstract for people who are not transportation
professionals to easily understand. As a follow-up, many participants from the highway
users and the general public groups contacted during the survey results analysis period
agreed on the fact that the specific meanings of the term “Mobility and Accessibility”
were not clear to them at the time of completing their survey questionnaires.
130
Figure 23: Pairwise Comparison Weightings for Objectives with Respect to Users
Figure 24: Pairwise Comparison Weightings for Objectives with Respective to Agencies
Figure 25: Pairwise Comparison Weightings for Objectives with Respective to the
General Public
131
Table 10 summarizes the relative weighting for all the performance measures
with respect to their overarching objectives. Again, the “Pollution Damages” gains the
highest weights from all the stakeholder groups (see highlighted) with respect to the
“Regional Economic Impact” objective. The “General Public” put more weight on
“Traffic Volume” and “Average Travel Speed”, which may also involve some
environmental concerns. On the other side, with respect to “Mobility and Accessibility”,
“Highway Users” tend to value their travel time more and do not like congestion in any
case. And again, probably due to the lack of knowledge, “Pavement Deficiencies” are not
considered critical by either the “Public” or the “Highway Users”. On the benefit and cost
side, it is not surprising that the “Highway Users” value their costs the most, while it is
interesting to see that “Transportation Agencies” are concerned with maintenance costs
and matching needs and funds availability more, and the “General Public” counts more
on improvement costs and investment returns. With respect to social equity, the “Public”
input gained the majority of the total weights. Although every participant paid significant
amount of their attention to environmental protection, the “Transportation Agencies”
concern with environmental issues is greatest among the three groups.
132
Table 10: Pairwise Comparison Weighting for Performance Measures with Respect
to their Covering Objectives
Covering Objectives
Mobility and Accessibility
Regional
Impacts
Economic
Public Health and Safety
Social Equity
Environmental Protection
Performance Measures
Traffic Volume
Peak Hour Congestion Level
Average Congestion Level
Travel Time Savings
Average Travel Speed
Lane Width
Pavement Shoulder Width
Pavement Alignment
Pavement Deficiencies (IRI/PSR)
User Costs
Highway Maintenance Costs
Improvement Costs
Investment Returns (BCR)
Matches of Needs and Funds Availability
Pollution Damages
Pavement Deficiencies (IRI/PSR)
Pavement Alignment
Pavement Shoulder Width
Lane Width
Crash Rate
Injuries Rate
Fatality Rate
Pollution Damages
Global Weight
Global Weight
Public
0.157
0.176
0.146
0.107
0.256
0.054
0.038
0.035
0.032
0.064
0.085
0.110
0.232
0.150
0.358
0.045
0.033
0.024
0.042
0.132
0.181
0.347
0.196
0.052
0.065
Stakeholders
Agencies
Users
0.113 0.118
0.135 0.207
0.127 0.149
0.120 0.149
0.112 0.142
0.058 0.055
0.038 0.066
0.079 0.062
0.218 0.052
0.035 0.203
0.117 0.058
0.058 0.091
0.111 0.154
0.297 0.127
0.382 0.366
0.067 0.069
0.031 0.035
0.029 0.039
0.029 0.057
0.127 0.164
0.202 0.224
0.369 0.298
0.145 0.114
0.046 0.025
0.104 0.098
The survey analysis results explained some of the observations in the final
alternative recommendations generated by the AHP, however, the inconsistencies and
some arguable concerns involved in survey results described above can be explored by
doing a dynamic sensitivity analysis within the Expert Choice software.
133
AI.2
AHP Evaluations
Figure 26 charts the assessment process using the hierarchical analysis
conducted in this research. The objective is to help regional planning agencies develop
their long-range investment strategies using an asset management framework. The
simplified hierarchy is shown in the left column, and the tools used in the WILMAPCO
case study are shown in circles. The process of obtaining global priorities through
stakeholder survey and Expert Choice presented in the previous section is used to capture
the interdependencies among various influencing factors within the regional
transportation context. In this section, the rest of the AHP evaluation process in Figure
26 is explained.
In Chapter 5, six scenarios are developed in HERS-ST for WILMAPCO
region. The scenarios are Minimum BCR=1, Minimum BCR=4, WILMAPCO Funding
Constrained, WILMAPCO Aspiration, Maintain Current Condition, and Full Engineering
Needs. These six HERS-ST scenarios are used as six alternatives in the AHP framework
to generate six comparative solution packages. The process is outlined below.
134
Pairwise Comparisons
Level 1: Goal
Contextual
Interdependencies
Level 2: Stakeholders
Stakeholder
Survey
Expert
Choice
Global
Priorities
Synthesis
Level 3: Objectives
Level 4: Performance
Measures
HERS-ST
Level 5: Alternatives
Section Conditions and
Improvement Recommendations
Solution
Packages
Numerical
Values
System
Average
Figure 26: Illustration of AHP Evaluation Process
The section condition data are first exported from HERS-ST for each of the
six scenarios. A numerical value was extracted from relevant HPMS variables for each of
the 23 performance measures on the forth level of the hierarchy, and then an average was
taken across the network for all sections and the entire planning horizon. Table 11 shows
the normalized numerical values for each performance measures with respect to each of
the six HERS-ST scenarios before being adjusted by the weighting system extracted from
the survey and pairwise comparisons from Expert Choice. The final value is normalized
135
by taking the percentage of the averaged value of each performance measure for each
scenario with respect to the sum of the values for that performance measure for all six
scenarios (across rows in Table 11). In other words, the normalized values of any
performance are summed to 1 for all six scenarios. It should be noted that for the negative
values (i.e. the larger the value, the worse the system condition) involved in this
normalization process (e.g. pavement deficiencies and pollution damages), the
normalization process starts by taking a numerator of 1 divided by the negative
denominator, and then the normalization process described above is followed. Therefore,
it is assured that all normalized scores shown in Table 11 are positively correlated to
system conditions. One limitation of this normalization process by taking percentages
across rows in Table 11 is that the normalized values for each performance measure may
appear not significantly different from each other. An alternative method is to further
convert all the normalized values into scores ranging from 0 to 100.
Some observations are highlighted in Table 11. Under the overarching
objective of mobility and accessibility, the “WILMAPCO Funding Constrained” and
“Maintain Current Condition” alternatives gain lowest scores on increasing traffic
volumes. The Full Engineering Needs” alternative gives the system a much better
performance on reducing pavement deficiencies, which is not surprising. Under the
overarching objective of regional economic impacts, the “Minimum BCR=1” alternative
saves the most costs on maintenances, while the “Minimum BCR=4” alternative saves
the most costs on improvements. Following the HERS philosophy, the less total funds
136
available, the higher the BCR threshold the program gives. So it is understandable that
the “Minimum BCR=4” and “WILMAPCO Funding Constrained” alternatives that
require the least total investment gained higher scores on investment returns. In terms of
“Matches of Needs and Funds Availability”, this performance measure is defined here by
calculating the differences between the total investment needed and the WILMAPCO
projected available revenue over the 20-year planning horizon. The greater the funds gap,
the lower the score. The “WILMAPCO Funding Constrained” alternative is developed by
WILMAPCO mainly based on their revenue projections, so this alternative along with
another matching alternative “Minimum BCR=4” obtained significant high scores.
Another important performance measure under the objectives of “Regional
Economic Impacts”, “Public Health and Safety”, and “Environmental Protection” is the
pollution damage cost savings, which is scored by the survey participants as their top
priority among all performance measures.
From Table 11, it is shown that the
“WILMAPCO Funding Constrained” and “Maintain Current Condition” alternatives
obtained highest scores on this performance measure, and it is not surprising if these two
alternatives win out after being adjusted by priority weights mainly due to this fact.
Under the overarching objective of “Public Health and Safety”, the HERS
“Minimum BCR” alternatives generally gained higher score on reducing crash and injury
rates. It should be noted that since there is no internal measurement for “Social Equity” in
HERS program, all the six alternatives are sharing an equal numerical score on this
137
objective (a performance measure as well) for the sake of fairness. However, in practice,
opinions could be given by experts regarding the social equity judgment for each of the
six alternatives, by taking into account the detailed alternative characteristics such as the
time and locations of each improvement, maintenance and improvement investment
flows, and geographical distribution of capacity increases etc.
138
Table 11: Normalized Numerical Values for all HERS-ST Alternative
Covering
Objectives
Performance Measures
HERS-ST Scenarios
Minimum Constrained
BCR=4
Funds
($1.13B)
($1.18B)
Minimum
BCR=1
($2.43B)
Aspiration
($2.97B)
Traffic Volume
Peak Hour Congestion Level
Average Congestion Level
Travel Time Savings
Average Travel Speed
Lane Width
Pavement Shoulder Width
Pavement Alignment
Pavement Deficiencies (IRI/PSR)
0.168
0.168
0.168
0.169
0.167
0.120
0.165
0.168
0.162
0.169
0.178
0.168
0.192
0.168
0.126
0.163
0.168
0.181
0.166
0.154
0.161
0.137
0.166
0.124
0.126
0.164
0.087
Regional Economic
Impacts
User Cost Savings
Highway Maintenance Cost Savings
Improvement Cost Savings
Investment Returns (BCR)
Matches of Needs and Funds Availability
Pollution Damages Cost Savings
0.173
0.187
0.145
0.142
0.018
0.136
0.175
0.176
0.118
0.116
0.013
0.128
Public Health and
Safety
Pavement Deficiencies (IRI/PSR)
0.162
Pavement Alignment
Pavement Shoulder Width
Lane Width
Crash Rate
Injuries Rate
Fatality Rate
Pollution Damages Cost Savings
Social Equity
Pollution Damages Cost Savings
Mobility
Accessibility
and
Social Equity
Environmental
Protection
Unadjusted Total
0.164
0.150
0.161
0.145
0.165
0.117
0.132
0.158
0.064
Maintain
Current
Condition
($1.98B)
0.163
0.154
0.164
0.169
0.165
0.118
0.150
0.158
0.098
Full
Engineering
Needs
($6.70B)
0.171
0.196
0.179
0.188
0.169
0.395
0.262
0.184
0.408
0.141
0.168
0.311
0.264
0.454
0.155
0.147
0.141
0.195
0.249
0.483
0.218
0.179
0.148
0.178
0.176
0.028
0.242
0.185
0.180
0.052
0.053
0.004
0.121
0.181
0.087
0.064
0.098
0.408
0.168
0.165
0.120
0.190
0.188
0.200
0.136
0.167
0.168
0.163
0.126
0.153
0.156
0.200
0.128
0.167
0.164
0.126
0.124
0.194
0.188
0.200
0.155
0.167
0.158
0.132
0.117
0.182
0.179
0.200
0.218
0.167
0.158
0.150
0.118
0.153
0.155
0.100
0.242
0.167
0.184
0.262
0.395
0.127
0.134
0.100
0.121
0.167
0.136
0.128
0.155
0.218
0.242
0.121
3.892
3.807
4.338
4.323
3.873
4.769
139
The last row in Table 11 shows the unadjusted score for all the six
alternatives with respect to the 23 performance measures. This could also be the final
recommendations made solely by HERS-ST programming with selected criteria and
assuming equal weights. There is no doubt that HERS would recommend the “Full
Engineering Needs” alternative for its better overall performance. Although the “Full
Engineering Needs” alternative may not do well on several performance measures such
as the “Matches of Needs and Funds Availability” and “Pollution Damage Cost Savings”,
it wins out by having higher scores for the majority of the performance measures with
equal weights. The “Minimum BCR=4” and “WILMAPCO Funding Constrained”
alternatives also get higher scores, but this is mainly due the over 0.4 score difference on
“Matches of Needs and Funds Availability” performance measure. This difference may
be reduced after weighting, because either this performance measure nor its overarching
objective “Regional Economic Impacts” get significantly higher global weights. Overall,
the “WILMAPCO Aspiration” alternative is least preferred on this unadjusted alternative
scoring list.
There are basically three approaches to input the numerical values in Table
11 to Expert Choice: the first option is to directly input numerical values of each
alternative with respect to each performance measure in the data-grid function; the
second option is to choose the “direction assessment” function which is a substitute to the
“pairwise assessment”; the third option is to choose the “pairwise assessment”. The first
two approaches are not preferred in Expert Choice because these options do not follow
140
AHP principles and for convenience purposes only (e.g. if you have a large number of
alternatives so that pairwise comparison is not feasible). In the WILMAPCO case study,
the third approach is taken and pairwise comparisons are made among 6 alternatives
based on the numerical values shown in Table 11.
After all the pairwise comparisons are done in Expert Choice for each level
in the hierarchy presented in Figure 8, a synthesis process was initiated to obtain final
priority recommendations for WILMAPCO HERS scenarios.
AI.3
AHP Synthesis Results
Figure 27 illustrates the overall synthesized results for the entire hierarchy
based on the survey results and HERS-ST alternative numerical values. After the six
HERS-ST alternatives are adjusted by the influencing factors selected in this case study,
the synthesized alternatives are now solution packages. Each of the solution packages
contains a set of improvement and maintenance schemes with their specific
implementation timelines, and these solution packages could be dynamically observed by
varying one or more influencing factors.
As Figure 27 indicates, the “Maintain Current Condition” is the most
preferred solution package in this case study setting up, followed by “WILMAPCO
141
Funding Constrained” and “Full Engineering Needs” solution packages. The two
minimum BCR oriented solution packages are preferred to the “WILMAPCO Aspiration”
solution package which gets the least priority.
Figure 27: Synthesized Results for the Entire Hierarchy
Now, a dynamic view of the whole picture could be given by observing the
priority changes by varying the relative weights assigned to the influencing factors. Table
12 and Table 13 present the synthesized results for the second and the third levels in the
hierarchy, where we can take a closer look at the composition of the final priority
recommendation Expert Choice gives in Figure 27. In Table 12, it is shown that from the
perspective of each of the three stakeholders, the “Maintain Current Condition” and the
“WILMAPCO Funding Constrained” package solutions are the top two preferences on
the list. Both “Highway Users” and the “Transportation Agencies” rank the “Full
Engineering Needs” package solution as their third priorities, while the “General Public”
142
prefer the two minimum BCR package solutions more than the “Full Engineering Needs”
one.
143
Table 12: Synthesized Results for the Second Level in Hierarchy
Goal
Stakeholders
Global
Weights
Aspiration
($2.97B)
0.319
Minimum
BCR=1
($2.43B)
0.104
Efficiently
Functioning
Regional
Highway
System
Highway Users
Transportation
Agencies
General Public
Constrained
($1.18B)
Maintain Current
Condition ($1.98B)
0.105
Minimum
BCR=4
($1.13B)
0.123
0.200
0.290
Full
Engineering
Needs ($6.70B)
0.178
0.467
0.127
0.116
0.136
0.192
0.237
0.192
0.214
0.147
0.131
0.167
0.200
0.221
0.134
Table 13: Synthesized Results for the Third Level in Hierarchy
Stakeholders
Objectives
Global
Weights
Minimum
BCR=1
($2.43B)
Aspiration
($2.97B)
Minimum
BCR=4
($1.13B)
Constrained
($1.18B)
Highway
Users
(G: 0.319)
Mobility and Accessibility
Regional Economic Impacts
Social Equity
Natural Environment
Public Health and Safety
Mobility and Accessibility
Regional Economic Impacts
Social Equity
Natural Environment
Public Health and Safety
Mobility and Accessibility
Regional Economic Impacts
Social Equity
Natural Environment
Public Health and Safety
0.057
0.039
0.025
0.101
0.098
0.082
0.024
0.046
0.090
0.225
0.020
0.038
0.052
0.065
0.038
0.128
0.086
0.167
0.056
0.179
0.128
0.079
0.167
0.056
0.187
0.138
0.101
0.167
0.056
0.213
0.225
0.075
0.167
0.037
0.123
0.206
0.053
0.167
0.037
0.123
0.229
0.065
0.167
0.037
0.133
0.059
0.209
0.167
0.089
0.193
0.060
0.237
0.167
0.089
0.199
0.067
0.251
0.167
0.089
0.228
0.041
0.243
0.167
0.283
0.181
0.042
0.312
0.167
0.283
0.197
0.044
0.261
0.167
0.283
0.210
Transportation
Agencies
(G: 0.467)
General
Public
(G: 0.214)
144
Maintain
Current
Condition
($1.98B)
0.064
0.277
0.167
0.499
0.136
0.062
0.263
0.167
0.499
0.150
0.060
0.246
0.167
0.499
0.125
Full
Engineering
Needs
($6.70B)
0.482
0.109
0.167
0.037
0.188
0.501
0.057
0.167
0.037
0.144
0.462
0.075
0.167
0.037
0.091
In Table 13, the third column gives the global weights for each the alternative,
and for each group of stakeholders. The sum of the five objectives is equal to the
stakeholder’s global weight. As already highlighted, the “Natural Environment” and
“Public Health and Safety” objectives generally have higher global weights for either
stakeholder. The sums of the global weights for each alternative under all three
stakeholders are as follows:
− “Mobility and Accessibility”:
0.159
− “Regional Economic Impacts”: 0.101
− “Social Equity”:
0.123
− “Natural Environment”:
0.256
− “Public Health and Safety”:
0.361
As shown, the “Public Health and Safety” and the “Natural Environment”
objectives have considerably higher summed global weights over other objectives, which
obviously played major roles in determining the final recommendations shown in Figure
23. Now, if we look at the priority ranking for all the six solution packages under each
objective, the contextual interdependencies among all the influencing factors with respect
to each of the solution packages could be further examined. Overall, the “WILMAPCO
Funding Constrained” and the “Minimum BCR=4” solution packages obtained
significantly higher priorities over other packages with respect to the “Public Health and
Safety” objective. In addition, the “Maintain Current Condition” and the “WILMAPCO
145
Funding Constrained” solution packages get higher priorities with respect to the “Natural
Environment” objective, and especially, the “Maintain Current Condition” package has
almost 50% of the total priority under this objective. The “Full Engineering Needs”
solution package gained the large portion of its score with respect to the objective of
“Mobility and Accessibility”, and it is also doing well under the Objective of “Public
Health and Safety” from the highway users’ perspective. When comparing the last two
solution packages, the “Minimum BCR=1” package wins in that it has higher priorities
with respect to the objective of “Public Health and Safety” from the perspectives of all
three stakeholders.
Reasonably, the above observations illustrate the story told by Figure 27.
AI.4
Discussions
The survey report section 5.1 explained the global weights for each of the
five objectives. The significantly higher priorities that the “Public Health and Safety” and
“Natural Environment” objectives end up with basically come from the stakeholders’
inputs. By asking why the “Maintain Current Condition” and “WILMAPCO Funding
Constrained” solution packages gained significantly higher priorities with respect to the
“Public Health and Safety” and “Natural Environment” objectives, we can better
understand their relationships.
146
The following equation tabulates the emission cost savings on improved
sections during the last year of the funding period. HERS calculates the savings on a
section as (FHWA,2005):
EMCBen = ( EMCu ∠EMCi ) × VMTu ∠EMCi × (VMTi ∠VMTu )
Where:
EMCBen = emission cost savings;
EMC n
= emission cost per vehicle mile traveled on the unimproved section u or the
improved section i
VMTn
= vehicle miles traveled in the last year of the funding period on the
unimproved section u or the improved section i
Note that HERS regards the cost of emission on additional trips on the
section (as a result of a lower user price due to the improvement) as a disbenefit, and
subtracts it from benefits realized through the reduction of emission on existing trips. The
above equation indicates that the “pollution damage cost savings” performance measure
is negatively correlated to the vehicle miles traveled, i.e. traffic volume. Thus, the
alternatives that have the lowest traffic volume numerical values will gain the highest
pollution damage cost savings. Table 11 shows these two alternatives are the “Maintain
Current Condition” and the “WILMAPCO Funding Constrained”. It should also be noted
here that all the pollution damage cost savings numerical values in HERS-ST output are
147
negative numbers, which basically means that the more the improvements and
maintenance, the better the road conditions, the more the traffic volumes, and the less the
pollution damage cost savings. Therefore, the “Maintain Current Condition” and the
“WILMAPCO Funding Constrained” alternatives assigned significantly higher priorities
by Expert Choice with respect to the “Natural Environment” objective.
Furthermore, Table 10 presents the priority compositions of the each
objectives, and majority of the weights of the “Public Health and Safety” objective are
put on the “Fatality Rate”, “Injuries Rate”, “Pollution Damages”, and “Crash Rate”. The
average ratings among all three stakeholder groups for these performance measures are
0.338, 0.202, 0.152, and 0.141 respectively, which count to a total of 83.3% of the
“Public Health and Safety” priority. As shown in Table 11, the top 3 alternatives which
have the highest priorities on these performance measures are the “WILMAPCO Funding
Constrained”, the “Minimum BCR=4”, and the “Minimum BCR=1” alternatives.
Summarizing the above discussions, based on the stakeholders’ inputs, the
“Natural Environment” and the “Public Health and Safety” are considered as their highest
prioritized objectives in achieving the ultimate goal of building an efficiently functioning
regional highway system for the WILMAPCO region. Among the 6 HERS-ST
alternatives, the “Maintain Current Condition” and the “WILMAPCO Funding
Constrained” alternatives gain the highest priorities and identified in this context by
148
Expert Choice as the two most preferable solution packages, due to their higher rankings
on the above two highest prioritized regional objectives.
The “Full Engineering Needs” solution package is the third preferred option
because of its significant advantages on road conditions and road performances. However,
in reality, this solution package is only considered as a hypothetical HERS-ST alternative
for comparison purposes only, because it is generally not practically feasible.
The HERS-ST case study presented in Chapter 4 for the WILMAPCO region
suggested that by doing the two pairs of comparison between the “WIlMAPCO Funding
Constrained” scenario vs. the “Minimum BCR=4” scenario, and the “WILMAPCO
Aspiration” scenario vs. the “Minimum BCR=1” scenario, the minimum BCR
approaches show overall better system performance, and higher investment returns on
user benefits and maintenance costs savings over their counterparts. It is also shown in
Figure 17 that the “WILMAPCO Funding Constrained” scenario performs better on
pollution damage cost savings over other scenarios. Therefore, as a conclusion for the
WILMAPCO HERS-ST case study, from the system performance and investment return
perspectives, the minimum BCR oriented approaches are favorable. However, when
involving stakeholder inputs which put significantly higher priorities on pollution
damages and public health and safety, the “Maintain Current Condition” and
“WILMAPCO Funding Constrained” approaches are preferable to the minimum BCR
oriented approaches.
149
The WILMAPCO AHP case study illustrates the process of developing a
hierarchical framework for regional transportation planning agencies in making
investment decisions. Table 14 provides an example of the characteristics of the solution
packages by comparing their pros and cons with respect to the objectives considered in
the framework. It is noted that the social equity objective is not included in this example
because the data available for this research is not 100% sample HPMS data so that the
regional impacts in terms of fairness cannot be assessed. In the case study introduced
above, an equal weight is given to each of the HERS alternative on social equity. As
mentioned earlier, the social equity effects of each package solution could be assessed by
analyzing the highway section level characteristics such as the time and locations of each
improvement, maintenance and improvement investment flows, and geographical
distribution of capacity increases etc.
150
Table 14: Solution Package Characteristics with respect to four Regional Objectives
Solution Packages
Minimum BCR=1
Pros
- Public health and safety: low crash,
injuries, and fatality rates;
- Mobility and accessibility: good system
performance
Cons
- Regional economic impacts:
high investment requirements;
- Environmental damages:
increases emissions
- Mobility and accessibility: excellent
system performance
- Regional economic impacts:
high investment requirements;
- Environmental damages:
increases emissions;
- Public health and safety: high
crash, injuries, and fatality rates
- Regional economic impacts: reasonable
investment requirements;
- Public health and safety: low crash,
injuries, and fatality rates
- Environmental damages:
increases emissions;
- Mobility and accessibility: less
good system performance
- Regional economic impacts: investment
requirement meets revenue projection;
- Public health and safety: low crash,
injuries, and fatality rates;
- Environmental damages: less emissions;
- Mobility and accessibility:
relatively worse system
performance
- Regional economic impacts: reasonable
investment requirements;
- Public health and safety: low crash,
injuries, and fatality rates;
- Environmental damages: least
emissions;
- Mobility and accessibility:
relatively worse system
performance;
- Mobility and accessibility: excellent
system performance
- Regional economic impacts:
high investment requirements;
- Environmental damages:
increases emissions;
- Public health and safety: high
crash, injuries, and fatality rates
due to constructions
WILMAPCO
Aspiration
Minimum BCR=4
WILMAPCO
Constrained
Maintain Current
Condition
Full Engineering Needs
151
Table 14 only illustrates the characteristics of all solution packages on the
regional objectives level, however, a complete table could also be made on the
performance measures level which contains detailed engineering and economic
information. In addition, if 100% sample HPMS data is available for HERS-ST
programming, the ideal characteristics table for all solution packages is able to contain
section level information including: specific improvement projects and maintenance
schedules for implementations. The incorporation of the AHP evaluation process into the
regional asset management system will help regional transportation planning agencies fill
the investment analysis gaps in their long-range plans by taking a system-wide BCA
approach, which is also compatible to their current RTP routines.
152
APPENDIX II: SAMPLE AHP SURVEY
There are total of 3 questions in this survey, and pairwise comparisons are required in Question 1 and Question
3. Here is an example of pairwise comparison:
With respect of the objective of Mobility and Accessibility in selecting regional public transportation projects, 2
performance measures A and B need to be compared in Table 1:
Table 1: Pairwise Comparison Example:
Moderate
Strong
Very strong
Extreme
Longer Service Hours
Equal
More Frequent Services
Moderate
and
Strong
Mobility
Accessibility
Performance Measure
B
B over A
Very strong
Performance Measure
A
Objective
OR
Extreme
A over B
9
7
5
3
1
3
5
7
9
In Table 1, number “7” is marked, which means with respect of the objective “Mobility and Accessibility”, and
performance measure A (More Frequent Services) is “Very strongly” important over performance measure B
(Longer Service Hours).
Please do similar pairwise comparisons in Question 1 (Table 2-4), Question 2, Question 3 (Table 5), and
Question 4 (Table 6). In each row of any table, please only check or circle ONE score.
The following 3 questions are set up for selecting regional HIGHWAY projects, and the reference planning
horizon is 20 years.
153
Question 1:
You are asked do a pairwise comparison of performance measures in Table 1-3 with respect to each of the three objectives.
These objectives and corresponding performance measures might be used in selecting regional highway transportation projects.
Table2: Performance Measures Pairwise Comparisons on the “Mobility and Accessibility” Objective – Check or circle the
appropriate score – only circle one rating per row.
A over B
Strong
Moderate
Equal
Moderate
Strong
Very strong
Extreme
Performance Measure B
Mobility and
Accessibility
Traffic Volume (AADT)
9
7
5
3
1
3
5
7
9
9
9
9
9
9
9
7
7
7
7
7
7
5
5
5
5
5
5
3
3
3
3
3
3
1
1
1
1
1
1
3
3
3
3
3
3
5
5
5
5
5
5
7
7
7
7
7
7
9
9
9
9
9
9
9
7
5
3
1
3
5
7
9
9
7
5
3
1
3
5
7
9
9
7
5
3
1
3
5
7
9
Congestion
Level
Congestion
Level
Pavement
Deficiencies
(IRI/PSR)
Pavement Alignment
Pavement Shoulder Width
Lane Width
Average Travel Speed
Travel Time Savings
Average
Congestion
Level
(V/C)
Peak Hour Congestion Level
(V/SF)
Pavement
Deficiencies
(IRI/PSR)
Pavement Alignment
Congestion
Level
Pavement Shoulder Width
9
7
5
3
1
3
5
7
9
Congestion
Level
Lane Width
9
7
5
3
1
3
5
7
9
Traffic Volume (AADT)
Traffic Volume (AADT)
Traffic Volume (AADT)
Traffic Volume (AADT)
Traffic Volume (AADT)
Traffic Volume (AADT)
Traffic Volume (AADT)
Peak Hour
(V/SF)
Peak Hour
(V/SF)
Peak Hour
(V/SF)
Peak Hour
(V/SF)
B over A
Very strong
Performance Measure A
Extreme
Objective
OR
154
Peak Hour Congestion Level
(V/SF)
Peak Hour Congestion Level
(V/SF)
Peak Hour Congestion Level
(V/SF)
Average Congestion Level (V/C)
Average Congestion Level (V/C)
Average Congestion Level (V/C)
Average Congestion Level (V/C)
Average Congestion Level (V/C)
Average Congestion Level (V/C)
Travel Time Savings
Travel Time Savings
Travel Time Savings
Travel Time Savings
Travel Time Savings
Average Travel Speed
Average Travel Speed
Average Travel Speed
Average Travel Speed
Lane Width
Lane Width
Lane Width
Pavement Shoulder Width
Pavement Shoulder Width
Pavement Alignment
Average Travel Speed
9
7
5
3
1
3
5
7
9
Travel Time Savings
9
7
5
3
1
3
5
7
9
Average
Congestion
Level
(V/C)
Pavement
Deficiencies
(IRI/PSR)
Pavement Alignment
Pavement Shoulder Width
Lane Width
Average Travel Speed
Travel Time Savings
Pavement
Deficiencies
(IRI/PSR)
Pavement Alignment
Pavement Shoulder Width
Lane Width
Average Travel Speed
Pavement
Deficiencies
(IRI/PSR)
Pavement Alignment
Pavement Shoulder Width
Lane Width
Pavement
Deficiencies
(IRI/PSR)
Pavement Alignment
Pavement Shoulder Width
Pavement
Deficiencies
(IRI/PSR)
Pavement Alignment
Pavement
Deficiencies
(IRI/PSR)
9
7
5
3
1
3
5
7
9
9
7
5
3
1
3
5
7
9
9
9
9
9
9
9
7
7
7
7
7
7
5
5
5
5
5
5
3
3
3
3
3
3
1
1
1
1
1
1
3
3
3
3
3
3
5
5
5
5
5
5
7
7
7
7
7
7
9
9
9
9
9
9
9
9
9
9
9
7
7
7
7
7
5
5
5
5
5
3
3
3
3
3
1
1
1
1
1
3
3
3
3
3
5
5
5
5
5
7
7
7
7
7
9
9
9
9
9
9
9
9
9
7
7
7
7
5
5
5
5
3
3
3
3
1
1
1
1
3
3
3
3
5
5
5
5
7
7
7
7
9
9
9
9
9
9
9
7
7
7
5
5
5
3
3
3
1
1
1
3
3
3
5
5
5
7
7
7
9
9
9
9
9
7
7
5
5
3
3
1
1
3
3
5
5
7
7
9
9
155
Table 3: Performance Measures Pairwise Comparisons on the “Regional Economic Impacts” Objective – Check or circle the
appropriate score – only circle one rating per row.
Improvement Costs
Improvement Costs
Investment Returns (Benefit-Cost
Ratio)
Investment Returns (Benefit-Cost
Ratio)
Matches of Needs and Funds
Availability
Extreme
Highway Maintenance Costs
Improvement Costs
Very strong
Highway Maintenance Costs
Strong
Regional
Economic
Impacts
Moderate
User Costs
Highway Maintenance Costs
Highway Maintenance Costs
Equal
User Costs
Moderate
User Costs
User Costs
User Costs
Performance Measure B
Strong
Performance Measure A
B over A
Very strong
Objective
OR
Extreme
A over B
Highway Maintenance Costs
Improvement Costs
Investment Returns (Benefit-Cost
Ratio)
Matches of Needs and Funds
Availability
Pollution Damages
Improvement Costs
Investment Returns (Benefit-Cost
Ratio)
Matches of Needs and Funds
Availability
Pollution Damages
Investment Returns (Benefit-Cost
Ratio)
Matches of Needs and Funds
Availability
Pollution Damages
Matches of Needs and Funds
Availability
Pollution Damages
9
9
9
7
7
7
5
5
5
3
3
3
1
1
1
3
3
3
5
5
5
7
7
7
9
9
9
9
7
5
3
1
3
5
7
9
9
9
9
7
7
7
5
5
5
3
3
3
1
1
1
3
3
3
5
5
5
7
7
7
9
9
9
9
7
5
3
1
3
5
7
9
9
9
7
7
5
5
3
3
1
1
3
3
5
5
7
7
9
9
9
7
5
3
1
3
5
7
9
9
9
7
7
5
5
3
3
1
1
3
3
5
5
7
7
9
9
9
7
5
3
1
3
5
7
9
9
7
5
3
1
3
5
7
9
Pollution Damages
156
Table 4: Performance Measures Pairwise Comparisons on the “Public Health and Safety” Objective– Check or circle the
appropriate score – only circle one rating per row.
Objective
Performance Measure A
Performance Measure B
Strong
Moderate
Equal
Moderate
Strong
Very strong
Extreme
B over A
Very strong
OR
Extreme
A over B
Public
Health and
Safety
Pavement Deficiencies (IRI/PSR)
Pavement Deficiencies (IRI/PSR)
Pavement Deficiencies (IRI/PSR)
Pavement Deficiencies (IRI/PSR)
Pavement Deficiencies (IRI/PSR)
Pavement Deficiencies (IRI/PSR)
Pavement Deficiencies (IRI/PSR)
Pavement Alignment
Pavement Alignment
Pavement Alignment
Pavement Alignment
Pavement Alignment
Pavement Alignment
Pavement Shoulder Width
Pavement Shoulder Width
Pavement Shoulder Width
Pavement Shoulder Width
Pavement Shoulder Width
Lane Width
Lane Width
Lane Width
Lane Width
Pavement Alignment
Pavement Shoulder Width
Lane Width
Crash Rate
Injuries Rate
Fatality Rate
Pollution Damages
Pavement Shoulder Width
Lane Width
Crash Rate
Injuries Rate
Fatality Rate
Pollution Damages
Lane Width
Crash Rate
Injuries Rate
Fatality Rate
Pollution Damages
Crash Rate
Injuries Rate
Fatality Rate
Pollution Damages
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
157
Crash Rate
Crash Rate
Crash Rate
Injuries Rate
Injuries Rate
Fatality Rate
Injuries Rate
Fatality Rate
Pollution Damages
Fatality Rate
Pollution Damages
Pollution Damages
9
9
9
9
9
9
7
7
7
7
7
7
5
5
5
5
5
5
3
3
3
3
3
3
1
1
1
1
1
1
3
3
3
3
3
3
5
5
5
5
5
5
7
7
7
7
7
7
Question 2:
Please answer the following question:
What role do you identify yourself in this survey? (Circle one please)
A.
B.
C.
Transportation Agency representatives
A highway user
An ordinary resident representing the general public
Question 3:
Similarly, please do the pairwise comparisons on the relative importance of each pair of objectives from the standing point of
the stakeholder group that you are representing.
158
9
9
9
9
9
9
Table 5: Objectives Pairwise Comparisons – Check or circle the appropriate score – only circle one rating per row.
159
Moderate
Equal
Moderate
Strong
Very strong
Extreme
Regional Economic Impacts
Public Health and Safety
Natural Environment Protection
Social Equity
Public Health and Safety
Natural Environment Protection
Social Equity
Natural Environment Protection
Social Equity
Social Equity
Strong
Mobility and Accessibility
Mobility and Accessibility
Mobility and Accessibility
Mobility and Accessibility
Regional Economic Impacts
Regional Economic Impacts
Regional Economic Impacts
Public Health and Safety
Public Health and Safety
Natural Environment Protection
Objective B
B Over A
Very strong
Objective A
OR
Extreme
A over B
9
9
9
9
9
9
9
9
9
9
7
7
7
7
7
7
7
7
7
7
5
5
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
3
1
1
1
1
1
1
1
1
1
1
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
7
7
7
7
7
7
7
7
7
7
9
9
9
9
9
9
9
9
9
9
Table 6: Stakeholders Pairwise Comparison on the Regional Goal – Check or circle the appropriate score – only circle one
rating per row.
160
Equal
Moderate
Strong
Very strong
Extreme
Highway Users
General Public
General Public
Moderate
Transportation Agencies
Transportation Agencies
Highway Users
Stakeholder B
Strong
An Efficiently Functioning
Regional Highway System
Stakeholder A
B over A
Very strong
Goal
OR
Extreme
A over B
9
9
9
7
7
7
5
5
5
3
3
3
1
1
1
3
3
3
5
5
5
7
7
7
9
9
9
161