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Electronic Theses, Treatises and Dissertations The Graduate School
2014 Roles of Service Planning and Organizational Decisions in Influencing the Economic Sustainability of Multimodal Bus and Light Rail Transit Systems Michal A. Jaroszynski
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COLLEGE OF SOCIAL SCIENCES AND PUBLIC POLICY
ROLES OF SERVICE PLANNING AND ORGANIZATIONAL DECISIONS IN
INFLUENCING THE ECONOMIC SUSTAINABILITY OF MULTIMODAL BUS AND
LIGHT RAIL TRANSIT SYSTEMS
By
MICHAL A. JAROSZYNSKI
A Dissertation submitted to the Department of Urban and Regional Planning in partial fulfillment of the requirements for the degree of Doctor of Philosophy
Degree Awarded: Fall Semester, 2014 Michal Jaroszynski defended this dissertation on September 9, 2014. The members of the supervisory committee were:
Jeffrey R. Brown Professor Directing Dissertation
Keith Ihlanfeldt University Representative
Andrew Aurand Committee Member
Michael Duncan Committee Member
Gregory L. Thompson Committee Member
The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.
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For My Grandpa
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ACKNOWLEDGMENTS
First and foremost I would like to specially acknowledge Dr. Jeff Brown and Dr. Greg Thompson for providing insight and guidance throughout the entire doctoral program, equipping me with comprehensive planning scholar knowledge and skills, preparing me for a professional academic career, and, last but not least, for all their assistance and advice with this dissertation. I truly appreciate their extraordinary mentorship. Many thanks to committee members Dr. Andrew Aurand, Dr. Michael Duncan, and Dr. Keith Ihlanfeldt for providing valuable feedback and help with my dissertation research as well as other assistance during the course of my academic life at the Florida State University. I greatly appreciate the DeVoe L. Moore Center for providing significant financial support, which afforded me the opportunity to dedicate myself full-time to my dissertation research. It was a tremendous honor to receive this support, and I truly appreciate that great opportunity. Recognition is also due to the FSU Department of Urban and Regional Planning and the Mineta Transportation Institute for funding my teaching and research assistantships throughout my doctoral program. I would like to acknowledge my partner, Whitney, for all her contributions and assistance with my scholarly activities and for all the support that enabled me to concentrate on my research. None of this would have been possible without the generosity and care of my parents Irena and Jan, who first motivated me to pursue a doctoral degree and provided unconditional support during my program. My love and gratitude go to Grandpa Slawek for the encouragement and inspiration that he has given to me since the early years of my education. My thanks go to the many others that generously assisted me throughout the course of my doctoral program and dissertation research, including FSU DURP faculty, administrative staff, and my fellow students. I am also grateful to all planners and data custodians from the transit agencies and other organizations for their generous, voluntary help with providing performance data and other relevant information.
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TABLE OF CONTENTS
List of Tables ...... vii List of Figures ...... ix Abstract ...... x 1. INTRODUCTION ...... 1 1.1 Study’s Background: Varied Outcomes of Multimodal (Bus and Light Rail) Transit Investments ...... 2 1.2 Purpose of the Study ...... 5 1.3 Research Questions ...... 7 1.4 Overview of Research Design ...... 7 1.5 Case and Timeframe Selection ...... 8 1.6 Dissertation Outline ...... 21 2. THE BENEFITS AND COSTS OF MULTIMODAL (BUS AND LIGHT RAIL) TRANSIT INVESTMENTS AND OPERATIONS ...... 23 2.1 Literature Review...... 23 2.2 Categories of Benefits and Costs and their Estimated Values ...... 35 2.3 Benefit-Cost Analysis Final Results and Conclusions ...... 49 3. ROLE OF INTERNAL SERVICE PLANNING DECISIONS IN INFLUENCING BENEFITS AND COSTS OF MULTIMODAL TRANSIT ...... 59 3.1 Literature Review...... 59 3.2 Research Methodology ...... 67 3.3 Model Results ...... 75 3.4 Route-level Feasibility and Performance Analysis ...... 80 3.5 Conclusions ...... 87 4. ROLE OF TRANSIT OWNERSHIP AND MANAGEMENT IN INFLUENCING BENEFITS AND COSTS OF MULTIMODAL TRANSIT ...... 92 4.1 Literature Review...... 93 4.2 Research Methodology and Model Specification ...... 99 4.3 Model Results ...... 102 4.4 Policy Analysis ...... 106 4.5 Conclusions ...... 120 5. CONCLUSIONS AND IMPLICATIONS ...... 126 5.1 Key Findings and Implications ...... 126
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5.2 Opportunities for Future Research ...... 132
APPENDICES ...... 137 A. ADDITIONAL RESULTS OF THE BENEFIT-COST ANALYSIS ...... 137 B. ADDITIONAL INPUT DATA FOR REGRESSION ANALYSIS ...... 140 C. IRB APPROVAL ...... 142 D. INFORMED CONSENT FORM ...... 143 REFERENCES ...... 144 BIOGRAPHICAL SKETCH ...... 158
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LIST OF TABLES
1.1 Economic Characteristics of Selected U.S. Multimodal Bus and Light Rail Systems, 2010 ...... 4
1.2 Study’s Case Multimodal Transit Systems and their Selected Characteristics, 2010 ...... 10
1.3 Full List of Transit Agencies Included in the Study ...... 11
1.4 Key Demographic Characteristics of the Case Metropolitan Statistical Areas, 2010 ...... 20
2.1 Results of Four Discussed Transit Benefit-Cost Analyses ...... 29
2.2 Direct Revenues (Transportation and Auxiliary) per Passenger Mile, 2011$ ...... 37
2.3 Consumer Surplus (User Benefits) per Passenger Mile, 2011$ ...... 39
2.4 Congestion Savings per Passenger Mile, 2011$ ...... 41
2.5 Reduction in Negative Environmental Impacts per Passenger Mile, 2011$ ...... 43
2.6 Reduction in Accident Recovery Costs per Passenger Mile, 2011$ ...... 43
2.7 Operating Costs per Passenger Mile, 2011$ ...... 44
2.8 Summary of Capital Cost Categories and their Average Lifecycles ...... 47
2.9 Annualized Capital Costs per Passenger Mile, 2011$ ...... 48
2.10 System-level Total Net Benefits and Net Benefits per Passenger Mile...... 50
2.11 Adjusted Input Parameters Used in Sensitivity Analysis ...... 52
2.12 Sensitivity Analysis Results: Net Benefits per Passenger Mile ...... 53
2.13 Additional Sensitivity Analysis Results: Influence of Changes of Specific Benefit and Cost Categories ...... 54
3.1 Summary of the Previous Findings on the Impact of Network Orientation and Service Planning Decisions on Transit Ridership and Performance ...... 63
3.2 Overview and Discussion of Model Variables ...... 70
3.3 Observations for Variables Reflecting Internal Factors ...... 73
3.4 Pairwise Correlation for Explanatory Variables ...... 74
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3.5 Model Results (Internal Service Decisions) ...... 76
3.6 Additional Statistical Analysis of the Role of Decentralization Ratio ...... 79
3.7 Service Characteristics for Specific Route Categories ...... 83
3.8 Economic Indicators for Specific Route Categories ...... 85
3.9 Decentralization Ratio and Net Benefits...... 89
4.1 Share of Bus Service Volume (Revenue Miles) Operated by Private Contractors...... 101
4.2 Number of Independent Transit Organizational Entities within the Case Metropolitan Areas ...... 101
4.3 Pairwise Correlation for Explanatory Variables (with Ownership and Management Variables) ...... 102
4.4 Model Results (with Ownership and Management Variables) ...... 103
4.5 Summary of Policy Analysis on Governance and Ownership Structure ...... 122
5.1 Summary of Study’s Key Quantitative Results ...... 127
A.1 Total Values of Estimated Benefits and Costs ...... 137
B.1 Observations for Variables Reflecting External Transit Influences ...... 140
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LIST OF FIGURES
1.1 Schematic Diagrams of Study’s Case Regional Transit Systems, 2011 ...... 12
2.1 Graphic Illustration of Consumer Surplus Estimation Methodology ...... 31
2.2 The Idea of Capital Cost Annualization...... 33
3.1 Schematic Concept of a Radial and Decentralized Transit Network ...... 65
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ABSTRACT
Several recently published studies have demonstrated that employing planning strategies
oriented on improving the internal characteristics of transit service, including frequency,
coverage, intermodal integration, and seamless connectivity between all important trip attractors, positively influences ridership and productivity of multimodal bus and light rail transit systems.
However, the research has not assessed overall economic outcomes of implementing these strategies, including social benefits and capital costs. Another emerging body of scholarship pointed to transit service contracting and consolidated regional governance as another possible strategy for improving transit feasibility. Again, not all economic aspects of these decisions have been evaluated thus far, and the available assessments of contracting and transit governance models do not consider long-term effects of specific organizational decisions.
This study intends to fill these research gaps by investigating the influence of several internal and external transit performance factors on the amount of net benefits generated by 13
U.S. bus and light rail transit systems, observed annually during the 2001 – 2011 period. The evaluation starts with an estimation of net benefits (agency revenues plus non-direct social benefits minus operating and capital costs). Next, a panel regression model is employed to examine the statistical relationship between specific performance factors and the average net benefits generated by the case systems. The results of this study indicate that higher frequency, higher service density, higher ratio of contracted service and the presence of strong regional transit governance positively influence net benefits. The role of network decentralization
(volume of service headed outside of the central business district) appears to be insignificant.
These results bring additional evidence indicating the positive outcomes of certain internal transit planning strategies, which corresponds with the findings offered by previous research studies.
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CHAPTER ONE
INTRODUCTION
This study evaluates the influence of internal service planning decisions, which comprise adjusting the internal transit performance factors, on the economic outcomes of multimodal (bus and light rail) transit systems. This study was motivated by recent research findings emphasizing the positive role of specific internal decisions in improving transit performance. The previous research used measures, such as ridership and average vehicle load for evaluating performance, while it did not evaluate the overall economic outcomes and social benefits of these planning decisions. This study intends to fill that gap by estimating the social benefits generated by transit systems that have adopted certain planning strategies. Additionally, a route-level financial analysis is included.
This evaluation also considers another important type of transit planning strategy; specifically, the decisions made concerning transit service ownership and management, including privatization of transit services, and creation of regional governance structures. These strategies appear to be successful in reducing the current operating costs of transit; however, their role in influencing the overall economic outcomes has not been fully investigated so far. The study also, on another unexplored theme in the area of transit management, briefly analyzes the policy processes that lead to service contracting, and to the establishment of specific regional governance structures.
The overall goal of this study is to contribute the ongoing discussion on feasibility of transit investments and operations (which is often focused on the bus and light rail systems), by evaluating specific improvement strategies and identifying successful stories of transit systems
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that manage to maintain financial sustainability, while increasing service supply and
attractiveness for riders. Additionally, the study addresses several other important transit
research objectives by providing new insights on the benefits and costs of rail transit
investments, expanding the discussion on the role of internal transit planning decisions, and
exploring new economic and policy aspects of transit management and governance strategies.
1.1 Study’s Background: Varied Outcomes of
Multimodal (Bus and Light Rail) Transit Investments
In the early 20th century, public transportation carried the vast majority of urban trips within the U.S. cities (Jones, 2010; Warner, 1978). However, along with the popularization of individual motorization, which was catalyzed with technological improvements and governmental policies stimulating rapid development of road infrastructure, the automobiles have almost completely replaced streetcars and buses in serving as the primary mode of urban transportation. The dominant role of cars, initially perceived as a natural consequence of society’s progress and advancement (Flink, 1990), has quickly revealed numerous drawbacks, including issues with congestion, overwhelming costs of infrastructure, environmental damage, and excessive suburbanization (Flink, 1990; Jones, 2010). Starting in the 1960s, policy-makers began to recognize these problems and consequently oriented their attention on modern rapid transit as a possible alternative to the continuous expansion of automobile infrastructure. The emergence of new federal and state funding brought additional incentive for transit investments.
While initially, rapid transit was assumed to be based on sophisticated, but very expensive technologies, such as monorail and heavy rail, in the 1970s some cities drew their attention to the concept of light rail transit (LRT). LRT combines the fully separated heavy rail and traditional
2 streetcar technologies. By including street or at-grade sections, easy adaption of former railroad or streetcar right-of-way, and using smaller, less sophisticated vehicles, LRT requires lower capital and operational expenses when compared to heavy rail or monorail. Simultaneously, LRT still provides higher travel speeds than a traditional streetcar or bus, making transit service attractive for automobile users. The concept of modern LRT emerged in the 1960s in West
Germany, where it has quickly proven to be successful as an attractive and reasonably priced alternative to the automobile. Inspired by these positive outcomes, policymakers began to consider bringing light rail to American cities (Jones, 2010; Thompson, 2003; Vuchic, 2005).
Since 1980, almost twenty U.S. metropolitan areas decided to build new light rail systems. Continuous problems with traffic congestion and increasing awareness of other negative effects of mass motorization motivated a gradual shift in the transportation policy and brought more attention to alternative, sustainable transportation options, such as light rail. The dynamic growth of rail transit has been additionally stimulated with new funding sources, including primarily, federal grants. However, looking from today’s perspective, not all rail investments were successful. At least in some cities, the anticipated substantial increase of the transit modal share did not materialized. Table 1.1 presents selected economic and performance indicators for
13 multimodal bus and light rail systems, which were eventually selected for analysis by this study (the case selection criteria are discussed in Section 1.5).
As indicated by Table 1.1, none of the 13 multimodal transit systems captured more than
10% of the metropolitan travel market. All of these cities continue to struggle with road congestion, and it seems that in most cases, transit investments did not yield substantial reductions in automobile travel (TAMU, 2012). Simultaneously, several systems observe very low farebox recovery ratio, which means that the majority of their capital and operating costs has
3 to be covered with public subsidies. Insignificant impact on the transit modal share and unsatisfactory economic results raised many concerns regarding the feasibility of multimodal transit investments. Critics of excessive public spending have frequently claimed that light rail systems will never attract enough riders to justify their excessive costs (Pickrell, 1992, O’Toole,
2010).
Table 1.1 Economic Characteristics of Selected U.S. Multimodal Bus and Light Rail Systems, 2010
Capital Costs Light Rail System-wide Light Rail System-wide Metro area- of Light Rail Farebox Farebox Operating Cost Operating Cost wide Transit Construction Recovery Recovery per Pass. Mile per Pass. Mile Modal Share (millions of (2009) 2010$) * Buffalo $ 1,368 19% 26%$ 1.45 $ 1.28 3.6% Charlotte $ 544 20% 21%$ 0.94 $ 0.79 1.9% Dallas $ 5,137 13% 12%$ 0.89 $ 1.19 1.5% Denver $ 3,052 31% 28%$ 0.51 $ 0.67 4.6% Houston $ 1,256 39% 18%$ 0.61 $ 0.72 2.2% Minneapolis $ 997 40% 30%$ 0.47 $ 0.70 4.7% Phoenix $ 2,051 28% 20%$ 0.38 $ 0.88 2.3% Pittsburgh $ 2,248 16% 23%$ 1.49 $ 1.14 5.8% Portland $ 3,080 35% 26%$ 0.51 $ 0.79 6.1% Sacramento $ 1,120 30% 24%$ 0.58 $ 0.83 2.7% Salt Lake $ 1,860 37% 22%$ 0.49 $ 0.72 3.0% San Diego $ 2,187 54% 39%$ 0.33 $ 0.59 3.1% St. Louis $ 2,202 32% 24%$ 0.39 $ 0.73 2.6% * - all capital expenses made until year 2010, no annualization Sources: FTIS (2014), U.S. Census (2009)
While these findings might restrain further cities from rail investments, more than twenty light rail systems are already in operation, and all of them continue to seek possible strategies for improving their financial results and reducing the negative impact on local and state budgets.
The radical solution based on shutting down and demolishing those systems is hardly imaginable, as it would be politically unfeasible for the elected officials. Moreover, such decision would actually increase the budget deficit instead of reducing it. The cities would have to return the funding received from federal or state budgets, and they would still have to repay 4
the full costs of the capital (FTA, 2003). Therefore, the discussion on possible solutions to the
problems with multimodal transit inefficiencies should be focused on finding possible strategies
for improving the economic outcomes of the current operations, rather than considering closures
or service reductions.
The figures presented in Table 1.1, as well as other performance and ridership statistics referred later in the text, indicate that while the outcomes of many of the new multimodal transit systems are unsatisfactory, some of those networks perform substantially better than their counterparts do. As suggested by previous research studies, these variations might be caused by specific internal factors, including service parameters, network design, and transit ownership structure. Specifically, the referred research (discussed broadly later in the text) has determined that adapting network structures to the current spatial development trends, providing frequent services, and seamless transfers tends to result in better performance and productivity. Some other studies have identified service privatization and regional governance as other possible internal planning decisions that could positively influence transit efficiency. While the previous studies have focused on ridership, productivity (service utilization), and direct operating costs as their metrics of transit outcomes, this research considers other economic outcomes of transit investments, including the direct costs and revenues, as well as indirect social benefits. The following section elaborates on the objectives of this study.
1.2 Purpose of the Study
This study intends to increase the understanding of the role of internal planning and
organizational decisions in increasing transit effectiveness and economic feasibility. The study
also aims to expand and complement the recent findings on the advantages of particular planning
5 strategies. The results of this study should be useful for transit planners and decision makers who are seeking methods for increasing transit use and service efficiency as well as possibilities of reducing excessive spending and improving the financial results of transit investments. They should provide a better understanding of the role of internal planning factors in improving the outcomes of transit investments and everyday operations The study’s conclusions might be particularly interesting for transit agencies that experience problems with low ridership and low efficiency of their multimodal bus and light rail networks.
Another important goal of this study is to provide new insights on the roles of specific ownership and governance models in transit, and to offer new criteria for evaluating those roles, based on the measures of overall economic outcomes, including all categories of revenues and costs. Such approach has not been utilized in prior research; the available literature provides only basic post-ante economic evaluations of specific organizational models, using operating costs as the only economic indicator. Additionally, this study aims to analyze the policy aspects of particular transit organizational structures by figuring out how they had emerged and assessing their current functionality (in the terms of roles played by agencies and other stakeholders).
The study also aims to contribute to the discussion on the benefits and costs of multimodal transit. As discussed later in the text, recently conducted transit benefit-cost analyses
(BCA) have inconsistent results, mainly due to different approaches to non-direct benefit and capital cost assessments. This study focuses in detail on the importance of all key BCA elements and aims to adopt valid, justified estimates of the non-direct transit benefits. It investigates comprehensively the reasonable assumptions provided by literature for monetizing these benefits and annualizing capital costs. Such precise estimation methods should increase the validity of the analysis results, and bring new insights on the benefits and costs of transit investments.
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1.3 Research Questions
Considering the previous scholarly findings and the objectives of this study discussed in
sections 1.1 and 1.2 the author has formulated the following two major research questions:
1. How do network and service planning decisions influence the economic outcomes of
multimodal bus and light rail transit systems?
2. How do non-traditional forms of transit service management, such as service
contracting and regional governance, influence the economic outcomes of
multimodal bus and light rail systems?
1.4 Overview of Research Design
This study is structured along three major stages, which are discussed in detail by the subsequent chapters:
1) Benefit-cost analysis, which provides estimations of the net benefits generated by
analyzed transit systems. These estimations serve in the following two sections as
indicators of transit economic outcomes (Chapter 2).
2) Statistical analysis of the relationship between internal system characteristics and transit
benefits, and other economic outcomes. This stage provides responses to the first major
research question (Chapter 3).
3) Statistical analysis of the relationship between the organizational structure and transit
benefits, also, brief overview of policies regarding transit management and ownership
adopted in the case cities. This stage provides responses to the second major research
question (Chapter 4).
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To maximize the validity of the study’s results, the author decided to adopt a multiple-
case, time-series study design, focusing on multiple bus and light rail systems, and including
annual observations made throughout several years. Metropolitan transit system X in year Y
serves as the primary unit of observation for the study. The following section presents the case
and timeframe selection process. Detailed description of research methodology and relevant
literature is included separately for each of the three major sections.
1.5 Case and Timeframe Selection
Currently, modern light rail systems operate in 24 U.S. metropolitan areas (APTA, 2014).
The author intended to include as many of the light rail systems as possible in the study, simultaneously trying to retain uniformity across the case set and excluding outlying systems.
For that purpose, all metropolitan areas where heavy rail is operating together with light rail are eliminated: Baltimore, Boston, Cleveland, Philadelphia, Los Angeles, New York / New Jersey, and the San Francisco Bay Area (both San Francisco and San Jose rail systems). Heavy and light rail lines in these areas form unified rail transit networks, and the presence of heavy rail is expected to significantly influence system-level benefits and costs. Heavy rail requires substantially higher capital and operating expenses than light rail; simultaneously, it might generate higher system-level revenues and other benefits. Systems in southern New Jersey (River
Line) and northern San Diego County (Sprinter), officially designated by Federal Transit
Administration as light rail systems, are operated with light diesel-propelled railcars and their operational characteristics resemble suburban commuter rail rather than light rail (those systems were classified under the light rail category mainly for the purpose of being exempt from restrictive railroad safety regulations). Streetcar or heritage trolley systems (Kenosha, Little
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Rock, Memphis, New Orleans, Tampa) were also eliminated from the study. In the cities that operate both light rail and streetcars/trolleys (i.e. Portland), only the light rail performance and economic indicators are considered under the rail category. After the discussed calibrations, 14 cities operating light rail services remained in consideration. One more city, Seattle, has been removed from the case set for several reasons: 1) Light rail operations in Seattle commenced in late 2009, so there would be only two observations from Seattle included in the analysis 2)
Seattle operates a large fleet of electric trolleybuses, which have a different array of benefits and costs when compared to diesel motorbuses 3) The complex structure of transit governance in the
Puget Sound area with three major agencies complicated the data collection and interpretation process.
To increase the study’s validity, and to diminish the effects of temporary fluctuations in the input data, the author attempted to extend the study over a longer timeframe. The initially assumed 20-year period of study (1992 – 2011) was eventually reduced to eleven years (2001 –
2011) due to unavailability of data for many of the relevant categories during the pre-2001 period. The last year of analysis, 2011, was also determined by data availability; during the data collection phase, 2011 was the last year for which the National Transit Database has provided a full set of statistics. In the case of systems that commenced regular rail service after 2001, the observations were made only for the years in which light rail was operating.
1.5.1 Basic Characteristics of Case Transit Systems
The adjustments of the study’s scope discussed previously eventually left 13 cases of metropolitan areas operating bus and light rail in consideration of the study. Table 1.2 presents the final selection of metropolitan transit systems that were selected for the study, and provides basic performance statistics for each of those systems. As a reminder, several other economic
9 and performance indicators of these transit systems and areas served by them were already presented in Table 1.1.
Table 1.2 Study’s Case Multimodal Transit Systems and their Selected Characteristics, 2010
Year Light Rail Total length of Light Rail System-wide Light Rail System-wide opened light rail Boardings per Boardings per Passenger Passenger network (mi) Revenue Mile Revenue Mile Miles per Miles per Revenue Mile Revenue Mile Buffalo 1984 7 6.6 2.4 17.2 8.0 Charlotte 2007 5 4.0 2.0 21.1 9.9 Dallas 1996 49 3.6 1.7 25.4 8.9 Denver 1994 36 2.5 2.1 17.5 11.4 Houston 2004 9 11.8 1.8 26.8 11.0 Minneapolis 2004 15 5.2 2.8 27.5 13.9 Phoenix 2008 22 4.6 1.9 33.0 8.7 Pittsburgh 1903 / 1984 24 3.8 2.2 18.0 10.4 Portland 1986 56 5.2 3.2 25.6 14.1 Sacramento 1987 37 3.8 2.1 20.3 10.4 Salt Lake 1999 20 4.1 1.8 17.6 9.4 San Diego 1981 51 3.9 2.9 24.1 13.2 St. Louis 1993 48 2.7 1.7 23.5 10.3 Source: FTIS (2014)
The study focuses on all fixed-route bus and light rail services operating in a particular metropolitan area. Table 1.3 presents a full list of transit agencies from respective metropolitan areas that were considered by this study. In some areas, bus services are facilitated by more than one transit agency. In such cases, all agencies that report their ridership and performance statistics to the National Transit Database (NTD) are included in all analyzed statistics.
Several very small and rural agencies that do not report data to NTD were omitted by the study, however, their service volume and ridership is negligible in the metropolitan-wide context. Simultaneously, in some cases statistics for service provided by several subsidiaries are reported to NTD by a one, single agency.
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Table 1.3 Full List of Transit Agencies Included in the Study
City, Agency NTD ID City, Agency NTD ID
BUFFALO PITTSBURGH
Niagara Frontier Transportation Authority 2004 Port Authority of Allegheny County 3022
Beaver County Transit Authority 3023
CHARLOTTE Westmoreland County Transit Authority 3044
Charlotte Area Transit System 4008 Mid Mon Valley Transit Authority 3061
Concord Kannapolis Area Transit 4167 Southwestern Pennsylvania Commission 3078
Western Piedmont RTA (dba Greenway Public Transportation) 4172 Fayette Area Coordinated Transportation 3087
City of Washington 3101
DALLAS
Fort Worth Transportation Authority 6007 PORTLAND
Dallas Area Rapid Transit 6056 Tri-County Metropolitan Transp. District 0008
Denton County Transportation Authority 6101 Clark County Transit (dba C-Tran) 0024
South Metro Area Regional Transit 0046
DENVER
Regional Transit District 8006 SACRAMENTO
Sacramento Regional Transit 9019
HOUSTON Yuba-Sutter Transit Authority 9061
Metropolitan Transit Authority of Harris County 6008 Yolo County 9090
Island Transit 6015 City of Fairfield - Fairfield and Suisun Transit 9092
Gulf Coast Center (Connect Transit) 6082 Roseville Transit 9168
Fort Bend County Public Transportation 6103 Placer County Transit 9196
City of Elk Grove (dba e-tran) 9205
MINNEAPOLIS City of Folsom 9220
Metro Transit 5027
Metropolitan Council 5154 SALT LAKE CITY
Utah Transit Authority 8001
PHOENIX
City of Phoenix (Valley Metro) 9032 SAN DIEGO
City of Glendale 9034 San Diego MTS 9026
City of Scottsdale 9131 North County 9030
Maricopa County STS 9132 San Diego Trolley, Inc. (LRT Operator) 9054
Sun Cities 9135 SD Regions Transp Serv 9095
Regional PTA (Valley Metro) 9136 MTS Contract Services 9185
Peoria Transit 9140 National City 9189
City of Tempe (Valley Metro) 9172 Chula Vista 9193
Valley Metro Rail (LRT Operator) 9209
ST. LOUIS
Madison County Transit 5146
Bi-State Development Agency (dba Metro) 7006
Notes:
*) NTD ID: National Transit Database Identification Code
Major agencies (operating light rail and majority of the bus service) are indicated in bold (in Phoenix and San Diego there is no single major agency). Not all listed agencies operated throughout the entire period of analysis.
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Figure 1.1 presents schematic layouts of the metropolitan transit systems selected for analysis. The purpose of Figure 1.1 is to give a general idea of the network layout and the role light rail plays within specific networks. Some of the outlying portions of particular systems are not captured by the diagrams; additionally, due to issues with data availability, some of the routes operated by minor, suburban agencies are not presented as well as noted on the maps.
Buffalo Charlotte
Dallas Denver
Houston Minneapolis
Figure 1.1 Schematic Diagrams of Study’s Case Regional Transit Systems, 2011
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Phoenix Pittsburgh
Portland Sacramento
San Diego Salt City Lake
LEGEND St. Louis
Source: Transit Agency Data (2014)
Figure 1.1 – continued
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1.5.2 Outline of the Analyzed Transit Systems
The following paragraphs provide a brief overview of the thirteen case transit systems,
putting particular attention to their characteristics relevant for this study, such as network
structure, role of light rail within the network, and the character of regional coordination of
transit services. More detailed analysis of ownership and managerial structures of the case
systems are provided in Chapter 4. The system descriptions refer to the period of analysis, and
therefore, ignore any expansions and other changes made after 2011.
Buffalo transit system includes a single light rail line, connecting the city center and northern suburbs. The downtown section runs in street alignment; the remaining part of the line is located underground. The metropolitan system includes city networks in Buffalo and Niagara
Falls, as well as suburban routes running between these two major cities, as well as serving several other outlying townships. All transit services in the metro area are operated and coordinated by a single agency, Niagara Falls Transportation Authority (NFTA).
Charlotte system includes a single light rail connecting the central city with southern
outskirts. The entire line runs on its own right-of-way, being once a railroad corridor. A
relatively dense bus network, operated by Charlotte Area Transit System (CATS), serves the
region’s core county, Mecklenburg. CATS also provides very limited, peak-only service to
neighboring counties. Few small, local transit systems operate independently from CATS in the
outlying counties.
Dallas operates the largest light rail system in the US by length. During the last year of
analysis (2011), it consisted of three major lines, which have intersected Dallas and also served
some of the municipalities surrounding the city of Dallas proper. All lines share a common
section in the central city, running through a pedestrian mall. Outside of the CBD, majority of
14 the lines utilize fully segregated right-of-way, what includes multiple elevated structures and a short underground section. The rail system is a part of the Dallas Area Rapid Transit (DART) network, which serves primarily the Dallas County and some portions of the adjacent counties.
Two other significant transit networks serve the region, specifically, “The T” (Forth Worth
Transit Authority) operating a bus network in Tarrant County, and the recently created Denton
County Transportation Authority operating local networks in the cities of Denton and Lewisville.
Interestingly, both FWTA and DCTA bus networks are not directly connected with each other, nor with the DART bus network; simultaneously, one commuter rail line links Dallas and Fort
Worth (“Trinity Rail Express” – TRE), and another, separate line links one of the outlying
DART light rail stations with Denton and Lewisville (“A-Train”). The three core agencies are not coordinated by any single planning entity; however, they have established some patterns of collaboration. The TRE rail service is a joint venture of DART and FWTA, while A-Train operates under DCTA authority. The three agencies have created a partially unified fare system; a “regional” fare (at a double cost of local DART fare) serves as a regional ticket, accepted by all three agencies. Few other small transit agencies operate in the Metroplex, but their service is limited to few local, partially demand-respond routes, making up less than half percent of the total area’s service volume.
Denver’s rail network consists of two branches originating in the central city, which merge south of downtown, and they split up again into two long suburban legs, reaching the southern outskirts. The CBD section runs in a street alignment; the remaining parts are fully segregated and share the right-of-way with a freight railroad or an interstate highway. Denver metro area also serves by extensive bus network, which includes both local and regional routes,
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covering virtually all urbanized parts of the region. The entire system is operated and managed
by a single entity, Regional Transportation District (RTD).
Houston transit system includes a single rail line, originating from downtown and running in street alignment or street medians towards the southern inner suburbs. There are no underground or elevated sections on Houston’s light rail system, although the train movement is not impeded by other road users and most of the line can be considered as running its own right- of-way. Bus services in the Houston region include local routes serving primarily the area’s core and express, peak routes linking downtown Houston with remote municipalities. Metropolitan
Transportation Authority of Harris County (METRO) acts as the region’s primary transit agency, however, as already indicated by the name, its services are focused primarily on the core Harris
County. Selected portions of the surrounding counties are served by other, local agencies.
Minneapolis light rail system consists of a single line, originating from a street section in
the CBD, and then continuing southbound towards the suburbs, passing through the airport
terminal on its way, and terminating at a large mall. The outer section runs along a major
highway, or on its median. A large bus network serves most of the urbanized areas within the
region. All transit services are operated under the umbrella of Metro Transit, which facilitates
unified fare and route numbering system. Metro Transit maintains a majority of the area’s bus
services, while the remaining services in some of the outlying counties are operated by separate,
local transit agencies and by private contractors.
Portland transit system includes an extensive light rail network, which consists of the major line stretching between the far western and eastern outskirts, and three additional legs located east of the CBD, including an airport branch. In the central city, light rail forms two loops running in a street alignment; the remaining parts of the network utilize their own rights-
16
of-way, running through former railroad or highway corridors. The core agency, Tri-Met,
operates a vast majority of transit services on the Oregon side of Portland metro area. The
Washington side is served by a separate entity (C-Tran), which runs a local bus system, as well
as several bus routes connecting to Portland CBD and to light rail stations. TriMet and C-Tran
cross-honor most of their fare types and provide seamless connections between their local
networks.
Phoenix’s single light rail line connects the inner suburbs located northwest of the CBD
with the city of Mesa, located in the eastern part of the metropolitan area. Except for the crossing
over Salt River, the line runs entirely on a highway median or in a street alignment. The unified
bus network, operated under the umbrella of Valley Metro, serves all urbanized portions of the
Phoenix metro.
Pittsburgh’s light rail originated from a legacy streetcar and interurban system. Contrary to all other case metropolitan regions, urban rail transit in Pittsburgh operated without interruptions since the early 20th century; however, substantial improvements were made to the old, traditional rail lines during their transformation into the current, modern light rail system.
While that might raise some concerns about the uniformity of the Pittsburgh case with all other cities evaluated by the study, the majority of the modern Pittsburgh rail infrastructure was built from zero, and its capital costs were comparable to the costs of the completely new rail networks constructed in the analogical period (Baum-Snow & Kahn, 2005; Pittsburgh Post-Gazette, 1984).
Currently, the Pittsburgh rail system consists of a downtown underground section, which splits into two suburban branches after leaving the central city, running mostly on its own, exclusive right-of-way. The system still features some remnants of the streetcar era, including street sections with frequent stops, and on-board, manual fare collection (except for the few most
17
popular stations). Pittsburgh also operates an extensive bus rapid transit network, which consists
of three fully separated busways. One of those busways serves the northeastern portions of the
city, lacking any rail service, while the two other ones duplicate the southern legs of the light rail
system. Transit management in Pittsburgh is strongly partitioned. The core agency, Port
Authority (PAT) limits its operations to the core Allegheny County. Surrounding counties
maintain their own, unconsolidated agencies.
Sacramento’s light rail system consists of a north-south line and an east-west line linking
the suburbs with the CBD. All suburban branches run in railroad corridors, while the downtown
section is placed in a Type B street alignment. Similarly as in Pittsburgh, the transit network in
Sacramento metro area is operated by multiple agencies that are not coordinated by any
“umbrella” authority. The core agency, Sacramento Regional Transit, limits its operating area to
the northern and central parts of Sacramento County. Several other, independent agencies are
present in the region, in most cases operating local bus services within one of the suburban
municipalities, as well as regional routes connecting to Sacramento CBD or to one of the
outlying light rail stations.
San Diego’s light rail network is being formed by three branches originating in the CBD.
The southern line runs along the Pacific coast reaching the border crossing with Mexico. Two other branches serve northern and northeastern suburbs. In the central city rail lines form a loop running in Type B street alignments, the suburban sections utilize their own right-of-way, partially running in former railroad corridors. The entire regional transit network is coordinated by a single entity, Metropolitan Transit System (MTS), which performs planning and managerial functions and maintains a unified regional fare system. Particular services are facilitated by several agencies, including San Diego Trolley, North County Transit District and few others.
18
Salt Lake City’s light rail network consists of a main line running from the CBD to the
southern suburbs. Additional branch links the city center with the university campus. In August
2011, two additional suburban branches were added to the main north-south line. The downtown
portion of the network and the university line run on Type B street alignments, the suburban
section utilize former railroad right-of-way. Entire transit network in the Wasatch Front area
(including Salt Lake, Ogden-Clearfield, and Provo-Orem metropolitan statistical areas) is owned,
operated and maintained by a single agency, Utah Transit Authority (UTA).
St. Louis’ light rail network includes long line stretching from the city’s main airport
(located northwest) to the far eastern outskirts. Additional line branches off in the central city and reaches the southwestern suburbs. The entire network utilizes its own, segregated right-of- way, including a tunnel under the CBD and several sections running in former or current railroad corridors. Most of the transit service in the St. Louis metro area is facilitated by the Bi-State
Development Agency (doing business as “Metro”); one of the counties (Madison) owns its separate agency running local intra-county routes and commuter routes connecting to St. Louis
CBD. Both systems maintain a ticket cross-honoring policy. Fixed-route transit does not operate outside of the region’s core jurisdictions.
1.5.3 Socioeconomic Characteristics of Selected Case Metropolitan Areas
Transit literature commonly recognizes two major categories of transit performance determinants: internal, related to the service parameters and other features controlled directly by planners, and external, including socioeconomic characteristics and spatial forms of the served areas (Taylor & Fink, 2003). While this study focuses primarily on the internal factors and analyzes their influence on specific transit outcomes, it is still important to consider the external characteristics of the served areas and understand their possible influence on ridership patterns
19 and economic outcomes of transit investments in the case cities. This section provides a brief overview of the socioeconomic situation in the thirteen analyzed metropolitan areas. Table 1.4 presents selected population and economic indicators that illustrate the transit operational setting.
Table 1.4 Key Demographic Characteristics of the Case Metropolitan Statistical Areas, 2010
Population characteristics Economic characteristics (Metro area) Core City 2000-2010 Metro Area 2000-2010 Core County Un- Median Zero-Vehicle Travel Time Population Change Population Change Density employment Income Households Index *) (000s) (000s) (persons / rate (000s) rate sq mile) Buffalo 261 -11% 1,136 -2% 749 9% $46 13% 1.17 Charlotte 731 35% 1,758 29% 1,690 12% $50 6% 1.20 Dallas 1,198 1% 4,236 31% 2,614 8% $54 5% 1.25 Denver 600 8% 2,543 30% 3,895 9% $59 7% 1.27 Houston 2,099 7% 5,947 25% 2,312 9% $54 6% 1.26 Minneapolis 383 0% 3,280 17% 2,142 7% $62 8% 1.21 Phoenix 1,446 9% 4,193 45% 415 10% $58 7% 1.18 Pittsburgh 306 -9% 2,356 -2% 1,645 8% $47 11% 1.24 Portland 584 10% 2,226 27% 1,584 11% $53 9% 1.28 Sacramento 466 15% 2,149 22% 1,428 13% $56 7% 1.20 Salt Lake 186 3% 1,124 24% 1,279 8% $57 5% 1.14 San Diego 1,307 7% 3,095 13% 686 11% $60 6% 1.18 St. Louis 319 -8% 2,813 4% 2,234 10% $51 8% 1.14 Source: US Census (2014)
As indicated by Table 1.4, there are some remarkable variations in socioeconomic characteristics across the thirteen case metropolitan regions. First four columns clearly indicate differences in demographic situation between the Midwest cities (Buffalo, Pittsburgh, and St.
Louis), where the population is increasing very slowly or even declining and the Western U.S., where some metropolitan areas observed a 30% growth rate during the study’s timeframe. The statistics presented in Table 1.4 also reflect the phenomenon of suburbanization: metropolitan areas grow at a higher pace than their core cities, and the core cities usually represent less than a third of the total metropolitan population, the net growth in the suburbs is much higher than in the core city. As discussed later in the text, the rapid expansion of the outlying areas, and
20 simultaneously the declining role of the core city, have significant consequences for transit planning.
The four right columns of Table 1.4 present several indicators of economic conditions in the analyzed regions, as well as the travel time index (TTI), which reflects the level of congestion (higher TTI value means more congestion). Higher unemployment, lower median income, and higher ratio of zero-vehicle households clearly reflect the stagnation of Midwest cities. On the other hand, some of the western metropolitan areas are also characterized with higher-than-average unemployment (e.g. Sacramento, San Diego), or lower income (Dallas,
Houston, Portland).
In general, the referred numbers indicate some differences in population and economic characteristics between the analyzed areas, although they also reveal several similar trends observable within majority or even all case cities (e.g. higher pace of growth in the suburbs). As indicated by previous research focused on bus and light rail systems (Brown and Thompson,
2012; Jaroszynski and Brown, 2014), inconsistencies in socioeconomic settings are not necessarily powerful determinants of transit outcomes. Nevertheless, the possible influence of external setting variations on the observed transit outcomes has been considered and examined in the further parts of the study.
1.6 Dissertation Outline
The dissertation manuscript includes five chapters: introduction, conclusion, and three core chapters discussing each of the three major stages of the study. The first chapter introduces the research problems and briefly discusses the cases selected for investigation. The following three chapters are structured along the three key stages of the study. Chapter 2 describes the
21 benefit-cost analysis, which has estimated the amount of net social benefits generated by the case systems. Chapter 3 includes a statistical regression determining the relationship between network planning decisions adopted by the analyzed systems and the net benefits of those systems.
Additional route-level economic analysis of particular route structures is also discussed. Chapter
4 responds to the second major research question, by analyzing the statistical influence of service ownership and governance structures on the economic outcomes of transit. Chapter 4 also includes a brief policy analysis, which overviews the emergence of particular ownership and governance philosophies across the analyzed metropolitan areas. Each of the three core chapters includes a discussion of the theoretical background and a literature review relevant for the specific stage. The last, fifth chapter, synthesizes and summarizes the study’s findings and draws implications for further research.
22
CHAPTER TWO
THE BENEFITS AND COSTS OF MULTIMODAL (BUS AND LIGHT RAIL) TRANSIT INVESTMENTS AND OPERATIONS
The major purpose of this study is to evaluate the influence of specific planning decisions on the economic outcomes of bus and light rail systems. The economic outcomes were assessed with the consideration of non-direct social benefits and the annualized capital costs. Net benefits
- the difference between all profits (benefits, revenues) and costs – served as the ultimate measure of economic outcomes. This chapter discusses the estimation of net benefits. The relationship between net benefits and internal planning factors is investigated in further stages of the study. This chapter begins with a literature review, focusing primarily on the multiple-case transit benefit-cost analyses, which has adopted a similar, cross-sectional design. Next, it discusses the methodology adopted by this study, and finally, presents the benefit-cost analysis results and attempts to draw some preliminary conclusions regarding the values of net benefits estimated for particular case systems.
2.1 Literature Review
There are multiple ways of assessing the economic outcomes of mass transit investments.
The simplest financial analysis, comprising a balance sheet containing direct revenues and expenses, usually reveals negative results, as costs exceed the direct revenues. Virtually all public transit investments across the world (as well as many other transportation investments) generate net losses, regardless of ridership, socio-economic setting, costs of using alternative modes, etc. However, these investments could also have significant non-direct impacts on the
23 society and economy. Therefore, scholars and practitioners generally agree that economic assessments of transit investments should focus not only on direct cash payables and receivables, but they should also consider the possible non-direct impacts benefiting (or affecting) the riders and the society. A typical assessment of transit outcomes considers at least at some degree the non-direct effects of the transit investment. There are several methods of assessing non-direct effects, and the literature discusses many possible categories of additional benefits (or costs) generated by transit operations. Among these methodological frameworks, the benefit-cost analysis (BCA) seems to be the best fit for the purposes of this study (TCRP, 1998; Litman,
2012).
The benefit-cost analysis is commonly used for assessing public investments that are not oriented on generating direct profits. BCA allows evaluations of the social benefits of the investment and estimates of the actual, annualized costs of capital. Social benefits play an important role in justifying planning decisions, including the development of transit systems
With the purpose of identifying best practices for conducting multiple-case transportation benefit-cost analysis, as well as analyzing the findings of previous transit BCA studies and their potential relevance for this study, the author has carefully reviewed several recent multiple-case benefit-cost analyses. The review also focuses on literature discussing the theoretical foundations of transit BCAs and examines benefit and cost categories that could be included in this study.
The following paragraphs discuss the aforementioned four studies and elaborate on particular categories of benefits and costs commonly considered by transit BCAs.
2.1.1 Recent Multiple-Case Analyses of Transit Benefits and Costs
The author has identified several articles and reports discussing the overall assumptions of a transit benefit-cost analysis (Banister, 2007; Banister and Thurstain-Goodwin, 2011; Currie,
24
2011; FTA, 2009; Horwtiz and Beimborn, 1996; Litman, 2012; Nelson, 1997; TCRP, 1996,
1998, 2000; UITP, 2009; Williges and Mahdavi, 2008), as well as four recently conducted multiple-case benefit-cost analyses of rail and multimodal transit investments: (Harford; 2006,
Gordon and Kolesar; 2011, Guerra, 2010, Winston and Maheshri, 2007). Based on the referred literature, several common traits of a transit benefit-cost analysis could be identified. Apart from direct revenues and expenditures, transit BCA typically considers the user benefits and the overall effects of the evaluated investment on society. In the case of transit investments, the user benefits are related to travel time savings and other positive impacts experienced by the riders.
Estimations of these benefits are grounded on the concept of consumer surplus, which is the aggregated difference between the consumer’s willingness-to-pay for a specific service and the actual price. The society effects generally include reductions or increases of the monetized negative external impacts of transportation (externalities), such as air contamination, accidents, and noise, and the monetized negative impacts of traffic congestion, which are discussed in detail later in this chapter. Simultaneously, opposite to some other types of economic analyses, the
BCA does not assess external economic impacts, such as increases in land value or additional revenues gener businesses benefiting from the new investment. Public investments are indirectly funded by the entire society (primarily in the form of taxes) and one of their primary goals is to provide benefits for that society. From such perspective, external economic impacts are less relevant and therefore are not included in the BCA (FHWA, 2003). The following paragraphs provide an outline of particular methodological approaches adopted by the four multiple-case analyses and other similar studies.
Gordon and Kolesar (2011) investigated whether the non-direct benefits balance the capital and operating costs of all 42 U.S. light, heavy, and commuter rail systems. Non-direct
25 benefits considered by the study included only the reductions in negative automobile externalities. To measure the externalities, the authors assumed that on average 25% of patrons on the rail systems are new users, while the remaining 75% switched from buses. Those assumptions were grounded on Federal Transit Administration evaluations of recently opened rail systems. Basing on that assumption, they estimated the non-direct benefits by multiplying the 25% of rail ridership by a predefined value of automobile externality ($0.20 per mile), averaging the externality values estimated by previous literature for two cities (Los Angeles and
Washington, D.C.). On the cost side, the analysis considers current operating costs obtained from agency statistics and capital costs taken from the 1992 – 2008 FTA statistics for systems opened after 1992, and from various other sources for the remaining systems (including other papers, news releases etc.). Capital costs are annualized at a discount rate of 7% and an average lifecycle of 30 years is assumed (the foundations of the capital cost annualizing procedure are discussed later in this chapter). The results of the study indicate that adding the reductions in automobile externalities to the direct fare revenue generally have an insignificant influence on the overall economic balance of the rail investments, which remains to be negative for all analyzed cases.
Still, according to the Gordon and Kolesar estimations, several light rail systems yield substantially higher non-direct benefits (exceeding 10% of the fare revenues) than their counterparts do.
Harford’s benefit-cost analysis (2006) considered two non-direct benefit categories: reductions in externalities (including congestion costs and environmental impacts) and user benefits. The study considers all modes and the case set includes 81 U.S. metropolitan areas (the author selected all areas for which congestion statistics were provided by the Urban Mobility
Report and simultaneously combined area-level transit statistics were available from National
26
Transit Database; in general, these were all metro areas with population larger than 500,000.
Non-direct user benefits were estimated utilizing demand elasticity of -0.2 (elasticity allows to estimate the consumer surplus, as explained later in this chapter) and average congestion savings per transit passenger mile equal to $13.45 (as estimated by the 2004 Urban Mobility Report).
Harford also conducted a very simplified estimation of the capital costs. First, he estimated the average bus modal share, basing on the statistics for bus and rail systems serving the 50 largest U.S. metropolitan areas. Then, for each bus and rail system, he multiplied the bus modal share (equal to 0.56) by the total operating costs to determine the average bus operating costs of those systems. The capital costs are estimated by multiplying the bus operating costs by
1.2. The remaining 44% of operating costs are assumed to be the rail costs; they were multiplied by 1.4 to determine rail capital costs. For cities without rail systems, obviously all operating costs are considered as bus costs. Harford also considers an additional costs category; the social costs of transit-dedicated taxation. It is estimated by multiplying by 1.3 the amount of costs not covered by the fares (total costs minus fare revenue). The analysis results are given in the form of a benefit-to-cost ratio. The ratio exceeds unity only for a few of the cities operating light rail.
In two other cases, the ratio falls into the range between 0.75 and 1.
Guerra (2010) performed a simplified benefit-cost analysis of all U.S. heavy and light rail systems, comparing system-level operating costs with the use benefits. The author used three thresholds of fare elasticity (-0.3, -0.6, -1.0) for the consumer surplus estimations. He also assumed relatively long, 50-year lifecycle of the capital (Federal Transit Administration suggests using 30-year lifecycles for rail transit financial forecasts) and a 2.2% discount rate. Guerra’s study does not consider externalities and other possible benefits. Tthe study has estimated that with the assumed fare elasticity of -0.3, only two heavy rail systems (the New York subway and
27
Bay Area Rapid Transit in San Francisco area) are generating net social benefits; however, few light rail systems appear to generate relatively low net losses (below $2 per passenger trip). As noticed by Guerra, if externalities would be also accounted, the net benefits of those systems might have assessed as positive.
Winston and Maheshri (2007) assessed the net benefits of 25 light and heavy rail systems, using an econometric framework, slightly different from the previous three papers.
Their analysis begins with a comprehensive estimation of transit demand and cost functions.
Passenger miles and total costs are the dependent variables, and multiple categories of variables reflecting network and service characteristics, and the socio-economic conditions serve as their determinants. The demand and cost functions are then pulled into the consumer surplus equation, and the surplus is estimated for each of the case systems. Next, authors estimate the savings generated by reductions in congestion costs, using a congestion cost function based on traffic volume and average delay. They mention other possible externality categories, but they decide to omit them in their estimations, claiming that there is no valid evidence for any actual monetized benefits related to externality reductions. The authors estimated that all of the analyzed urban rail systems generate negative net benefits.
Table 2.1 summarizes the results of the four multiple-case benefit-cost analyses referred to in the previous paragraphs. As indicated by the discussion, each of the studies had adopted slightly different approaches to the analysis, and included inconsistent assumptions with regards to cost estimations and savings provided by reductions in externalities. Additionally, the discussion provided by the referred studies clearly indicated that their authors were attempting to support specific hypotheses regarding the inefficiency or feasibility of rail investments. Guerra’s study intended to provide evidence that supports rail spending and contradicts the frequent
28
critique of transit spending, while Gordon and Kolesar, as well as Winston and Maheshri seem to
have a negative attitude towards rail investments. These different approaches and research goals
yield substantial differences in the estimated net benefits, as indicated by Table 2.1.
Table 2.1 Results of the Four Discussed Transit Benefit-Cost Analyses
Winston & Maheshri Gordon & Kolesar Guerra 2010 Harford 2006 Study: 2007 2011 Net benefit Net benefit Net benefit per passenger Annual net Annual net per per Units: mile, automobile Benefit-to-cost ratio benefits benefits passenger passenger externalities ignored [$ 000,000s] [$ 000,000s] mile mile Mode: Only rail Bus & rail Only rail Only rail Additional elasticity - elasticity - "low "high parameters: 0.3 0.6 estimates" estimates" 2008 data 2002 data 2000 data 2008 data Buffalo -$2.99 -$3.23 0.36 0.63 -51.5 -$3.34 -117.55 -$8.04 Charlotte -$1.48 -$1.59 0.86 1.50 n/a n/a -45.21 -$3.46 Dallas -$0.74 -$0.81 0.58 1.04 -411.8 -$6.84 -356.14 -$2.35 Denver -$0.23 -$0.37 0.69 1.21 -254.4 -$9.01 -143.52 -$1.07 Houston no data no data 1.32 2.26 n/a n/a -38.93 -$1.31 Minneapolis -$0.24 -$0.37 0.76 1.26 n/a n/a -69.82 -$1.14 Phoenix no data no data 0.76 1.30 n/a n/a n/a n/a Pittsburgh -$2.31 -$2.48 0.46 0.82 -122.4 -$3.68 no data no data Portland -$0.40 -$0.53 0.87 1.48 -199.9 -$1.42 -284.02 -$1.47 Sacramento -$0.52 -$0.65 0.64 1.10 -96 -$2.09 -123.38 -$1.44 Salt Lake -$0.36 -$0.48 0.64 1.11 no data no data -102.08 -$1.44 San Diego -$0.21 -$0.34 1.22 2.03 no data no data -166.75 -$0.81 St. Louis no data no data 0.62 1.11 -154 -$1.62 no data no data “n/a”: system did not yet operate in the year of analysis.
Notes: The original results obtained from Winston &Maheshri and Gordon &Kolesar papers are presented in left sub-columns. The right columns include the value of net benefit per passenger mile, that is, the same variable as used by Guerra (passenger miles were obtained from the NTD) Harford does not estimate the explicit value of net benefit in his study, therefore, the results of his study cannot be discounted in the same way.
The inconsistencies across the four available multiple-case analyses, reflected by Table
2.1, motivated the author to develop his own benefit-cost analysis design, which synthesizes the previous analysis and re-evaluates the assumptions made with regards to particular benefit and cost categories. The author also decided to re-estimate the net benefits, instead of incorporating the results of the existing analysis into this study. 29
2.1.2 Common Elements of a Multiple-Case Transit Benefit-Cost Analysis
This section summarizes the common categories of transit benefits and costs discussed by
literature, basing on the literature cited in the previous section, particularly on the four multiple-
case studies evaluating the benefits and costs of transit systems serving medium- and large-sized
U.S. metropolitan areas. It also explains the methodology used for estimating these benefits and
costs.
Direct operating revenue: fare revenue and other revenue generated by transit operations
(excludes subsidies provided by governmental entities). These revenues are available from transit
agency financial statements and statistical reports.
Consumer Surplus (users’ benefits): aggregated difference between the amount that the users (riders) are willing to pay for the transportation service and the amount they actually pay.
Consumer surplus reflects the monetized user benefits provided by travel time savings and other positive aspects of using transit.
Figure 2.1 presents the idea of consumer surplus. The demand curve represents the ridership at a certain cost of service (fare). Increasing the fare will naturally decrease the ridership, and at some point, there will not be any more riders willing to use the service
(ridership will be zero). That point is represented on the graph by the interception of the fare axis by the demand curve.
To estimate the surplus using the simplified demand curve presented by Figure 2.1, it is sufficient to calculate the triangle’s area using the common formula (width x height x 0.5, where width = current ridership and height = maximum fare – current fare). Current ridership and fare are given, and the maximum fare could be estimated based on the elasticity value. Elasticity is the percentage change in consumption (in this case reflected by ridership), resulting from a one
30
percent change in the price (reflected by the fare). In transportation demand analysis, elasticity
values for certain types of transit services (differentiated by service area and population, mode,
peak/off-peak period of the day, etc.) are usually obtained from comprehensive studies that
analyzed changes in demand for multiple transit systems (Balcombe, 2004, Litman, 2004).
Figure 2.1 Graphic Illustration of Consumer Surplus Estimation Methodology (Source: own work, based on Guerra, 2010 and FDOT-FRA, 1997)
Note: For the purpose of consumer surplus estimations, the demand curves are usually assumed linear. In fact, the demand curves are typically compounds of several curves. Such simplification leads to a moderate underestimation of the surplus, but it simultaneously removes the possible overestimation of the surplus for the consumers having the highest willingness-to-pay for transit services (Guerra, 2010).
Each particular system has its own elasticity, and the literature provides only a range of typical elasticities for that type of systems. Therefore, the decision on adopting specific elasticity value for the analysis is associated with some uncertainty. Elasticity selection criteria are further discussed in section 2.2. The selected elasticity value is pulled into the formula defining the elasticity of a linear demand function, as presented by Equation 2.1.
31
/ ∆ / 2.1 / ∆ /
Where q – current ridership, q – current fare, q = 0 (ridership drops to zero after the price reaches p ), p – unknown maximum price (Sihna and Labi, 2007)
After transforming the above equation:
Where is the difference between the maximum and current fare, and simultaneously, is
the height of the surplus triangle (Fig. 2.1)
The consumer surplus formula for the case illustrated by Figure 2.1 is illustrated by Equation
2.2:
0.5 0.5 2.2
Reduction in negative, external impacts of individual motorization (externalities): BCA
usually assumes that transit attracts some car owners that prefer to ride transit instead of driving.
Consequently, if there would be no transit service available, these riders would switch back to
the roads, increasing congestion and negative impacts of motorization on health (air pollution
and accidents). The difference in social costs of congestion and health impacts between
hypothetical and actual conditions is considered another benefit of transit.
As already indicated by literature review, there is no single method of estimating the
externalities. Scholars usually base on predefined values of benefits per vehicle mile travelled
(VMT); however, the literature reveals substantial differences in estimating the VMT reductions
influenced by transit, as well as recognizing specific types of externalities. Several
comprehensive sources providing monetization of congestion and externalities costs values are
32 provided by literature, includingng the Urban Mobility Report (2012), Litman (2011(20 ) and Federal
Transit Administration guidelinelines (FTA, 2013). Specific parameters andd methodological assumptions offered by literaturere are discussed later in the chapter.
Operating costs: expenseses required for maintaining everyday operationons (labor, fuel or electricity, fare control, etc.). SimSimilarly, as the direct revenues, these costs are easily obtainable from the transit agencies.
Capital costs: costs of tr transit infrastructure and vehicles. Transit agenencies report their current capital costs, which aree eexpenses on capital made during a particular year.yea However, due to the long lifecycle of transit invinvestments (particularly rail infrastructure), thee BCAsB are usually considering the annualized capitaital cost, which is the total cost divided by the numbernu of years in a lifecycle, adjusted for inflationon and deprecation. The idea of cost annualizationion is illustrated by
Figure 2.2.
Figure 2.2 The Idea of Capitalal CCost Annualization (Source: Own work based on FTATA New Starts (FTA, 2013))
Note: The capital cost for year 202011 is the sum of annualized costs for various typesty of capital that is in use during that year.
The annualized cost is estestimated with the formula presented by Equationn 2.3:
cocost = NPV × i / (1 - ( 1 + i )^(-t) ) (2.3)
33
Where NPV – net present value (in this case, capital expense made in the year of capital acquisition); i–
discount rate, t – capital lifecycle (Source: FTA, 2007).
2.1.3 Other Possible Benefits of Transit
Previous sections outlined the benefit and cost categories that are most commonly included in transit BCAs. The literature discussed several other types of possible transit benefits.
For example, Stokes, McDonald and Ridgeway (2008) conducted an interesting study discussing how a specific rail investment (light rail system in Charlotte) yields reductions in health care expenses. Apart from evaluating the commonly recognized reductions in automobile externalities, they have also investigated the savings in healthcare costs resulting from adopting a healthier lifestyle by persons who decide to use transit instead of driving. The walking distance to a transit stop is usually longer than the access to a car; therefore, transit riders experience more physical activity during their everyday commute. However, the study indicated that monetized value of such benefits are almost insignificant, partially because the authors have assumed quite conservative estimations of the additional walking distance.
Recent reports from the Brookings Institute (Tamer et al, 2011, 2012) indicate that job accessibility is substantially affected by limited transit opportunities, and the low-income population cannot use transit to reach many of the jobs within a reasonable time. Providing convenient transit services eases job access, and reduces the amount of social assistance that has to be allocated for the unemployed or underemployed.
Many more transit benefit categories are discussed by a series of Litman’s publications
(Litman, 2009, 2012, 2014). Selected benefits offered by Litman include: savings in expenses on vehicle purchases, savings in parking expenses, avoided chauffeuring, increases in workers’ productivity, increases in consumer expenditures (related to savings on transportation), and several other categories. One more category mentioned by Litman (2012) and Cervero (1997) is
34 based on changes in property values and other positive effects on the society related to the impact of transit on real-estate and development, e.g. the benefits of living in more livable and accessible communities located around rail stations.
2.2 Categories of Benefits and Costs and their Estimated Values
Presented in this section are the benefit and cost categories selected for analysis. For all categories, the cost and benefits were standardized: the amounts from each category were divided by the amount of passenger miles reported by each system in a particular year. The author decided to utilize passenger miles for standardization rather than unlinked passenger trips
(UPT) for several reasons. First, the UPT metric counts every transfer made within a single, continuous transit journey as a separate trip. Therefore, in the case of systems that provide less direct routes, and simultaneously promote transfers, the trip volume is exaggerated. Second, passenger miles illustrate better the patterns of system’s use. The trip volume might be high, but these could be mostly short trips made within a certain part of the system. They are also more important from the economic perspective; the costs and benefits of carrying a single rider are not constant, and they depend on the distance travelled by the rider. Total values of costs and benefits (before standardization) for all categories are presented in Appendix A.
Basing on the literature outlined in the previous section, the author has decided to perform a simplified benefit-cost analysis, which includes only the three major non-direct benefit categories, direct revenues, and the operating and capital costs. Such approach follows the common BCA design used in multiple-case studies and offered by the literature discussing the foundations of transit BCA, cited earlier in the text. The other possible non-direct benefits, discussed in section 2.1.3, were not included in this study for several reasons: 1) the multiple-
35 case, longitudinal research design would make it complicated to assess some of the benefits offered by literature, even though they seem to be important outcomes of transit investments
(e.g. job accessibility, changes in real estate values). Collecting and processing data necessary for these assessments would increase significantly the time needed for completing the study. The data collection issue could have been resolved with adopting averaged, per-mile parameters for these additional benefit categories; however, that would substantially increase the results’ bias, as each generalization made with regards multiple transit systems, observed throughout a longer period of time increases uncertainty and the risk of inaccuracy. 2) Evidence supporting the importance of some of those benefit types is limited, and some of these benefits are based on idealistic outcomes of transit investments, such as, for example, a substantial reduction in new roadway construction. 3) Some of the savings, considered by Litman as benefits for patrons are not actually benefiting the entire society; and some other ones are actually economic impacts rather than social benefits (e.g. workers’ productivity gains). Overall, further investigation is required to evaluate the actual role of the additional benefit categories offered recently by literature, and to identify methodology for estimating those benefits in particular cities. Such investigation would exceed the scope of this study.
The following paragraphs discuss the benefit and cost categories considered by the study and provide data sources and assumptions made for the estimations. As noticed in Chapter 1, the study considers both the bus and rail modes for each of the 13 study cases, and the analyzed ridership and performance data represent all fixed-route transit services operating in the particular metropolitan area. For all of the estimations, the input data has been adjusted for inflation and converted to 2011 dollars, basing on the Consumer Price Index (Bureau of Labor
Statistics, 2013), unless the specific input variable was already inflation-adjusted.
36
2.2.1 Benefits
2.2.1.1 Direct Revenue
Direct revenue was obtained from the transit agency data, available in the National
Transit Database (FTIS, 2014). Specifically, the revenue was constructed from the “Passenger
Fare Funds” and the “Total Directly Generated Park-n-Ride, Transportation and Non-
Transportation Revenue” statistics. The latter revenue category represents revenue such as park- and-ride fees, advertising contracts, merchandise venue franchising etc., and it is not aggregated by specific modes, as some of those revenues are not associated with a single mode; therefore the direct revenues presented in Table 2.2 are estimated only at the system level.
Table 2.2 Direct Revenues (Transportation and Auxiliary) per Passenger Mile, 2011$
Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $ 0.38 $ 0.20 $ 0.24 2002 $ 0.36 $ 0.26 $ 0.25 2003 $ 0.34 $ 0.19 $ 0.22 2004 $ 0.39 $ 0.22 $ 0.23 $ 0.13 $ 0.28 2005 $ 0.36 $ 0.19 $ 0.21 $ 0.12 $ 0.27 2006 $ 0.36 $ 0.23 $ 0.23 $ 0.14 $ 0.28 2007 $ 0.35 $ 0.34 $ 0.16 $ 0.17 $ 0.15 $ 0.26 2008 $ 0.33 $ 0.26 $ 0.18 $ 0.29 $ 0.12 $ 0.24 2009 $ 0.31 $ 0.46 $ 0.25 $ 0.27 $ 0.17 $ 0.30 $ 0.20 2010 $ 0.37 $ 0.20 $ 0.42 $ 0.22 $ 0.16 $ 0.27 $ 0.24 2011 $ 0.35 $ 0.20 $ 0.29 $ 0.22 $ 0.18 $ 0.27 $ 0.24
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $ 0.22 $ 0.26 $ 0.26 $ 0.22 $ 0.16 $ 0.20 2002 $ 0.26 $ 0.21 $ 0.33 $ 0.25 $ 0.20 $ 0.19 2003 $ 0.29 $ 0.20 $ 0.30 $ 0.23 $ 0.19 $ 0.18 2004 $ 0.29 $ 0.20 $ 0.24 $ 0.23 $ 0.20 $ 0.18 2005 $ 0.28 $ 0.19 $ 0.22 $ 0.22 $ 0.19 $ 0.19 2006 $ 0.28 $ 0.21 $ 0.25 $ 0.22 $ 0.19 $ 0.21 2007 $ 0.26 $ 0.29 $ 0.30 $ 0.16 $ 0.36 $ 0.20 2008 $ 0.30 $ 0.27 $ 0.27 $ 0.22 $ 0.37 $ 0.23 2009 $ 0.31 $ 0.28 $ 0.27 $ 0.29 $ 0.40 $ 0.16 2010 $ 0.33 $ 0.28 $ 0.27 $ 0.26 $ 0.43 $ 0.19 2011 $ 0.40 $ 0.27 $ 0.28 $ 0.24 $ 0.61 $ 0.18
37
2.2.1.2 Consumer Surplus (User Benefits)
Consumer surplus has been estimated with the Equation 2.2, derived in Section 2.1:
0.5
Two elements of the equation are easily obtainable from the transit agency data: is the
“Average Fare” variable (FITS, 2014), and is “Passenger Trips” (FITS, 2014).
As already mentioned in Section 2.1, selecting reasonable fare elasticity is the critical element of the surplus estimation. The literature suggests certain elasticities for specific types of service, city size, mode, etc. The so-called Simpson-Curtin rule proposing that fare elasticity is equal to -0.3 (Curtin, 1968) served as a rule-of-a-thumb for decades; currently, more comprehensive estimations are available. For light rail, literature usually offers elasticities ranging from -0.1 to -0.6; in most cases that range is narrowed to -0.2 to -0.4. The values of bus fare elasticities are usually slightly lower, and vary from -0.15 to numbers below -1. Detailed evaluations indicate that elasticities for larger cities are usually in the higher tier, typically between -0.25 and -0.55 (Balcombe, 2004; Litman, 2004; Pharm & Linsalata, 1991; TCRP,
2004). Considering the referred literature sources, the author decided to select the elasticity values at the midpoints of the above-mentioned ranges: for the rail mode: -0.3; for the bus mode:
-0.4. Table 2.3 presents the consumer surplus estimations for light rail services and for all transit operations in each of the case cities.
2.2.1.3 Congestion Savings
As already noticed in the literature review section, there are several complex studies providing estimations of the savings on congestion costs and externalities, related to transit investments. For the congestion costs, the author found the estimations provided by the Urban
Mobility Report (TAMU, 2012) to be the most reasonable source of congestion savings.
38
Table 2.3 Consumer Surplus (User Benefits) per Passenger Mile, 2011$
Light Rail Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $ 0.32 $ 0.12 $ 0.16 2002 $ 0.33 $ 0.12 $ 0.26 2003 $ 0.31 $ 0.09 $ 0.23 2004 $ 0.38 $ 0.10 $ 0.26 $ 0.15 $ 0.29 2005 $ 0.43 $ 0.09 $ 0.23 $ 0.10 $ 0.18 2006 $ 0.45 $ 0.27 $ 0.20 $ 0.17 $ 0.20 2007 $ 0.39 $ 0.52 $ 0.08 $ 0.19 $ 0.16 $ 0.19 2008 $ 0.35 $ 0.15 $ 0.11 $ 0.20 $ 0.22 $ 0.18 2009 $ 0.31 $ 0.20 $ 0.12 $ 0.21 $ 0.31 $ 0.25 $ 0.09 2010 $ 0.33 $ 0.23 $ 0.14 $ 0.19 $ 0.29 $ 0.23 $ 0.13 2011 $ 0.37 $ 0.18 $ 0.11 $ 0.19 $ 0.27 $ 0.20 $ 0.13
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $ 0.23 $ 0.16 $ 0.23 $ 0.12 $ 0.20 $ 0.13 2002 $ 0.26 $ 0.15 $ 0.47 $ 0.16 $ 0.22 $ 0.11 2003 $ 0.26 $ 0.16 $ 0.20 $ 0.14 $ 0.20 $ 0.11 2004 $ 0.27 $ 0.15 $ 0.19 $ 0.12 $ 0.20 $ 0.10 2005 $ 0.28 $ 0.18 $ 0.19 $ 0.12 $ 0.18 $ 0.13 2006 $ 0.26 $ 0.19 $ 0.19 $ 0.11 $ 0.17 $ 0.14 2007 $ 0.23 $ 0.20 $ 0.20 $ 0.11 $ 0.17 $ 0.16 2008 $ 0.26 $ 0.20 $ 0.20 $ 0.17 $ 0.18 $ 0.14 2009 $ 0.32 $ 0.20 $ 0.21 $ 0.19 $ 0.15 $ 0.14 2010 $ 0.28 $ 0.21 $ 0.21 $ 0.22 $ 0.21 $ 0.15 2011 $ 0.28 $ 0.22 $ 0.22 $ 0.21 $ 0.21 $ 0.14
All Fixed-Route Services Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $ 0.29 $ 0.11 $ 0.12 2002 $ 0.28 $ 0.10 $ 0.13 2003 $ 0.27 $ 0.09 $ 0.14 2004 $ 0.31 $ 0.10 $ 0.14 $ 0.09 $ 0.18 2005 $ 0.29 $ 0.09 $ 0.13 $ 0.09 $ 0.18 2006 $ 0.28 $ 0.15 $ 0.13 $ 0.09 $ 0.19 2007 $ 0.27 $ 0.11 $ 0.09 $ 0.14 $ 0.08 $ 0.17 2008 $ 0.25 $ 0.11 $ 0.12 $ 0.15 $ 0.08 $ 0.17 2009 $ 0.24 $ 0.14 $ 0.13 $ 0.17 $ 0.11 $ 0.19 $ 0.11 2010 $ 0.28 $ 0.14 $ 0.13 $ 0.16 $ 0.11 $ 0.17 $ 0.15 2011 $ 0.27 $ 0.15 $ 0.12 $ 0.16 $ 0.12 $ 0.18 $ 0.15
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $ 0.14 $ 0.16 $ 0.16 $ 0.12 $ 0.17 $ 0.14 2002 $ 0.17 $ 0.15 $ 0.21 $ 0.15 $ 0.18 $ 0.13 2003 $ 0.19 $ 0.11 $ 0.17 $ 0.12 $ 0.18 $ 0.14 2004 $ 0.20 $ 0.14 $ 0.18 $ 0.15 $ 0.19 $ 0.13 2005 $ 0.19 $ 0.15 $ 0.17 $ 0.13 $ 0.19 $ 0.14 2006 $ 0.18 $ 0.16 $ 0.18 $ 0.09 $ 0.18 $ 0.15 2007 $ 0.16 $ 0.18 $ 0.20 $ 0.08 $ 0.18 $ 0.15 2008 $ 0.19 $ 0.18 $ 0.19 $ 0.11 $ 0.18 $ 0.15 2009 $ 0.21 $ 0.18 $ 0.19 $ 0.16 $ 0.19 $ 0.15 2010 $ 0.22 $ 0.19 $ 0.19 $ 0.15 $ 0.21 $ 0.16 2011 $ 0.26 $ 0.20 $ 0.19 $ 0.16 $ 0.20 $ 0.14
39
The Urban Mobility Report (UMR) estimates transit-related congestion cost reductions, basing on a comprehensive assessment of the traffic conditions and the transit system characteristics in each particular metropolitan area. After measuring the traffic delays and transit ridership, the UMR determines the congestion increase (and its costs) that would result from eliminating transit. The results comprise specific values of congestion savings for each of these areas, for a particular year. Such methodology yields more accurate costs than the simplified approach, utilized in some of the benefit-cost analyses discussed earlier in the text. In the simplified method, a single average per-mile reduction of the congestion cost is adopted for all cities.
The UMR reports transit-related congestion savings under the “Public Transportation
Savings” category. These savings are presented in Table 2.4. Figures for congestion savings are available only at the system level, without modal disaggregation.
2.2.1.4 Reduction in Automobile Externalities
The estimates of reductions in automobile externalities were based on Litman (2011).
The referred source provides a comprehensive literature review of multiple studies that assessed the external social costs of automobile travel, including the costs of environmental impacts and the accident recovery expenses. Specific ratios of per-VMT reductions in externalities were adopted in this study, basing on the midpoint values of ratios provided by Litman’s synthesis.
The VMTs are vehicle miles traveled via automobile. The author assumed that volume of VMT reduction induced by transit is 25% lower than the volume of transit passenger miles. Such adjustments have been made as not all transit patrons would automatically transfer to solo- driving in case of transit service elimination (some would use other modes, others would carpool).
40
Table 2.4 Congestion Savings per Passenger Mile, 2011$
Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $ 0.16 $ 0.36 $ 0.22 2002 $ 0.16 $ 0.38 $ 0.23 2003 $ 0.16 $ 0.30 $ 0.23 2004 $ 0.16 $ 0.30 $ 0.23 $ 0.23 $ 0.24 2005 $ 0.16 $ 0.29 $ 0.23 $ 0.23 $ 0.24 2006 $ 0.16 $ 0.30 $ 0.23 $ 0.24 $ 0.24 2007 $ 0.16 $ 0.19 $ 0.30 $ 0.23 $ 0.24 $ 0.24 2008 $ 0.16 $ 0.19 $ 0.31 $ 0.23 $ 0.24 $ 0.24 2009 $ 0.14 $ 0.17 $ 0.33 $ 0.23 $ 0.26 $ 0.26 $ 0.19 2010 $ 0.15 $ 0.17 $ 0.34 $ 0.22 $ 0.27 $ 0.24 $ 0.21 2011 $ 0.17 $ 0.18 $ 0.30 $ 0.21 $ 0.26 $ 0.27 $ 0.25
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $ 0.25 $ 0.25 $ 0.21 $ 0.21 $ 0.27 $ 0.19 2002 $ 0.25 $ 0.25 $ 0.21 $ 0.23 $ 0.27 $ 0.20 2003 $ 0.24 $ 0.25 $ 0.21 $ 0.24 $ 0.27 $ 0.19 2004 $ 0.24 $ 0.24 $ 0.21 $ 0.25 $ 0.27 $ 0.20 2005 $ 0.24 $ 0.25 $ 0.21 $ 0.26 $ 0.27 $ 0.20 2006 $ 0.24 $ 0.25 $ 0.21 $ 0.24 $ 0.27 $ 0.20 2007 $ 0.24 $ 0.25 $ 0.21 $ 0.23 $ 0.27 $ 0.20 2008 $ 0.24 $ 0.25 $ 0.21 $ 0.27 $ 0.27 $ 0.20 2009 $ 0.23 $ 0.25 $ 0.21 $ 0.39 $ 0.27 $ 0.19 2010 $ 0.24 $ 0.26 $ 0.21 $ 0.35 $ 0.27 $ 0.22 2011 $ 0.27 $ 0.26 $ 0.21 $ 0.29 $ 0.27 $ 0.19
The author has also considered the increase in external costs caused by buses. While average per-passenger emissions are much lower for buses than for passenger cars, such increased should be also included for the bus mode. Therefore, the final, net value of reductions in externalities estimated by this study equals to the savings related to reduction of external impacts of passenger cars minus the impacts caused by transit buses. The bus externality costs were based on Litman (2011) as well. The ratios of per-mile bus external costs were multiplied by the volume of bus vehicle miles provided by transit agencies (FTIS, 2014).
For the costs of impacts of air pollution and other contamination, Litman’s review refers to values such as $0.013 to 0.205, $0.04, $0.015 (all in 2007 dollars per vehicle mile). In his conclusion, Litman sets the midpoint cost of urban traffic environmental externalities at $0.089 per passenger car VMT and $0.13 per bus VMT. These values were adopted in this study. The car ratio is multiplied by the volume of transit passenger miles, reduced by 25%. Next, the bus
41
ratio is multiplied by the volume of bus vehicle miles. The net reduction (car costs minus bus
costs) is added to the benefit side of the analysis. Equation 2.4 presents the formula used for
estimation of externalities.
$0.089 0.75 $0.13 _ (2.4)
Where: pm – system-level ridership volume in passenger miles; vm – system-level bus service volume in
vehicle miles; $0.089 and $0.13 are adopted per-mile externality values, as discussed in the previous
paragraph (Litman, 2011).
For the accident recovery and crash costs, the body of scholarship synthesized by Litman offers following average cost ratios: $0.063 per passenger mile (1994 dollars); $0.25 - $0.41
(2008), $0.022 - $0.066 (2004), $0.008 - $0.04 (1997), $0.07-$0.21 (1995) per vehicle mile.
Similarly as in the case of pollution costs, the variations among the results are coming from different research designs in each of the referred studies. Midpoint accident cost is estimated at
$0.138 per automobile VMT (in 2007 dollars), and $0.26 per vehicle mile for buses. These ratios are adopted by this study, and are used for estimating the reductions in accident recovery costs.
The estimation methodology is identical as the one used for the environmental externalities, which was presented in Equation 2.4. Tables 2.5 and 2.6 present the values of net reductions in automobile externalities.
2.2.2 Costs
2.2.2.1 Operating Costs
Operating costs were obtained directly from the transit agency data, available in the
National Transit Database under the “Operating Expenses” category (FTIS, 2014). NTD reports those costs separately for each mode. Standardized values of operating costs are presented in
Table 2.7.
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Table 2.5 Reduction in Negative Environmental Impacts per Passenger Mile, 2011$
Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $ 0.085 $ 0.091 $ 0.087 2002 $ 0.082 $ 0.087 $ 0.084 2003 $ 0.078 $ 0.082 $ 0.080 2004 $ 0.073 $ 0.078 $ 0.076 $ 0.079 $ 0.077 2005 $ 0.069 $ 0.074 $ 0.072 $ 0.074 $ 0.074 2006 $ 0.065 $ 0.070 $ 0.069 $ 0.071 $ 0.070 2007 $ 0.062 $ 0.060 $ 0.066 $ 0.067 $ 0.067 $ 0.067 2008 $ 0.057 $ 0.058 $ 0.060 $ 0.062 $ 0.062 $ 0.063 2009 $ 0.058 $ 0.059 $ 0.060 $ 0.062 $ 0.061 $ 0.062 $ 0.059 2010 $ 0.055 $ 0.058 $ 0.057 $ 0.061 $ 0.058 $ 0.061 $ 0.056 2011 $ 0.054 $ 0.054 $ 0.056 $ 0.058 $ 0.054 $ 0.057 $ 0.054
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $ 0.090 $ 0.093 $ 0.092 $ 0.081 $ 0.095 $ 0.088 2002 $ 0.085 $ 0.091 $ 0.088 $ 0.077 $ 0.091 $ 0.088 2003 $ 0.081 $ 0.087 $ 0.084 $ 0.077 $ 0.087 $ 0.085 2004 $ 0.076 $ 0.083 $ 0.079 $ 0.070 $ 0.081 $ 0.080 2005 $ 0.071 $ 0.077 $ 0.073 $ 0.069 $ 0.076 $ 0.075 2006 $ 0.067 $ 0.073 $ 0.069 $ 0.071 $ 0.072 $ 0.070 2007 $ 0.065 $ 0.069 $ 0.065 $ 0.068 $ 0.069 $ 0.067 2008 $ 0.061 $ 0.064 $ 0.062 $ 0.063 $ 0.064 $ 0.062 2009 $ 0.061 $ 0.065 $ 0.063 $ 0.059 $ 0.065 $ 0.063 2010 $ 0.058 $ 0.063 $ 0.060 $ 0.059 $ 0.063 $ 0.060 2011 $ 0.054 $ 0.060 $ 0.057 $ 0.056 $ 0.059 $ 0.057
Table 2.6 Reduction in Accident Recovery Costs per Passenger Mile, 2011$
Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $ 0.123 $ 0.135 $ 0.127 2002 $ 0.118 $ 0.127 $ 0.121 2003 $ 0.111 $ 0.120 $ 0.116 2004 $ 0.103 $ 0.114 $ 0.110 $ 0.115 $ 0.112 2005 $ 0.099 $ 0.108 $ 0.105 $ 0.109 $ 0.109 2006 $ 0.093 $ 0.104 $ 0.101 $ 0.105 $ 0.103 2007 $ 0.089 $ 0.085 $ 0.098 $ 0.099 $ 0.099 $ 0.100 2008 $ 0.083 $ 0.084 $ 0.088 $ 0.092 $ 0.092 $ 0.094 2009 $ 0.084 $ 0.085 $ 0.087 $ 0.092 $ 0.090 $ 0.092 $ 0.085 2010 $ 0.079 $ 0.084 $ 0.083 $ 0.090 $ 0.086 $ 0.091 $ 0.081 2011 $ 0.079 $ 0.079 $ 0.082 $ 0.086 $ 0.079 $ 0.085 $ 0.078
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $ 0.132 $ 0.138 $ 0.137 $ 0.115 $ 0.143 $ 0.129 2002 $ 0.123 $ 0.135 $ 0.130 $ 0.108 $ 0.136 $ 0.131 2003 $ 0.118 $ 0.130 $ 0.124 $ 0.110 $ 0.130 $ 0.125 2004 $ 0.110 $ 0.124 $ 0.116 $ 0.099 $ 0.120 $ 0.119 2005 $ 0.103 $ 0.115 $ 0.107 $ 0.100 $ 0.113 $ 0.111 2006 $ 0.098 $ 0.109 $ 0.102 $ 0.105 $ 0.108 $ 0.104 2007 $ 0.095 $ 0.103 $ 0.096 $ 0.101 $ 0.102 $ 0.099 2008 $ 0.089 $ 0.095 $ 0.091 $ 0.093 $ 0.095 $ 0.093 2009 $ 0.090 $ 0.097 $ 0.093 $ 0.085 $ 0.098 $ 0.094 2010 $ 0.085 $ 0.095 $ 0.089 $ 0.086 $ 0.094 $ 0.089 2011 $ 0.078 $ 0.090 $ 0.084 $ 0.082 $ 0.089 $ 0.084
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Table 2.7 Operating Costs per Passenger Mile, 2011$
Light Rail Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $ 1.22 $ 0.84 $ 0.30 2002 $ 1.30 $ 0.75 $ 0.53 2003 $ 1.44 $ 0.58 $ 0.54 2004 $ 1.53 $ 0.55 $ 0.60 $ 1.22 $ 0.82 2005 $ 1.71 $ 0.62 $ 0.66 $ 0.64 $ 0.36 2006 $ 1.68 $ 0.65 $ 0.66 $ 0.55 $ 0.40 2007 $ 1.77 $ 7.50 $ 0.62 $ 0.37 $ 0.58 $ 0.45 2008 $ 1.67 $ 0.76 $ 0.61 $ 0.32 $ 0.56 $ 0.41 2009 $ 1.42 $ 0.92 $ 0.82 $ 0.41 $ 0.60 $ 0.54 $ 0.34 2010 $ 1.49 $ 0.97 $ 0.92 $ 0.53 $ 0.63 $ 0.48 $ 0.39 2011 $ 1.55 $ 0.69 $ 0.77 $ 0.38 $ 0.71 $ 0.44 $ 0.35
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $ 1.13 $ 0.35 $ 0.72 $ 0.32 $ 0.25 $ 0.30 2002 $ 1.15 $ 0.42 $ 0.65 $ 0.52 $ 0.31 $ 0.34 2003 $ 1.22 $ 0.40 $ 0.78 $ 0.44 $ 0.30 $ 0.36 2004 $ 1.41 $ 0.37 $ 0.74 $ 0.36 $ 0.29 $ 0.34 2005 $ 1.54 $ 0.44 $ 0.78 $ 0.31 $ 0.29 $ 0.41 2006 $ 1.38 $ 0.43 $ 0.73 $ 0.30 $ 0.29 $ 0.38 2007 $ 1.33 $ 0.43 $ 0.65 $ 0.35 $ 0.29 $ 0.41 2008 $ 1.39 $ 0.45 $ 0.63 $ 0.40 $ 0.28 $ 0.41 2009 $ 1.82 $ 0.49 $ 0.57 $ 0.50 $ 0.28 $ 0.39 2010 $ 1.54 $ 0.53 $ 0.60 $ 0.50 $ 0.34 $ 0.41 2011 $ 1.33 $ 0.43 $ 0.60 $ 0.49 $ 0.31 $ 0.42
All Fixed-Route Services Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $ 1.16 $ 0.84 $ 0.73 2002 $ 1.28 $ 1.04 $ 0.77 2003 $ 1.28 $ 0.89 $ 0.78 2004 $ 1.41 $ 0.88 $ 0.74 $ 0.59 $ 0.93 2005 $ 1.34 $ 0.83 $ 0.72 $ 0.64 $ 0.80 2006 $ 1.35 $ 0.85 $ 0.71 $ 0.59 $ 0.78 2007 $ 1.42 $ 0.93 $ 0.85 $ 0.64 $ 0.60 $ 0.73 2008 $ 1.30 $ 0.87 $ 0.99 $ 0.66 $ 0.58 $ 0.68 2009 $ 1.24 $ 0.82 $ 1.13 $ 0.71 $ 0.70 $ 0.80 $ 0.91 2010 $ 1.32 $ 0.81 $ 1.22 $ 0.70 $ 0.74 $ 0.73 $ 0.91 2011 $ 1.25 $ 0.78 $ 1.07 $ 0.63 $ 0.77 $ 0.75 $ 0.82
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $ 0.79 $ 0.69 $ 0.79 $ 0.93 $ 0.44 $ 0.76 2002 $ 0.92 $ 0.71 $ 0.81 $ 1.04 $ 0.51 $ 0.69 2003 $ 0.95 $ 0.69 $ 0.93 $ 0.87 $ 0.54 $ 0.72 2004 $ 1.04 $ 0.67 $ 1.00 $ 1.02 $ 0.60 $ 0.72 2005 $ 1.07 $ 0.73 $ 1.04 $ 0.84 $ 0.60 $ 0.71 2006 $ 1.11 $ 0.72 $ 1.03 $ 0.56 $ 0.58 $ 0.73 2007 $ 0.98 $ 0.76 $ 1.02 $ 0.56 $ 0.57 $ 0.69 2008 $ 1.06 $ 0.78 $ 0.95 $ 0.57 $ 0.57 $ 0.69 2009 $ 1.07 $ 0.76 $ 0.86 $ 0.87 $ 0.54 $ 0.68 2010 $ 1.18 $ 0.82 $ 0.86 $ 0.75 $ 0.60 $ 0.75 2011 $ 1.35 $ 0.75 $ 0.85 $ 0.73 $ 0.55 $ 0.71
2.2.2.2 Capital Costs
Capital costs were estimated based on the agency reports and other sources. The National
Transit Database (NTD, 2014) reports the annual capital expenses of the transit agencies starting from 1997. These are the amounts spent on capital in a particular year. As mentioned earlier in the text, transit BCAs usually consider annualized costs of capital, and such an approach has 44 been also adopted in this study. To account for the annualized costs of capital, following steps have been made:
For bus and rail capital expansions made since 1997, annual costs reported in the NTD
(2013) were considered. NTD provides three cost categories for the 1997 – 2000 period, and nine categories since 2001, as illustrated by Table 2.8. Even though the study considers only the 2001
– 2011 period, the capital cost estimation includes expenses made prior to 2001, as the capital acquired before the period of analysis was still in use during that period (as illustrated by Figure
2.2 earlier in the text).
Following estimations of the pre-1997 costs were made:
- For rail capital expenses on investments commenced prior to 1997, the capital costs were
taken from Baum-Snow & Kahn’s (2005) report that summarizes the costs of new rail
openings for all of the systems operating prior to 1997 except for Pittsburgh; the pre-1997
costs for Pittsburgh were based on Federal Transit Administration press releases (FTA,
1996).
- The pre-1997 bus capital expenses were extrapolated from the 1997-2011 costs, due to
lack of sources for the actual costs. Specifically, the following method has been used:
First, it has been assumed that an average lifecycle of the bus capital is equal to 20 years
(based on capital cost estimation procedures suggested by the FTA), therefore, all bus
capital costs made since 1982 were taken into consideration. Next, an average annual cost
for the 1997 – 2011 period was estimated, and allocated to each of the years between
1982 and 1996, after adjusting for inflation. These costs were also adjusted for the
continuous system expansion and new technology development, which have increased
the capital costs at a higher pace than inflation (therefore, the average cost of capital
45
expansion in the past was lower). By a rule of thumb, following adjustment factors were
adopted: for 1982 – 86 period: x 0.4, for 1987 – 91: x 0.6, for 1992 – 97: x 0.8. For
example, if the average capital cost for the 1997 – 2011 period was equal to $1,000,000
in 2011 dollars, the cost for year 1985 was estimated as following: $1,000,000 x 0.4 x
0.48 = $192,000 (where 0.48 is the inflation adjustment index based on the CPI).
- For the purpose of cost annualization, an average lifecycle was assigned to each of the
categories, basing on the Federal Transit Administration guidelines, specifically Standard
Cost Categories for Capital Projects (FTA, 2013b). Table 2.8 presents the capital
categories and their lifecycles.
For all estimations, the discount rates were based on the current real interest rates (adjusted for inflation) for U.S. Treasury bonds provided by the Guidelines and Discount Rates for Benefit-
Cost Analysis of Federal Programs (Office of Budget and Management, 2013) for the year of the study’s completion (2014). These bonds are available in the following maturities: 3, 5, 7, 10, 20, and 30 years. The interest rates were assigned to lifecycle ranges using the following scheme:
For 15-year lifecycle– 10-year bond rate, 1.0%
For lifecycle between 16 and 25 years – 20-year bond rate, 1.6%
For lifecycles above 25 years - 30-year bond rate, 1.9%
While these rates are pretty low (in the previous years, they usually ranged from 2% to 5%), a sensitivity analysis discussed later in this chapter indicated that adopting a higher discount rate
(e.g. 5%) would have insignificant influence on the final result (net benefits).
Annualized capital costs were estimated with Equation 2.3 presented earlier in the text:
cost = NPV × i /( 1 - (1+i)^(-t) )
46
Annualized costs for a specific capital category where then allocated to the entire period of that category’s useful life. Table 2.9 presents the annualized capital costs discounted by passenger miles, for light rail only and for all fixed-route services.
Table 2.8 Summary of Capital Cost Categories and their Average Lifecycles
Light Rail Cost Category Bus Lifecycle Lifecycle
1997 – 2001 data
Rolling Stock 15 30
Guideway 40 60
Other 25 25
2001 – 2011 data
Guideway 25 50
Systems (signals, controls, etc.) 25 30
Stations 70 70
Maintenance Facilities 50 50
Revenue Vehicles 15 30
Other Vehicle Costs 15 15
Administration Buildings 50 50
Fare Collection Systems 25 25
Other 20 30
Source: FTA (2013b)
47
Table 2.9 Annualized Capital Costs per Passenger Mile, 2011$
Light Rail Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $ 1.50 $ 0.87 $ 0.27 2002 $ 1.62 $ 0.77 $ 0.48 2003 $ 1.59 $ 0.51 $ 0.66 2004 $ 1.63 $ 0.53 $ 0.85 $ 1.05 $ 1.70 2005 $ 1.78 $ 0.53 $ 0.94 $ 0.60 $ 0.38 2006 $ 1.70 $ 0.54 $ 0.83 $ 0.58 $ 0.41 2007 $ 1.66 $ 45.34 $ 0.61 $ 0.44 $ 0.70 $ 0.42 2008 $ 1.65 $ 1.10 $ 0.68 $ 0.46 $ 0.83 $ 0.38 2009 $ 1.36 $ 0.82 $ 0.96 $ 0.57 $ 1.11 $ 0.60 $ 1.38 2010 $ 1.51 $ 0.98 $ 1.20 $ 0.63 $ 1.64 $ 0.58 $ 0.77 2011 $ 1.55 $ 0.72 $ 0.89 $ 0.59 $ 2.00 $ 0.69 $ 0.75
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $ 0.82 $ 0.33 $ 0.25 $ 0.27 $ 0.17 $ 0.30 2002 $ 0.94 $ 0.31 $ 0.29 $ 0.25 $ 0.22 $ 0.24 2003 $ 1.05 $ 0.32 $ 0.35 $ 0.28 $ 0.21 $ 0.27 2004 $ 1.22 $ 0.31 $ 0.39 $ 0.25 $ 0.20 $ 0.32 2005 $ 1.36 $ 0.32 $ 0.40 $ 0.22 $ 0.18 $ 0.40 2006 $ 1.28 $ 0.33 $ 0.32 $ 0.20 $ 0.24 $ 0.44 2007 $ 1.33 $ 0.34 $ 0.33 $ 0.24 $ 0.24 $ 0.40 2008 $ 1.50 $ 0.37 $ 0.31 $ 0.46 $ 0.25 $ 0.39 2009 $ 1.79 $ 0.38 $ 0.29 $ 0.72 $ 0.24 $ 0.37 2010 $ 1.69 $ 0.38 $ 0.34 $ 0.96 $ 0.28 $ 0.42 2011 $ 1.70 $ 0.38 $ 0.40 $ 0.90 $ 0.28 $ 0.41
All Fixed-Route Services Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $ 0.40 $ 0.30 $ 0.16 2002 $ 0.44 $ 0.36 $ 0.18 2003 $ 0.46 $ 0.28 $ 0.21 2004 $ 0.48 $ 0.32 $ 0.21 $ 0.27 $ 0.28 2005 $ 0.46 $ 0.29 $ 0.22 $ 0.30 $ 0.23 2006 $ 0.44 $ 0.30 $ 0.22 $ 0.29 $ 0.23 2007 $ 0.44 $ 0.34 $ 0.33 $ 0.21 $ 0.31 $ 0.22 2008 $ 0.41 $ 0.30 $ 0.41 $ 0.22 $ 0.31 $ 0.21 2009 $ 0.37 $ 0.29 $ 0.53 $ 0.25 $ 0.35 $ 0.26 $ 0.36 2010 $ 0.42 $ 0.31 $ 0.62 $ 0.27 $ 0.40 $ 0.25 $ 0.39 2011 $ 0.43 $ 0.30 $ 0.57 $ 0.27 $ 0.44 $ 0.29 $ 0.40
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $ 0.20 $ 0.17 $ 0.16 $ 0.21 $ 0.10 $ 0.19 2002 $ 0.25 $ 0.16 $ 0.19 $ 0.24 $ 0.11 $ 0.19 2003 $ 0.27 $ 0.17 $ 0.23 $ 0.22 $ 0.12 $ 0.22 2004 $ 0.31 $ 0.17 $ 0.28 $ 0.27 $ 0.12 $ 0.25 2005 $ 0.32 $ 0.17 $ 0.29 $ 0.23 $ 0.12 $ 0.27 2006 $ 0.33 $ 0.18 $ 0.27 $ 0.16 $ 0.15 $ 0.30 2007 $ 0.30 $ 0.20 $ 0.27 $ 0.17 $ 0.15 $ 0.29 2008 $ 0.35 $ 0.21 $ 0.25 $ 0.23 $ 0.16 $ 0.27 2009 $ 0.36 $ 0.21 $ 0.25 $ 0.41 $ 0.16 $ 0.27 2010 $ 0.40 $ 0.22 $ 0.27 $ 0.44 $ 0.19 $ 0.32 2011 $ 0.49 $ 0.23 $ 0.30 $ 0.47 $ 0.19 $ 0.29
48
2.3 Benefit-Cost Analysis Final Results and Conclusions
The analysis results provide the opportunity for making some preliminary conclusions regarding the economic outcomes of particular transit investments and the possible influence of internal factors on transit benefits. A more detailed assessment of the benefit determinants, which focuses on the importance of market-oriented service planning strategies and ownership structure is discussed in Chapters 3 and 4.
Table 2.10 demonstrates the final results of the benefit-cost analysis, including both total values of the net benefits, and the standardized benefits per passenger mile. These amounts were calculated by adding benefits from tables 2.2, 2.3, 2.4, 2.5 and 2.6, and subtracting costs included in tables 2.7 and 2.9. The total amounts of benefits and costs for particular subcategories are presented in Appendix A.
The analysis indicated that the following categories of benefits and costs have the highest influence on the final BCA result, that is, the net benefit amount: direct fare revenues, consumer surplus, congestion savings and operating costs. Capital costs also play an important role, although after annualization, they represent not more than 25-30% of the total annual cost. Due to long lifecycles, especially for the rail mode, the actual annualized cost of capital investments is not that overwhelming as claimed by some of the opponents of rail investment referred in
Chapter 1. Additionally, higher capital expenses do not automatically result in lower net benefits.
This suggests that if a capital investment is carefully planned, the increased expenses could be balanced with higher benefits. The values of benefits in the remaining categories, specifically the savings on car externalities, are relatively low and have an insignificant role in determining the net benefits.
49
Table 2.10 System-level Total Net Benefits and Net Benefits per Passenger Mile
Total amount of net benefits (millions of 2011$) Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 -$41 -$79 -$36 2002 -$53 -$131 -$52 2003 -$57 -$153 -$77 2004 -$60 -$142 -$71 -$117 -$93 2005 -$62 -$153 -$86 -$155 -$58 2006 -$67 -$127 -$77 -$126 -$50 2007 -$77 -$44 -$198 -$76 -$139 -$43 2008 -$75 -$52 -$244 -$29 -$161 -$41 2009 -$78 -$24 -$280 -$69 -$185 -$59 -$201 2010 -$72 -$56 -$269 -$110 -$214 -$63 -$172 2011 -$70 -$53 -$306 -$93 -$238 -$75 -$133
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 -$62 $18 -$15 -$52 $141 -$50 2002 -$99 -$18 -$5 -$59 $105 -$39 2003 -$97 -$37 -$39 -$46 $81 -$55 2004 -$128 -$24 -$67 -$60 $59 -$67 2005 -$154 -$61 -$85 -$46 $48 -$67 2006 -$175 -$46 -$84 $2 $42 -$75 2007 -$158 -$31 -$72 -$18 $116 -$71 2008 -$166 -$58 -$74 -$12 $112 -$68 2009 -$171 -$48 -$57 -$51 $151 -$88 2010 -$198 -$73 -$60 -$54 $115 -$91 2011 -$202 -$46 -$58 -$74 $207 -$99
Net benefits per passenger mile (2011$) Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 -$0.53 -$0.24 -$0.09 2002 -$0.72 -$0.45 -$0.14 2003 -$0.78 -$0.39 -$0.21 2004 -$0.86 -$0.39 -$0.18 -$0.23 -$0.31 2005 -$0.82 -$0.37 -$0.20 -$0.31 -$0.16 2006 -$0.84 -$0.30 -$0.17 -$0.24 -$0.13 2007 -$0.93 -$0.49 -$0.47 -$0.15 -$0.27 -$0.10 2008 -$0.84 -$0.46 -$0.63 -$0.05 -$0.29 -$0.09 2009 -$0.78 -$0.20 -$0.80 -$0.13 -$0.37 -$0.15 -$0.61 2010 -$0.80 -$0.48 -$0.80 -$0.21 -$0.46 -$0.14 -$0.57 2011 -$0.75 -$0.42 -$0.79 -$0.17 -$0.52 -$0.18 -$0.44
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 -$0.16 $0.05 -$0.10 -$0.40 $0.30 -$0.21 2002 -$0.30 -$0.04 -$0.03 -$0.47 $0.25 -$0.14 2003 -$0.30 -$0.08 -$0.27 -$0.31 $0.20 -$0.21 2004 -$0.43 -$0.05 -$0.46 -$0.48 $0.15 -$0.26 2005 -$0.50 -$0.13 -$0.56 -$0.30 $0.11 -$0.26 2006 -$0.56 -$0.10 -$0.50 $0.01 $0.10 -$0.29 2007 -$0.45 -$0.07 -$0.42 -$0.07 $0.26 -$0.26 2008 -$0.53 -$0.13 -$0.39 -$0.05 $0.25 -$0.23 2009 -$0.54 -$0.10 -$0.28 -$0.30 $0.33 -$0.29 2010 -$0.65 -$0.15 -$0.31 -$0.29 $0.28 -$0.35 2011 -$0.78 -$0.10 -$0.33 -$0.38 $0.49 -$0.34
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2.3.1 Sensitivity Analysis
To provide additional insights on the benefit-cost analysis results, the author decided to
conduct a sensitivity analysis, which determines how the BCA results would change if different
values of the fixed input parameters would be adopted. These parameters include the average
per-passenger mile values of externalities, the demand elasticity used for consumer surplus
estimations, and the discount rate influencing the capital cost calculations. As discussed earlier in
the text, literature offers various ranges of these parameters; the author selected midpoint values
from the proposed ranges, but more conservative or liberal assumptions should also be
considered. An element of uncertainty is also present in the capital cost estimations; therefore,
the discount rate used for calculating the annualized costs, and reflecting the changing value of
money in time, might be higher or lower (higher rate means higher annualized cost).
Table 2.11 presents the conservative and liberal levels of parameters, non-direct benefit parameters, and discount rates. The ranges of elasticities and average costs of externalities offered by literature were discussed earlier in the text; liberal and conservative values were based on the upper and lower bounds of these ranges reported by literature. Reductions in congestion costs used in the study were adopted from an external source and are based on a combination of multiple factors. Detailed investigation of the hypothetical, more optimistic or more conservative scenarios in which those factors would have higher or lower values exceeds the scope of this study; therefore, the author assumed by a rule of the thumb that these reductions might be 25% higher for the more optimistic (liberal) scenario, or 25% lower for the more pessimistic
(conservative) scenario. The discount rates used in the capital cost estimations were based on the current bond rates for the time of analysis (year 2014). Discount rates are generally dependent on the overall condition of economy, situation on the financial market, inflation, and other factors,
51 which again are beyond the focus of this study. An overview of the historical discount rate values (Office of Budget and Management, 2014) indicated that the rates were generally higher in the past, reaching values between 2% and 5% throughout the last two decades, and a falling trend could be easily noticed. Additionally, the rates for 10-, 20-, and 30-year bonds in a particular year were almost the same in the past years, with differences not exceeding 0.3 percentage points. Basing on these observations, the author decided to assume following discount rates for the conservative scenario: 2.9%, 3.0%, 3.1% for 10-, 20-, and 30-year bonds respectively. In the liberal scenario, the current values were reduced by a half.
Table 2.11 Adjusted Input Parameters Used in Sensitivity Analysis
Parameter Base value Upper-bound Lower-bound (Liberal (Conservative assumptions) assumptions)
Rail / bus elasticity Rail: -0.3; bus: -0.4 Rail: -0.2; bus: -0.25 Rail: -0.4; bus: -0.55 $0.089 per vehicle- Reduction in negative mile traveled $0.14 per VMT $0.03 per VMT impact on environment (VMT) Negative impact on $0.13 per bus $0.05 per VM $0.20 per VM environment for buses vehicle miles (VM) Reduction in accident $0.138 per pass. $0.20 per VMT $0.07 per VMT recovery costs mile (PM) Additional accident $0.26 per vehicle- recovery costs for mile traveled $0.19 per VM $0.34 per VM buses (VMT) Specific monetary Reduction in values provided by 25% higher 25% lower congestion costs literature Discount rates 0.5% / 0.65% / 1.0% / 1.3% / 1.9% 2.9% / 3.0% / 3.1% (10/20/30 years) 0.95%
Table 2.12 presents the estimates of the net benefits per passenger mile for the base, conservative and liberal assumptions made with regards to the discussed parameters and discount rates.
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Table 2.12 Sensitivity Analysis Results: Net Benefits per Passenger Mile
Net benefits per passenger mile (2011$), liberal assumptions for all parameteres Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 $0.00 $0.15 $0.17 2002 -$0.19 -$0.05 $0.23 2003 -$0.26 -$0.04 $0.16 2004 -$0.31 -$0.04 $0.18 $0.09 $0.10 2005 -$0.30 -$0.04 $0.14 $0.00 $0.23 2006 -$0.34 $0.07 $0.17 $0.06 $0.26 2007 -$0.45 -$0.15 -$0.15 $0.18 $0.03 $0.26 2008 -$0.39 -$0.15 -$0.28 $0.27 -$0.01 $0.26 2009 -$0.35 $0.13 -$0.41 $0.22 -$0.05 $0.24 -$0.28 2010 -$0.33 -$0.16 -$0.39 $0.14 -$0.13 $0.22 -$0.20 2011 -$0.30 -$0.09 -$0.41 $0.17 -$0.19 $0.20 -$0.08
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 $0.22 $0.44 $0.28 -$0.11 $0.69 $0.16 2002 $0.12 $0.33 $0.37 -$0.07 $0.65 $0.21 2003 $0.13 $0.24 $0.12 $0.04 $0.60 $0.14 2004 $0.01 $0.29 -$0.07 -$0.09 $0.54 $0.08 2005 -$0.08 $0.21 -$0.18 $0.05 $0.49 $0.09 2006 -$0.16 $0.24 -$0.13 $0.29 $0.47 $0.06 2007 -$0.08 $0.29 -$0.04 $0.20 $0.62 $0.08 2008 -$0.13 $0.22 -$0.03 $0.25 $0.61 $0.10 2009 -$0.12 $0.26 $0.08 $0.11 $0.69 $0.03 2010 -$0.22 $0.21 $0.05 $0.10 $0.66 $0.00 2011 -$0.29 $0.27 $0.03 $0.00 $0.86 -$0.02
Net benefits per passenger mile (2011$), conservative assumptions for all parameteres Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 -$0.96 -$0.60 -$0.38 2002 -$1.13 -$0.82 -$0.45 2003 -$1.19 -$0.71 -$0.52 2004 -$1.28 -$0.72 -$0.49 -$0.51 -$0.64 2005 -$1.20 -$0.67 -$0.49 -$0.58 -$0.46 2006 -$1.21 -$0.63 -$0.45 -$0.50 -$0.43 2007 -$1.28 -$0.77 -$0.77 -$0.42 -$0.52 -$0.38 2008 -$1.16 -$0.72 -$0.95 -$0.32 -$0.54 -$0.35 2009 -$1.08 -$0.46 -$1.16 -$0.42 -$0.64 -$0.44 -$0.88 2010 -$1.13 -$0.74 -$1.18 -$0.49 -$0.74 -$0.41 -$0.86 2011 -$1.07 -$0.68 -$1.13 -$0.44 -$0.81 -$0.46 -$0.74
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 -$0.49 -$0.29 -$0.42 -$0.70 -$0.03 -$0.53 2002 -$0.64 -$0.37 -$0.38 -$0.81 -$0.08 -$0.45 2003 -$0.64 -$0.38 -$0.59 -$0.63 -$0.12 -$0.52 2004 -$0.77 -$0.36 -$0.79 -$0.81 -$0.18 -$0.56 2005 -$0.83 -$0.43 -$0.87 -$0.60 -$0.19 -$0.56 2006 -$0.88 -$0.39 -$0.80 -$0.24 -$0.21 -$0.59 2007 -$0.75 -$0.36 -$0.73 -$0.31 -$0.03 -$0.55 2008 -$0.83 -$0.41 -$0.67 -$0.31 -$0.03 -$0.50 2009 -$0.84 -$0.38 -$0.56 -$0.66 $0.04 -$0.57 2010 -$0.97 -$0.44 -$0.59 -$0.63 -$0.01 -$0.64 2011 -$1.13 -$0.38 -$0.61 -$0.71 $0.21 -$0.61
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Table 2.13 provides additional insights on the role of specific benefit and cost categories in influencing the final results. Adopting more relaxed or more conservative assumptions only for one of the categories does not have a significant influence on the net result; however, if all benefit parameters and the discount rate would be modified to upper or lower bound, the final result would change substantially.
Table 2.13 Additional Sensitivity Analysis Results: Influence of Changes of Specific Benefit and Cost Categories
Liberal scenario Conservative scenario
Average change of the benefits (or costs) after adopting liberal or conservative assumptions for a specific category of benefits (base values for other parameters):
Consumer Surplus 18% -8% Congestion Costs 7% -7% Externalities - Air Pollution 7% -8% Externalities - Accidents 9% -9% Capital Costs -12% 17%
Average change in benefits if a particular scenario is applied to all 42% -62% benefit and cost categories
Average change in the amount of costs if a particular scenario is -2% 3% applied to all benefit and cost categories
2.3.2 Discussion of Benefit Estimates for Particular Case Systems
The analysis indicated substantial differences in average net benefits generated by particular transit systems. Only one system, San Diego, generates positive net benefits throughout the entire period of analysis. Few annual benefit values higher than zero were
54 estimated for some other systems, but usually, only for one or two years. In general, three systems – Denver, Minneapolis, and Portland – appear to generate net benefits close to zero
(falling into the range between -$0.31 and $0.05) throughout the entire study period,. There are several possible explanations for the better-than-average performance of these four systems. San
Diego’s system, along with Denver and Portland, offers the highest amount of rail service among the analyzed cases (over 7 million revenue miles per year), while it ranks eight in terms of bus service supply. Simultaneously, these three systems are attracting the highest ridership
(measured in passenger miles) at the system level, what yields high direct fare revenues, as well as large consumer surplus and substantial savings in automobile externalities. These observations, reinforced by a previous study investigating the influence of service planning decisions on ridership and performance (Jaroszynski and Brown, 2014) indicate that Denver,
Portland, and San Diego systems share several common characteristics: they operate expanded, regional transit networks in which light rail plays the leading role, serving as the backbone of the transit systems. Their bus services are oriented on providing access to rail stations and linking suburban areas that are not directly connected by the rail network. Simultaneously, the situation in Minneapolis in many ways differs from the other three highly ranked systems. There was only one rail line operating in Minneapolis during the period of analysis (opened in 2004), and even though it was successful in attracting ridership in the areas it serves, it still plays a much smaller role in the context of the entire metropolitan transit system when compared to Denver, Portland, and San Diego.
Lowest net benefits values (or highest losses), reaching even $-0.93 per passenger mile, were estimated for Buffalo (in the entire period of analysis) as well as for Dallas and Pittsburgh
(since 2004). A brief overview of the recent history of transit development and relevant planning
55
and socioeconomic characteristics brings several possible explanations for the unsatisfactory
economic results of those three systems. The transit system in Buffalo clearly undergoes
stagnation: its only light rail line has not been extended since its opening in 1984; the amount of
bus service has been at a stable level until 2008, and then experienced substantial reductions. In
Dallas, the situation is exactly opposite: the rail network has been intensively expanded since the
opening in 1996, eventually making it the largest light rail system in the nation. However, the
ridership has not increased proportionally to the service expansion, and therefore, the extensive
capital and operating costs are not being balanced by increased fare revenues and social benefits,
which resulted in highly negative results of the benefit-cost analysis especially during the last
few years of the analysis.
Pittsburgh falls somewhere in the middle between Buffalo and Dallas in the means of
service supply: the amount of rail service increased during the period of analysis, mainly after
one of the suburban branches reopened in 2004, while the bus service volume has recently
dropped. The factors responsible for generating negative benefits in Pittsburgh include high
capital costs, which are not resulting in attracting additional ridership, if compared to other cities.
As noticed in Chapter 1, Pittsburgh is the only city in the case set that operates an extensive bus rapid transit (BRT), utilizing dedicated guideways that are separated and independent from other road infrastructure (there are few BRT routes in some of the other case cities, but all of them are sharing the right-of-way with freeways or arterial roads). While some scholars claim that BRT is a more feasible alternative to light rail, the results of this analysis seem to contradict that notion.
The unsatisfactory results of the BCA for Pittsburgh could also be explained by the fragmentation of transit management: the primary agency (Port Authority) operates only in the central Allegheny County, while several other agencies serve the remaining parts of the
56
metropolitan area as well as operate duplicative service connecting the remote counties with
downtown Pittsburgh. Similar organizational scheme is functioning in Dallas and Sacramento,
two other regions where transit systems appear to generate substantial loses. The partitioned
governance appears to generate additional costs, as discussed in Chapter 4, along with a more
detailed analysis of the influence of governance structure on transit economic outcomes.
Last but not least, it is also relevant to notice that Buffalo and Pittsburgh metropolitan areas have been recently experiencing decline in population and economic activity, while Dallas, a city located in the dynamically growing Southwest region, has observed a rapid population increase, as illustrated by Table 1.3. Simultaneously, the statistics indicate higher ratios of transit dependency in Buffalo and Pittsburgh. Still, the higher number of persons lacking car access does not imply satisfactory economic results of the transit systems, while other cities, with lower amount of zero-vehicle households and more affluent populations are able to make their transit services more efficient.
In general, the results of the benefit-cost analysis suggest that at least some of the socioeconomic factors are not necessarily directly related to the economic outcomes of the multimodal transit investments considered by this study, and simultaneously, the internal factors, regulated by planning decisions, might have the prevailing role in explaining the variations in economic outcomes. Further analysis, discussed in the following two chapters, comprehensively investigates the role of internal and external characteristics in influencing the net benefits of transit operations.
It is pertinent to note here that some of the unexpected analysis outcomes might be also resulting from generalizations made throughout the analysis. As emphasized in the methodology discussion, due to the complexity of the data set (thirteen systems analyzed during an eleven-
57 year period), the author has made some simplifications, which include adopting uniform per-mile or per-passenger externality values, or uniform elasticity values for all cities. The actual values of consumer surplus and reductions in negative impacts of motorization might be different across the cities. The sensitivity analysis has indicated that adopting conservative assumptions could reduce the estimated amount of benefits even by 60%. On the other hand, the benefits could be higher than estimated, if the liberal scenario would be adopted. Further, detailed research would be required to provide a more precise estimation of the non-direct benefits generated by the analyzed systems. Still, even if the estimated benefit amounts might be not fully accurate, the results reveal substantial differences between the economic outcomes of transit investments in specific cities. These differences indicate that certain systems perform much better in the terms of economic results than other ones, what is a very important outcome from the perspective of this study’s objectives. Changing the assumptions made with regards to the non-direct benefit categories and the capital cost discount rates would influence the net benefit results, but not the variations between the benefits of specific systems.
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CHAPTER THREE
ROLE OF INTERNAL SERVICE PLANNING DECISIONS IN INFLUENCING BENEFITS AND COSTS OF MULTIMODAL TRANSIT
This chapter discusses the second key stage of the study, which has investigated the relationship between the net benefits of particular systems (estimated in the previous stage), and the internal service planning strategies adopted by these systems. The goal of that investigation was to provide a response to the first major research question of this study. Using a panel regression model, the analysis determines how the planning strategies influence the amount of transit social benefits generated by each of the systems. The regression model also compares the significance of these strategies in explaining the benefit values with the explanatory power of the commonly recognized external determinants of transit outcomes.
The author has also conducted an additional, simplified route-level economic evaluation of planning decisions, measuring the average values of performance and feasibility indicators
(farebox recovery, operating costs, and ridership) for specific types of routes, categorized by their role within a particular transit network. Due to limitations in data availability, the route- level analysis was performed only for the last year of the study’s timeframe, that is, 2011.
3.1 Literature Review
The major research objective of this study (economic evaluation of particular internal planning decisions) was motivated by an emerging body of scholarship pointing to the role of internal planning decisions in increasing transit ridership and improving its productivity. This section discusses briefly the previous findings on the importance of service planning strategies
59 and other internal planning decisions in transit management. It also relates these findings to other ridership and performance improvement strategies offered by the literature.
The term “internal planning decisions” has been coined by scholars to describe transit planning strategies oriented primarily on modifying and adjusting internal factors of transit performance. The internal factors are the factors controlled by transit managers and planners, and in general, comprise service parameters and other characteristics fully dependent on transit agencies’ decisions. More specifically, internal factors include service characteristics such as service frequency, travel speed, safety, comfort, reliability, as well as several other features controlled by planners and decision-makers, including network design and orientation, connectivity and transfer opportunities. The remaining influences on transit performance are classified as external factors, and include primarily the socioeconomic and spatial characteristics of the served area, including spatial form, density, and economic conditions (Taylor and Fink,
2003; Thompson and Brown, 2012; Jaroszynski and Brown, 2014).
For many years, the internal planning decisions were not receiving much attention from scholars and practitioners investigating best practices in transit planning. The majority of transit planning strategies was grounded on the notion of importance of external factors, such as urban form and spatial density. The famous Pushkarev & Zupan (1977) study, followed by Meyer and
Gomez-Ibanez (1981), Jones (1985), Hendeckrison (1986), Mierzejewski and Ball (1990), and
Pisarski (1996), have claimed that transit operations yield the best performance outcomes in denser, central-city areas, and that the continuous suburbanization of American cities results in declining transit patronage outside of the urban core. Consequently, the planning strategies were focused primarily on providing access to the central business districts (CBDs) and other traditional, pre-automobile era environments. Such philosophy was reflected by the radial
60 network layout, a common structure observed in most of U.S. cities until the 1990s (Brown and
Thompson, 2008a).
More recently, the notion of the strong relationship between density, urban form and transit performance has served as one of the foundations for the concept of transit-oriented development (TOD), encompassing high-density, mixed-use (residential and commercial) development with convenient transit access, provided by rail or frequent bus service. The combined residential and commercial character of the TOD is supposed to generate and attract significant transit ridership, as well as allow some of the residents to work within a walking distance from their home (Calthorpe 1993; Bernick and Cervero 1997, TCRP, 2002).
While most of the scholars investigating the factors determining transit ridership and performance have focused primarily on the external influences (incl. socioeconomic setting and spatial form), more recently some researchers decided to put more attention to the internal factors and examine their relevance for designing strategies aimed at increasing transit use and efficiency. The referred scholarship on internal factors was inspired by observations of performance patterns across U.S. metropolitan regions. These observations revealed substantial differences in standardized performance measures, such as “boardings per mile” or “passenger miles per vehicle mile”. In many cases, these differences were not easily explainable with the differences in external factors, incl. socio-economic characteristics. Besides, the research revealed that some agencies decided to modify their planning policies, and that they no longer treat the traditional downtown as the only important destination for transit trips. These agencies began expanding their service to the emerging suburban activity centers, increasing frequency, and introducing new crosstown routes, improving the connectivity between the outlying, but increasingly important remote areas. The preliminary analysis had indicated that these decisions
61 were successful, and that they might actually provide explanation for the aforementioned differences in ridership and performance (Thompson and Matoff, 2003).
These preliminary observations motivated the scholars to perform a more comprehensive examination of the effects of specific internal planning decisions on transit outcomes. The researchers have speculated that specific internal factors might also have a significant influence on transit results, and that they successfully serve as a part of the improvement strategies, oriented on increasing ridership, modal share and other results. The research has confirmed these hypothetical assumptions. Certain planning strategies, based on modifying some of the internally controlled transit service characteristics, were identified as positive influences on ridership and performance. More specifically, scholars have found that agencies that expand services into the suburban areas, provide frequent and multi-connected services, and transform their networks from the traditional radial, CBD-focused orientation into multi-destination, decentralized grids perform better than other systems (Thompson and Matoff, 2003; Brown and Thompson, 2008a,
2009; Mees, 2010). Table 3.1 synthesizes the results of the referred multiple-case studies on the importance of internal planning decisions.
There are several reasons underlying the discussed internal planning strategies. One of them is the ongoing suburbanization and spatial expansion of American cities. As indicated by the U.S. Census results and other socioeconomic statistics referred in Chapter 1, majority of urban employment and population in U.S. metropolitan areas are currently located outside the central city, and consequently, majority of commuting and other trips is made between non-CBD destinations. A typical CBD is still an important cluster of economic activity, but the share of metropolitan area’s jobs located within the CBD usually does not exceed 20%. The discussed strategies generally improve access to the remaining 80% jobs, scattered across the region.
62
Table 3.1 Summary of the Previous Findings on the Impact of Internal Service Planning Decisions on Transit Ridership and Performance
Reference Scope of Study, Results Research Objectives
Thompson and Matoff (2003) 9 large metropolitan areas, some with Multi-destination, networks with bus and rail systems, others bus-only, convenient, all-day, “whole-system” 1983-1998 service have higher ridership, better service effectiveness (productivity) Compared various performance and lower operating costs than radial indicators among systems that networks focused on providing peak- adopted various network structures hour service to downtown. and service planning strategies (multi-destination vs. radial) Thompson and Brown (2006) 83 mid-sized/large metropolitan Decentralization is significantly and areas, 2000 positively correlated with Brown and Thompson (2008a) productivity, except for areas with a Investigated the influence of network population above 1 million. Cities in decentralization on transit the Western U.S., featuring productivity and regional variations predominantly low-density and auto- in transit productivity and ridership. oriented urban structures observed highest pace of transit ridership increase. Brown and Thompson (2008b) Atlanta metropolitan region, 1978- Transit ridership declines if the 2003 agency does not react to the ongoing decentralization of employment Investigated the influence of locations; providing convenient employment decentralization on access to suburban job destinations transit ridership patterns. positively influences ridership
Brown and Thompson (2008c) 45 large metropolitan areas, 1984- Systems with multi-destination 2004 orientation and presence of rail service observe better performance Compared productivity and operating and lower average operating costs costs among systems that adopted than their counterparts do. multi-destination or radial network structures, and operate or do not operate rail.
Brown and Thompson (2009) 11 large metropolitan areas with Multi-destination network layout, multimodal systems (bus and rail), recognition of the non-CBD travel 1984-2004 market, seamless transfer opportunities and providing Investigated the influence of service convenient access to rail stations are planning decisions on transit all positively influencing ridership performance and service effectiveness of a multimodal transit system. Continued on Next Page
63
Table 3.1 - Continued
Reference Scope of Study, Results Research Objectives Mees (2000, 2010) Opposite to other authors, Mees did not conduct comprehensive quantitative assessments; however, he provided several strong arguments based on experiences of multiple transit systems in the U.S. and other developed countries supporting the notion of the importance of service planning decisions for increasing ridership and improving transit performance. Thompson et al (2011) Atlanta metropolitan region, 2000 A multi-destination system could successfully serve different rider Explored the ridership patterns groups; the “feeder” bus network within a multi-destination, could provide access to various multimodal system, focusing on how destinations for the transit- does the system serve different dependent, as well as ease groups of transit riders (transit- accessibility to rail stations for the dependent vs. choice riders) choice riders. Brown et al (2012) Tallahassee (Florida), 2011 Ridership and productivity declined after the decentralization, however, Investigated the immediate effects of some critical elements of the multi- a sudden network decentralization in destination strategy, such as high a small-sized metropolitan area. frequency and seamless transfers, were not implemented by the agency. Brown and Thompson (2012a), Multimodal bus and light rail Better rail performance and higher systems: ridership are associated with several Jaroszynski and Brown (2014) - 5 systems, 2006 in the 2012 study internal factors, including: - 8 systems, 2011, in the 2014 study characteristics of access to rail stations (feeder bus routes, park-and- Investigated the influence of service ride facilities), frequency, coverage planning decisions on rail of population and employment, performance infrastructure design balancing access and costs
The other important feature of the decentralized systems, which allows them to yield increased performance and ridership, is the replacement of some of the radial, downtown- oriented routes, with crosstown services. That feature is particularly important in the case of multimodal bus and rail networks. Rail transit has a much higher capacity than regular bus routes, and therefore, it requires a sufficient amount of riders to justify the investment expenses.
In the case of traditional, radial networks, buses often duplicate rail lines, taking away the ridership from the rail services that are running in the same direction. In decentralized networks, 64
bus services are oriented primariarily on providing access to rail stations, which allows funneling
larger groups of riders into thee rail lines and consequently increasing the railail ridership. They
simultaneously function as crosstsstown services, providing direct, convenient connnnections between
the suburban residential and emplployment locations.
Figure 3.1 illustrates thehe traditional, downtown-oriented radial transitit network n and the
conceptual design of a decentralizalized, multi-destination network.
Figure 3.1. Schematic Conceptpt of a Radial (left) and Decentralized (right) Transit Network. (Source: Own work, based on BroBrown and Thompson, 2008c)
Note: This diagram illustrates a common structure of a multimodal bus and railrai network. If no rail is present in the transit systemtem, all lines represent bus routes.
One more advantage of imimprovement strategies based on adjusting internernal factors is that
such decisions are usually underer the control of planners and policy-makers, andan do not require
any coordination and cooperatiotion with third-party, private entities, such as landlan developers or
65
property owners. Transit service coverage is being adjusted to the existing distribution of trip
attractors and to the current land use patterns. That makes planning improvements based on
internal decisions easier to implement if compared with other strategies, focused on external
factors. Those other strategies usually assume interventions on the real-estate market (e.g. by
creating transit-oriented development or enacting specific land use regulations), which require
collaboration with third-party stakeholders, for example, land developers (Brown and Thompson,
2012b)
Overall, the findings on the importance of service planning decisions are contrary to the
some common transit planning paradigms, mentioned in the previous paragraph. They indicate
that while high-density urban core is still an important trip attractor and therefore, it should enjoy good levels of service, transit systems should also provide convenient travel opportunities between all major residential and employment trip destinations in a particular city, which include also the dynamically growing suburban locations. While density and urban form play an important role in determining transit demand, and density-based strategies such as TOD or pedestrian access improvements undoubtedly have a positive impact on transit ridership, they are expected to be much more successful if the offered transit service provides convenient access to all popular destinations.
Although the literature provides strong evidence for the advantages of adopting transit network structures to the current urban spatial phenomena, it simultaneously omits some important aspects of these strategies. The evidence supporting the positive outcomes of these strategies is based primarily on measuring ridership and productivity (service effectiveness) with indicators such as the “number of passengers per service mile” and “volume of passenger miles eper service miles” variables (as illustrated by the literature review presented in Table 3.1). Some
66 of the referred studies assessed the current operating costs as well. While all these variables are appropriate measures of the service utilization and its usefulness for the society, they do not reflect the overall economic costs and social benefits of particular network planning strategies.
The philosophy of providing expanded, decentralized transit services might require overwhelming capital investments, which are not captured by any of the performance indicators used by the up-to-date studies. On the other hand, as mentioned in Chapter 2, transit effectiveness assessments should also consider non-direct benefits, which might outbalance the expenses and justify a particular planning decision. The direct fare revenues, not included in the previous studies, might be also higher for the networks employing the discussed internal decisions, as more riders enjoy the frequent and accessible service.
As already mentioned in Chapter 1, one of the major objectives of this study is to fill the aforementioned research gap and to expand the findings on the importance of service planning decisions by adding an additional, very important dimension to their evaluation, which are the overall economic outcomes. This chapter addresses that issue by examining the relationship between the net benefits generated by transit systems and the planning decisions made with regards to these systems. Additionally, it expands the previous evaluations of the operating costs of specific route and service categories.
3.2 Research Methodology
The author decided to use a panel regression model as the primary research method in this stage of the study. Panel regression models are commonly used to analyze cross-sectional
(longitudinal) data changing in two dimensions: across time and across different observations groups such as people, cities, transit systems (Frees, 2004; Hsiao, 1999). In the case of this study,
67
a single-year snapshot of the 13 systems would not provide sufficiently large observation group
for conducting a statistically significant regression analysis and could raise other questions
regarding the study’s validity. Therefore, the author decided to include observations made
throughout multiple years, and as explained earlier in the text, selected the 2001 – 2011 period
based on data availability. The analysis included a total of 123 annual observations.
The statistical model investigated the regression of net benefits on several variables
representing the factors explaining the transit ridership. While the primary goal of the analysis
was to investigate the relationship between net benefits and market-oriented network planning
strategies, other explanatory variables were also included in the equation to reinforce the model’s
explanatory power and increase validity of the analysis. As discussed earlier in the text, service
planning decisions emerge as important, yet not the only significant determinants of transit
outcomes.
The additional route-level analysis was based on calculating average values of the
ridership, farebox recovery, and operating costs per revenue mile for specific route categories,
reflecting the role of particular routes in the network structure and their connectivity with the rail lines, and the performance and economic indicators were compared for each of these categories.
The methodology for the route-level analysis is discussed along with the route-level assessment in Section 3.4.
3.2.1 Panel Regression Model
There are two major categories of panel regresison models: fixed-effects and random- effects. Random-effects models are used if the units of analysis are samples from a larger population. While the analyzed cities are not the only ones with transit, the study focuses only on that specific type of transit systems, and therefore, none of the cities was selected randomly for
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the analysis. Therefore, the analysis has utilized a fixed-effects model. Additionally, the model
was designed to capture possible factors, equally affecting all observation groups (case cities),
which are not represented by any of the explanatory variables. Specifically, dummy variables for
each period of analysis were added to the model to control for time-specific effects (Markus,
1979; Woolridge, 2002). Basing on these assumptions and additional literature (Schmidheiny,
2013), the following regression equation was adopted:
, … , … … Equation 3.1
where: , , ,…,
, ,…, , ,…, ,
,…, , ,
,
,
Equation 3.1 specifies a model based on Ordinary Least Squares method, in which the
error terms are assumed to be independent and individually distributed (iid). According to the
literature, such assumption often leads to underestimating the error terms and ignoring
heteroskedasticity, especially if there has been a repeatable under- or overestimation of the input
variables (Hoechle, 2007; Woolridge, 2009). For example, the socioeconomic data is usually
based on surveys, which are conducted on sample populations, and which might be biased by
individuals underreporting their annual income. Similar issues might arise within the agency
performance data, which might also carry some repeatable bias. For that purpose, the author
decided to include an additional iteration of the model, which clusters the error terms and .
Specifically, in the second iteration the term in Equation 3.1 is replaced with cluster-
robust aggregate standard error ( ).
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3.2.2 Model Specification: Overview and Discussion of Model Variables
The panel regression model utilized in this study includes net benefits per passenger mile as the dependent variable, and multiple explanatory variables, which reflect both internal and external determinants of transit performance and economic outcomes. For the internal category, which is the major focus area of this analysis, following variables illustrating the effects of different network planning strategies were selected: level of network decentralization (measured as the percentage of service volume not entering the CBD, in revenue miles), service density (in revenue miles per service area), and average headway. These variables correspond with the previous scholarship findings on the importance of planning strategies, which emphasize the importance of service decentralization, coverage, and high frequency. Table 3.2 presents the full list of variables included in the model, refers their sources, and discusses their hypothetical influence on the dependent variable.
Table 3.2 Overview and Discussion of Model Variables
Variable Definition, Discussion of Hypothesized Relationship with the Dependent Variable, Data Source Dependent variable ben_per_pm Net benefits per passenger mile (in dollars); obtained in the previous phase of the study (Chapter 2) Explanatory variables representing network and service characteristics (observations presented in Table 3.3) Dec Decentralization ratio, representing the percentage of service that is not entering the CBD. As explained earlier in the text, the higher decentralization ratio is expected to yield higher net benefits. Routes were stratified into “non-CBD” and “CBD” categories. Ratio of revenue miles between those two categories was calculated
Continued on Next Page
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Table 3.2 - Continued
Dec whenever data were available (Transit Agency Data, 2014). In other cases, the (continued) author consulted the historical schedules (Archive.org, 2014) and estimated the ratio basing on the levels of service: routes were weighted depending on service frequency, next, total numbers of Non-CBD and CBD routes were calculated. CBD definition was based on Census of Retail Trade (US Census, 1982)– the latest official, comprehensive source of CBD boundary designations, providing the census tracts included in the CBD. The author reviewed the 1982 boundaries and updated them whenever necessary, using aerial photography (Google Maps, 2014). As indicated by literature, there is no single up-to-date CBD definition and common sense should be used for defining CBD boundaries (Demographia, 2000). revpersqm Revenue miles per service area [in square miles] – represents service density. Higher service density usually indicates better adaptation of the service to the current spatial trends, and therefore, is expected to be positively correlated with the net benefit amount. Source: Revenue Miles and Service Area variables from FTIS (2014). Some of the observations were adjusted basing on GIS files provided by transit agencies (the observations available in FTIS were not representing the actual service areas). headway Average headway [in minutes] Lower headways reduce the waiting and transfer times, making the transit service more attractive (and therefore, increasing the social benefits); on the other hand, more frequent service usually requires additional operating expenses. (FTIS, 2014) Explanatory variables representing external determinants of transit performance pop_dens Population density in the core county of the metropolitan area (US Census, 2014) As large portions of the metropolitan areas are not served by transit, and the Census-designated metropolitan areas are usually much larger than the actual urbanized areas; therefore, the author selected the core county to keep consistency between cases.
Continued on Next Page
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Table 3.2 – Continued unempl Unemployment rate in the metropolitan area (American Community Survey ,2014; Bureau of Labor Statistics 2014) Lower employment is expected to reduce the volume of commuting trips, including transit trips, consequently, reducing transit economic outcomes.
med_inc Median Household Income in the metropolitan area (American Community Survey, 2014; Bureau of Economic Analysis, 2014) Higher incomes are associated with improved economic activity, which yields a larger volume of urban travel, including transit trips; therefore, income is expected to positively influence transit feasibility
0veh_hh Percentage of Zero-Vehicle Households (American Community Survey, 2014) Higher percentage of transit-dependent population should naturally result in more transit, and indirectly, in higher transit benefits. gas Average Gas Price [in dollars per gallon] in the metropolitan area (EIA, 2014) Higher gas prices are expected to generate additional transit ridership, and consequently, higher benefits. tti TTI (Travel Time Index). TTI is an indicator of the congestion volume (TAMU, 2012). TTI is a ratio of the actual driving time over the free-flow time (hypothetical driving time in non-congested conditions). TTI value is expected to be positively correlated with the value of net benefits, as higher congestion attracts more people to use transit (considering that this study analyzes rail systems).
Time-effects Time dummy variables allow to determine whether there are any factors not dummy measured by the explanatory variables that are affecting all cities the same way, variables as discussed earlier in the text. Dummy variables are generated by “Stata” software package during model estimations.
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Table 3.3 Observations for Variables Reflecting Internal Factors
Decentralization ratio (Percentage of Total Service Volume Allocated to Routes Not Serving the CBD) 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Buffalo 30% 30% 30% 30% 31% 32% 33% 37% 40% 44% 48% Charlotte 27% 28% 28% 29% 29% Dallas 54% 53% 53% 52% 53% 55% 57% 59% 62% 64% 66% Denver 61% 62% 62% 63% 63% 64% 64% 62% 61% 59% 58% Houston 35% 35% 37% 38% 38% 37% 37% 37% Minneapolis 37% 38% 37% 37% 37% 37% 36% 36% Phoenix 67% 64% 62% Pittsburgh 18% 18% 19% 17% 16% 14% 14% 14% 14% 14% 15% Portland 46% 47% 45% 42% 40% 38% 35% 36% 37% 38% 39% Sacramento 60% 61% 63% 63% 63% 63% 62% 61% 60% 63% 65% Salt Lake City 40% 40% 40% 42% 47% 53% 54% 54% 54% 54% 59% San Diego 65% 65% 65% 65% 65% 65% 64% 63% 62% 61% 60% St. Louis 60% 60% 63% 66% 69% 74% 74% 74% 74% 74% 75%
Average headway (in minutes) 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Buffalo 26.1 24.0 24.7 24.5 23.9 22.6 22.6 23.0 21.6 21.9 23.8 Charlotte 28.6 22.3 19.6 21.9 21.5 Dallas 13.3 14.4 13.6 10.0 10.1 11.1 11.6 3.6 12.9 13.4 11.9 Denver 22.3 23.9 22.0 20.6 20.2 21.8 21.2 18.8 20.4 21.4 21.8 Houston 9.5 14.4 14.5 15.9 17.3 16.0 16.2 16.2 Minneapolis 18.0 18.4 16.9 16.8 18.2 19.2 16.9 17.2 Phoenix 27.0 24.2 21.8 Pittsburgh 17.4 16.7 16.9 16.2 17.8 17.9 19.2 19.5 20.1 19.3 20.6 Portland 12.6 10.9 10.2 10.4 10.2 10.5 10.6 10.4 10.3 10.8 11.1 Sacramento 37.0 34.4 33.9 23.8 31.4 33.1 33.3 37.8 32.8 32.5 30.9 Salt Lake City 13.0 13.1 13.8 13.4 14.1 13.9 14.2 16.3 16.7 17.4 21.3 San Diego 21.0 25.0 26.7 24.9 34.9 32.5 29.2 22.8 21.4 25.5 21.9 St. Louis 19.4 20.8 21.6 26.4 27.0 25.9 22.2 23.0 23.7 27.0 22.0
Service Density (Revenue Miles per Service Area Square Mile, 000s) 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Buffalo 191 185 182 175 190 199 208 226 252 225 233 Charlotte 143 177 192 187 200 Dallas 321 289 395 365 415 366 352 322 290 280 324 Denver 192 186 185 204 223 237 272 277 270 276 295 Houston 404 389 417 348 369 293 272 264 Minneapolis 499 624 673 722 785 646 702 700 Phoenix 452 416 408 Pittsburgh 362 323 285 289 269 273 305 253 256 244 209 Portland 526 601 608 637 633 663 631 650 709 683 672 Sacramento 224 192 193 233 184 188 190 213 229 213 202 Salt Lake City 81 90 104 89 107 166 179 175 117 131 138 San Diego 573 509 491 503 516 539 543 543 570 368 377 St. Louis 297 355 340 339 356 351 376 396 495 351 405 Source: See Table 3.2
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For the category of external factors, the selection process was more complicated, as the
literature discusses many possible external determinants of transit outcomes. Basing on a
literature compendium discussing and summarizing transit ridership determinants (Taylor and
Fink, 2003), the author attempted to eliminate variables illustrating similar, identical or
interdependent phenomena. The variables incorporated into the model represent several major
types of external transit performance determinants: spatial characteristics (population density),
economic setting (median income, unemployment), dependency on transit services (zero-vehicle
households), and characteristics of automobile travel (gas prices, congestion).
For the purpose of examining possible multicollinearity between the selected variables,
the author conducted a pairwise correlation test. As for all other model estimation, the analysis
was performed by “Stata” statistical software package. Table 3.4 presents the results of
multicollinearity analysis.
Table 3.4 Pairwise Correlation for Explanatory Variables
revm dec headway med_inc pop_dens unempl 0veh_hh gas tti persqm Decentralization dec 1.00 Headway headway 0.35 1.00 Service Density revmpersqm 0.02 -0.29 1.00 Median Income med_inc 0.41 0.10 0.33 1.00 Pop. Density pop_dens 0.18 -0.25 -0.04 0.21 1.00 Unemployment unempl 0.07 0.09 0.04 0.13 -0.05 1.00 Zero-veh. H-holds 0veh_hh -0.53 0.14 -0.03 -0.57 -0.28 0.02 1.00 Gas Price gas 0.02 0.06 0.10 0.49 -0.03 0.31 0.05 1.00 Congestion index tti -0.27 -0.39 0.18 -0.18 0.47 -0.37 -0.09 -0.34 1.00
For most of the pairs, the correlation coefficient is low and does not indicate
multicollinearity; only for two pairs (0veh_hh with dec and 0veh_hh with med_inc) it exceeds
0.5 but still falls into the range of moderate, acceptable level of correlation (R < 0.6). The author
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decided to leave all variables in the model, simultaneously taking into consideration possible
influence of multicollinearity during the further stages of analysis.
Following the previous findings on the importance of service planning decisions, the author hypothesized that all three variables representing these decisions will have a significant influence on the dependent variable. Specifically, revmpersqm and dec should be positively correlated with the amount of net benefits, while headway is supposed to have an opposite sign, as longer headways are less attractive for transit patrons, and therefore, should result in lower ridership and consequently, lower consumer surplus and other benefits (although, on the other hand, less service could reduce the operating expenses). These hypotheses were verified by the model estimation, discussed in the following section.
3.3 Model Results
The model has been estimated in the Stata software package, using commands for panel regression (xtreg), with fixed effects (fe) and (i.year). As discussed in Section 3.2, the model was estimated twice, with ordinary least squares (OLS) standard errors, andwithrobust standard errors. Model results are summarized in Table 3.5. The null hypothesis, stating that the model does not explain at all the changes in the dependent variable has been rejected.
The results of the OLS estimation indicate that two variables representing the service planning variables – revmpersqm (service density) and headway – are significantly correlated with the net benefits, while the dec variable (percentage of routes not entering the CBD) has no significant influence on the social benefits generated by the analyzed systems. Replacing OLS errors with the cluster-robust errors resulted in an overall drop of the t-scores. In the second iteration, revmpersqm (service density) is still located within acceptable confidence interval
(90%). The influence of average headway becomes barely significant (confidence 80%),
75 however, headway is one of the few variables that have relatively unbiased observations: agencies can easily measure their average headways. The cluster-robust errors model does not bring any significant changes for the estimated influence of decentralization variable: it remains to appear as highly insignificant predictor of net benefits.
Table 3.5 Model Results (Internal Service Decisions)
With OLS standard errors
Dependent Variable: ben_per_pm (net benefits per passenger mile)
Variable Coefficient Std. error t P>[t] Number of observations: 123 R-square: within = 0.4219 Decentralization Ratio -0.100 0.314 -0.32 0.75 between = 0.3253 Average headway -0.010 0.005 -2.12 0.04 overall = 0.3024 Service Density 0.001 0.000 3.50 0.00 Correlation:-0.4262
Population Density 0.199 0.229 0.87 0.39 Unemployment Ratio 0.022 0.015 1.47 0.15 F(19,91) = 3.49 Zero-vehicle Households -0.018 0.026 -0.70 0.49 Prob > F = 0.0 Median Income 0.011 0.009 1.23 0.22 Average Gas Price 0.105 0.177 0.59 0.56 Travel Time Index (TTI) -2.038 0.633 -3.22 0.00
Dummy time variables: sigma_u = 0.24 2002 -0.076 0.084 -0.90 0.37 sigma_e = 0.11 2003 -0.165 0.059 -2.79 0.01 rho = 0.83 2004 -0.241 0.059 -4.08 0.00 2005 -0.250 0.084 -2.98 0.00 2006 -0.288 0.161 -1.79 0.08 F test that all u_i=0: 2007 -0.336 0.166 -2.02 0.05 F(12, 91) = 22.54 2008 -0.571 0.299 -1.91 0.06 2009 -0.525 0.132 -3.97 0.00 Prob > F = 0.0000 2010 -0.638 0.249 -2.57 0.01 2011 -0.648 0.307 -2.11 0.04
Constant 1.453 0.971 1.50 0.14
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Table 3.5 - Continued
With Cluster-Robust Standard Errors (C-R S.E.)
Dependent Variable: ben_per_pm (net benefits per passenger mile)
Variable Coefficient C-R t P>[t] S.E. Additional parameters Decentralization Ratio -0.100 0.621 -0.16 0.88 (e.g. R-sq, rho) Average headway -0.010 0.007 -1.36 0.20 unchanged. Service Density 0.001 0.001 1.76 0.10
Population Density 0.199 0.388 0.51 0.62 Unemployment Ratio 0.022 0.023 0.96 0.36 Zero-vehicle Households -0.018 0.021 -0.88 0.40 Median Income 0.011 0.011 0.98 0.34 Average Gas Price 0.105 0.154 0.68 0.51 Travel Time Index (TTI) -2.038 1.173 -1.74 0.11
Dummy time variables: 2002 -0.076 0.074 -1.02 0.33 2003 -0.165 0.057 -2.92 0.01 2004 -0.241 0.039 -6.14 0.00 2005 -0.250 0.057 -4.40 0.00 2006 -0.288 0.135 -2.13 0.05 2007 -0.336 0.170 -1.97 0.07 2008 -0.571 0.316 -1.80 0.10 2009 -0.525 0.124 -4.22 0.00 2010 -0.638 0.235 -2.72 0.02 2011 -0.648 0.313 -2.07 0.06
Constant 1.453 1.514 0.96 0.36
The results for the revmpersqm and headway variables are meeting the hypothetical expectations; higher service density and lower headways should yield higher transit benefits.
However, the ratio of decentralization (dec) is not significant, indicating that the decentralization ratio is not an important determinant of transit benefits. Additionally, dec has a negative sign, suggesting that transit benefits are lower in cities that operate decentralized networks; however, as already noticed, that relationship is not statistically significant. Overall, these results are
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somehow surprising, although one of the previous studies (Thompson and Brown, 2006) has also
determined that decentralization is not a significant predictor of ridership in the areas where the
population exceeds 1 million (all cases in this study belong to that category). It is possible that in
larger areas, higher volume of decentralized service does not necessarily mean that the
opportunities for multi-destination travel are well designed (the volume of non-CBD service is
high due to the area’s size, but that service is not necessarily frequent and well connected to the
important trip attractors). The other-than-hypothesized results for the dec ratio might also be
explained by limitations in obtaining accurate CBD boundaries, discussed earlier in the text. A
more detailed investigation of the relationship between dec and other explanatory variables is
discussed in the following paragraphs.
Among the predictors representing external factors, only the tti, reflecting the volume of
congestion, is significantly correlated with transit benefits, and it has a negative sign, which
means that the average transit benefits are higher in less congested cities. Such takeaway might
be surprising, as it could be expected that multimodal transit systems are more beneficial for the
society in cities that are dealing with traffic problems. However, the results appear to reveal a
reverse relationship: when transit attracts more patrons, and therefore yields more benefits,
congestion is lower. When the OLS errors are replaced with robust errors, significance of tti , is reduced; however, it is still within acceptable confidence interval (89%). Other external variables remain to be insignificant in the cluster-robust errors iteration.
The remaining exogenous variables appear to have no significant influence on the amount
of net benefits. Additionally, some of their coefficients seem to reflect contradictory phenomena;
for example, both unemployment and median income have positive signs, while higher unemployment is usually associated with weaker economic conditions, and consequently, lower
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median income. Some of the previous studies indicated the external factors are not necessarily
determinative of transit outcomes and the model results seem to reflect such a situation. On the
other hand, the unexpected results might have been caused by simplifications and generalizations
made in the research design. Due to the longitudinal character of the study and inclusion of over
100 observations, the socioeconomic indicators were collected at the metropolitan area level,
which is usually larger than the transit service area. Future research should examine more deeply
the role of external factors in determining the benefits and costs of particular transit investments.
After obtaining other-than-expected results for the dec (decentralization) variable, the
author decided to conduct an additional analysis on the role of statistical relationships between
dec and other variables. Such investigation should reveal possible dataset inconsistencies that
might have biased the model results. Specifically, two scenarios were analyzed for each
explanatory variable other than dec: 1) removing one of the variables from the model, 2) leaving only one of the variables and dec in the model. Table 3.6 presents the results of that additional analysis.
Table 3.6 Additional Statistical Analysis of the Role of Decentralization Ratio
variable x1 t-value for dec t-values for dec and x1 in case of after removing regressing only dec and x1 (all other variable x1 variables removed) t dec t x
headway -0.47 0.49 -1.63 revmpersqm 0.19 -0.16 4.05 pop_dens -0.54 0.27 0.14 unempl -0.68 0.63 2.17 0veh_hh -0.20 0.36 0.53 med_inc -0.52 0.23 -0.36 gas -0.53 0.23 -0.07 tti 0.24 -0.64 -2.68
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The results of the additional analysis indicate that removing any other explanatory variables from the model does not make dec a significant predictor of net benefits. They also indicate that dec coefficient changes its sign to positive after removing revmpersqm and tti.
Simultaneously, dec has a negative coefficient when paired with these two variables, and a positive sign in all other pairs. These two variables appear to be responsible for the negative sign of the dec coefficient in the model. It seems that both revmpersqm and tti have an opposite influence on the benefit amount than the decentralization ratio, although that relationship is not statistically significant.
3.4 Route-level Feasibility and Performance Analysis
The author conducted a supplementary route-level analysis of average operating costs, attempting to expand the understanding of the relationship between planning decisions and the economic outcomes of transit. As discussed earlier in the text, one of the major goals of this research was to assess the economic outcomes of internal planning strategies offered by some scholars. This analysis was performed to provide additional insights on that issue by comparing the economic and ridership indicators between specific bus route categories, created to reflect specific internal planning strategies, discussed throughout the study. Specifically, following categories are analyzed: 1) CBD-bound radial routes vs. crosstown (Non-CBD) routes, 2) routes providing access to a light rail station vs. other routes, and 3) routes providing access to a light rail station located outside the CBD, and not entering the CBD vs. other routes. These comparisons are important from the perspective of evaluating the planning strategies discussed in the literature review, which encompass multi-destination transit services, expanding the coverage over the emerging urbanized areas located in the outskirts, and orienting the bus service towards
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“feeding” the rail lines. Transit systems that have adopted these strategies are expected to have higher shares of the non-CBD service, more service headed to rail stations, and more service connected to rail, but not serving the CBD. As all rail networks serve the CBD regardless of the bus network structure, and the CBD is the central point of the rail networks consisting of more than one line, bus routes in decentralized multimodal transit systems are supposed to function primarily as rail “feeders”; that is, routes which connect to the nearest rail station, and simultaneously provide crosstown service.
The key point of this analysis is to determine whether higher shares of service in those particular categories are associated with increased ridership and economic feasibility. The analysis focused on two basic economic indicators of transit operations: farebox revenue and operating costs. Farebox revenue is the ratio of the amount paid by patrons in fares over the costs of operating a particular route or service. Opposite to the benefit-cost analysis, this evaluation will focus exclusively on the direct revenues and operating costs, as some of the data required for conducting the BCA are unavailable at the route level. The analysis has simultaneously included an assessment of service supply and consumption in each of the route categories, reflected with revenue miles of provided service, and boardings (unlinked passenger trips), representing the ridership volume. As already mentioned earlier in the text, the “boardings” measure is not an perfect ridership indicator (as it does not account for transfers), but no other measures were available at the route level.
Statistical and geospatial data provided by respective agencies and other related sources
(Transit Agency Data, 2014) served as the foundation for this analysis. Routes in each of the case systems were stratified by two independent categories: CBD routes & Non-CBD Routes, and
Routes connecting to rail & other. The methodology for distinguishing the CBD routes was
81 already discussed in Table 3.2 in the previous section. For the “rail connectivity” category, the selection has been made using GIS data. All routes passing through a 1/4-mile buffer surrounding any of the light rail stations are classified as “connecting to rail”. The third category, routes serving rail, but not CBD vs. remaining routes, was created by merging the first two categories.
Due to limited data availability, the timeframe of the analysis is limited to a single year
(2011). Additionally, two systems, Pittsburgh and Salt Lake City, were excluded from the analysis. The author was unable to obtain any detailed route-level data from Pittsburgh. In Salt
Lake, many bus routes were significantly modified in August 2011, along with the opening of two new light rail lines and the route-level data provided by the agency was not stratified in regards to the pre- and post-change periods. For the following five cities: Charlotte, Dallas,
Portland San Diego, and St. Louis, the fare revenue and farebox recovery data were unavailable at the route level, and, therefore were not included in the analysis. The analysis focuses only on the routes operated by the major transit agency in a particular area (in all of the cases, major agencies operate at least 90% of the entire transit service volume).
The results of the analysis are presented in Table 3.7 (service characteristics) and 3.8
(economic evaluation). The service characteristics indicate that some cities orient most of their bus services toward the CBD, while others focus more intensively on the outer, suburban areas, as already determined in the previous stage of this study. In all cities, except for Denver and
Phoenix, a vast majority of bus services are connected to at least one rail station. In Denver and
Phoenix, that ratio is slightly lower, but still exceeds 60%. In both cases, the lower-than-average share of routes connecting to rail might be explained by the large size of service areas, and by the presence of bus services in the outskirts located remotely from the areas served by rail.
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Table 3.7 Service Characteristics for Specific Route Categories
Share of Decentralized Service Share of Rail-feeding Service Share of Non-CBD Rail-feeding Service
Non-CBD Service CBD Service Service Headed to Service Not Headed to Bus Service Headed Remaining Service Rail Stations Rail Stations to Rail Stations but Not Serving the CBD
System % of Service Volume(Revenue Miles) Buffalo 48% 52% 85% 15% 33% 67% Charlotte 24% 76% 89% 11% 13% 87% Dallas * 66% 34% 93% 7% 59% 41% Denver 58% 42% 67% 33% 27% 73% Houston 36% 64% 84% 16% 24% 76% Minneapolis 36% 64% 82% 18% 18% 82% Phoenix 69% 31% 64% 36% 37% 63% Portland 39% 61% 97% 3% 35% 65% Sacramento 72% 28% 98% 2% 60% 40% San Diego 60% 40% 88% 12% 51% 49% St. Louis 75% 25% 94% 6% 71% 29% * - Revenue Hours for Dallas % of Service Consumption (Boardings) Buffalo 43% 57% 96% 4% 39% 61% Charlotte 17% 83% 92% 8% 9% 91% Dallas 65% 35% 95% 5% 61% 39% Denver 50% 50% 75% 25% 26% 74% Houston 43% 57% 82% 18% 28% 72% Minneapolis 30% 70% 88% 12% 19% 81% Phoenix 61% 39% 66% 34% 32% 68% Portland 37% 63% 99% 1% 35% 65% Sacramento * 71% 29% 98% 2% 62% 38% San Diego 58% 42% 87% 13% 48% 52% St. Louis 63% 37% 95% 5% 61% 39% * - Passenger Miles for Sacramento Average Ridership (Boardings per Revenue Mile) Buffalo 2.78 3.34 3.46 0.90 3.64 2.79 Charlotte 1.25 1.94 1.83 1.29 1.22 1.85 Dallas * 18.62 19.24 19.27 13.01 19.29 18.16 Denver 1.45 2.00 1.88 1.28 1.62 1.70 Houston 1.92 1.46 1.58 1.85 1.94 1.53 Minneapolis 2.47 3.18 3.14 1.93 2.98 2.91 Phoenix 1.68 2.35 1.93 1.81 1.65 2.03 Portland 2.97 3.25 3.21 1.24 3.13 3.14 Sacramento * 8.23 8.74 8.42 6.09 8.58 8.07 San Diego 2.76 2.95 2.82 2.95 2.72 2.96 St. Louis 1.31 2.28 1.58 1.25 1.35 2.07 * - Boardings per Revenue Hour for Dallas, Passenger Miles per Revenue Mile for Sacramento Source: Transit Agency Data (2014)
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Substantial differences between the cities are noticed in the third panel, illustrating the share of routes connected to rail, but not serving CBD. This share is unsurprisingly low for cities that operate smaller, less extensive networks, such as Buffalo, Charlotte, Houston, and
Minneapolis; however, it is also relatively low for Denver and Portland, which operate extensive rail networks. While Denver operates much of its service far away from the rail network, as already noticed, Portland seems to offer relatively long headways on its suburban bus routes. It also concentrates most of its bus services in the inner parts of the metro area. Most of the high frequency routes in Portland eventually reach the central city, even if they serve simultaneously as rail feeders in the outskirts. In three of the cities (Dallas, Sacramento, and St. Louis), the majority of bus service is heading to the rail stations but not the CBD. All of these cities eliminated many of the radial, CBD-bound bus routes, along with the rail system’s expansion.
For all cities except for Houston, the ratio of Non-CBD service consumption is lower than the ratio of Non-CBD service volume; however, it seems that for decentralized networks
(over 50% of Non-CBD service) these differences are smaller. Average ridership, illustrated with boardings per revenue mile is also lower on the Non-CBD routes; however, the differences are not substantial in most of the cases.
For the rail connectivity comparison, the share of service demand on the routes connected to rail stations is, in almost all cases, higher than the percentage of service supplied by those routes. In addition, the average ridership is higher for the rail-bounded routes, except for
Houston and San Diego. These results indicate that routes providing connections to rail usually attract more passengers than the remaining fixed-route services. For the third group of route categories, there is no single pattern of relationship between the service consumption and supply ratios; generally, the ratio values are at very similar or identical levels.
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Table 3.8 Economic Indicators for Specific Route Categories
Share of Decentralized Service Share of Rail-feeding Service Share of Non-CBD Rail-feeding Service
Non-CBD Service CBD Service Service Headed to Service Not Headed to Bus Service Headed Remaining Service Rail Stations Rail Stations to Rail Stations but Not Serving the CBD System Farebox Recovery Buffalo 0.26 0.29 0.30 0.11 0.32 0.26 Charlotte no data no data no data no data no data no data Dallas no data no data no data no data no data no data Denver 0.26 0.34 0.30 0.28 0.24 0.31 Houston 0.15 0.20 0.19 0.13 0.16 0.19 Minneapolis 0.24 0.32 0.30 0.24 0.25 0.31 Phoenix 0.22 0.28 0.24 0.22 0.22 0.24 Portland no data no data no data no data no data no data Sacramento 0.21 0.22 0.22 0.20 0.22 0.21 San Diego no data no data no data no data no data no data St. Louis no data no data no data no data no data no data
Operating Cost per Revenue Mile Buffalo $ 10.24 $ 11.24 $ 11.23 $ 8.15 $ 11.21 $ 10.55 Charlotte no data no data no data no data no data no data Dallas no data no data no data no data no data no data Denver $ 7.40 $ 9.07 $ 8.55 $ 7.18 $ 7.89 $ 8.18 Houston $ 8.45 $ 7.67 $ 7.86 $ 8.47 $ 8.41 $ 7.81 Minneapolis $ 8.98 $ 10.71 $ 10.51 $ 8.15 $ 9.79 $ 10.16 Phoenix $ 6.83 $ 7.66 $ 7.10 $ 7.08 $ 6.74 $ 7.30 Portland $ 8.17 $ 8.89 $ 8.66 $ 7.22 $ 8.26 $ 8.81 Sacramento * $ 10.91 $ 11.82 $ 11.17 $ 10.56 $ 11.13 $ 11.20 San Diego $ 6.77 $ 7.62 $ 7.15 $ 6.80 $ 6.84 $ 7.38 St. Louis no data no data no data no data no data no data * - Operating Cost per Passenger Mile for Sacramento Fare Revenue per Revenue Mile Buffalo $ 2.70 $ 3.25 $ 3.36 $ 0.87 $ 3.54 $ 2.71 Charlotte no data no data no data no data no data no data Dallas no data no data no data no data no data no data Denver $ 1.89 $ 3.06 $ 2.57 $ 1.98 $ 1.86 $ 2.57 Houston $ 1.24 $ 1.55 $ 1.50 $ 1.12 $ 1.30 $ 1.48 Minneapolis $ 2.17 $ 3.43 $ 3.20 $ 1.92 $ 2.42 $ 3.10 Phoenix $ 1.48 $ 2.12 $ 1.74 $ 1.58 $ 1.50 $ 1.79 Portland no data no data no data no data no data no data Sacramento * $ 2.32 $ 2.63 $ 2.41 $ 2.10 $ 2.42 $ 2.38 San Diego no data no data no data no data no data no data St. Louis no data no data no data no data no data no data * - Fare Revenue per Passenger Mile for Sacramento
Continued on Next Page
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Table 3.8 - Continued
Share of Decentralized Service Share of Rail-feeding Service Share of Non-CBD Rail-feeding Service
Non-CBD Service CBD Service Service Headed to Service Not Headed to Bus Service Headed Remaining Service Rail Stations Rail Stations to Rail Stations but Not Serving the CBD
System Operating Cost per Boarding Buffalo $ 3.69 $ 3.36 $ 3.25 $ 9.05 $ 3.08 $ 3.78 Charlotte no data no data no data no data no data no data Dallas no data no data no data no data no data no data Denver $ 5.11 $ 4.53 $ 4.56 $ 5.61 $ 4.87 $ 4.80 Houston $ 4.41 $ 5.26 $ 4.97 $ 4.57 $ 4.34 $ 5.11 Minneapolis $ 3.64 $ 3.37 $ 3.35 $ 4.23 $ 3.28 $ 3.49 Phoenix $ 4.07 $ 3.26 $ 3.67 $ 3.92 $ 4.09 $ 3.60 Portland $ 2.75 $ 2.74 $ 2.70 $ 5.81 $ 2.63 $ 2.80 Sacramento * $ 1.32 $ 1.35 $ 1.33 $ 1.73 $ 1.30 $ 1.39 San Diego $ 2.45 $ 2.58 $ 2.53 $ 2.30 $ 2.52 $ 2.49 St. Louis no data no data no data no data no data no data * - Operating Cost per Passenger Mile for Sacramento Fare Revenue per Boarding Buffalo $ 0.97 $ 0.97 $ 0.97 $ 0.97 $ 0.97 $ 0.97 Charlotte no data no data no data no data no data no data Dallas no data no data no data no data no data no data Denver $ 1.30 $ 1.53 $ 1.37 $ 1.54 $ 1.15 $ 1.51 Houston $ 0.65 $ 1.07 $ 0.95 $ 0.60 $ 0.67 $ 0.97 Minneapolis $ 0.88 $ 1.08 $ 1.02 $ 1.00 $ 0.81 $ 1.07 Phoenix $ 0.88 $ 0.90 $ 0.90 $ 0.87 $ 0.91 $ 0.88 Portland no data no data no data no data no data no data Sacramento * $ 0.28 $ 0.30 $ 0.29 $ 0.34 $ 0.28 $ 0.30 San Diego no data no data no data no data no data no data St. Louis no data no data no data no data no data no data * - Fare Revenue per Passenger Mile for Sacramento Source: Transit Agency Data (2014)
The results of the economic feasibility assessment, presented in Table 3.8, are slightly different from the ridership patterns shown in Table 3.7. The farebox revenue (the percentage of operating costs covered with fares) is usually slightly lower for the Non-CBD routes in the
CBD/Non-CBD classification, and higher for routes connecting to rail stations (although not in the case of rail connectors running exclusively in the suburbs). Simultaneously, operating costs per mile for the Non-CBD routes are lower too, which means that it is cheaper to facilitate the
Non-CBD service than the CBD service, even though a smaller portion of the costs is covered
86 with fares. Therefore, the average subsidy (difference between fare revenue and operating costs) is comparable between CBD and Non-CBD routes. The lower panel of Table 3.7, presenting the costs and revenues per boarding, indicates that both the costs and the farebox revenue per unlinked trip are higher in the CBD category for some cities and almost identical for other ones.
In the categories based on rail connectivity, routes heading to rail stations generate higher operating costs per mile in most of the cities; however, the rail-bound routes running outside the
CBD have lower average expenses than the remaining services. For the “operating cost per boarding” classification, unit operating costs of rail-bound routes are substantially lower for most of the cities, except for Houston and San Diego, where the average ridership is lower on the routes connecting to rail. The costs and revenues per boarding are at comparable levels for rail feeders regardless, whether they serve or do not serve the CBD.
3.5 Conclusions
This chapter has focused primarily on evaluating the relationship between specific internal planning decisions and the amount of net benefits generated by a transit system. The model results indicate that more frequent and denser service have significant, positive influence on transit benefits, while the amount of service decentralization is insignificant. The results for frequency and service density correspond with the previous studies, which have identified their important role in influencing ridership and productivity (average vehicle load). The weaker-than- expected importance of decentralization, inconsistent with the hypothesis, is somehow surprising, as most of the discussed literature identified decentralization as one of the strategies positively influencing transit ridership and productivity (consequently, the author expected that decentralization would positively influence economic outcomes as well). On the other hand, a
87 simple overview of decentralization and net benefit values for particular cities, included in Table
3.9 does not indicate any clear patterns of relationship between those variables.
As illustrated by Table 3.9, some of the cities with higher decentralization ratios, including Dallas, Phoenix, Sacramento, and St. Louis, generate lower net benefits. On the other hand, two among the four most beneficial systems, Denver and San Diego, also direct over 60% of their service outside of the CBD. In the case of Dallas, the lower-than-expected benefits might be explained with very intensive rail network expansion: it seems that the ridership growth is slower than the dynamic of capital expansion, and the additional expenses are not compensated with increased rider benefits. Still, Sacramento and St. Louis expand their rail systems at a much slower pace than Dallas does, and they yield relatively low benefits. Simultaneously, Denver and
San Diego have a comparable pace of rail system growth as Sacramento and St. Louis, but they generate much higher benefits per passenger mile. On the other side, Portland, a system which is commonly recognized by scholars and planning practitioners as an exemplary case of a successful multimodal investment, generated the second-highest average amount of benefits, despite having relatively low shares of suburban, non-CBD service. Both the decentralization ratio and a brief overview of route schedules indicate that Portland focuses most of its frequent bus services within the inner metropolitan area (located no farther than approx. 10 miles from downtown), while the headways on routes serving the outlying areas are quite long.
Clearly, the level of decentralization itself does not appear to serve as an important determinant of net benefits in the case of bus and light rail systems, and cities with similar decentralization ratios observe substantially different economic outcomes of transit operations.
However, it seems that decentralization might successfully serve as an element of a broader strategy, combining several internal factors and resulting in higher ridership and better
88 performance, as well as, better economic results. Systems, where the estimated economic results
(net benefits) tend to be better than the average, feature mostly decentralized networks, along with other adjustments of the internal factors.
Table 3.9 Decentralization Ratio and Net Benefits
Buffalo Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix Pittsburgh Portland Sacramento Lake Salt Diego San Louis St. Net Benefits per Passenger Mile 2001 -$0.35 -$0.11 $0.02 -$0.03 $0.16 $0.04 -$0.26 $0.30 -$0.07 2002 -$0.52 -$0.26 -$0.01 -$0.14 $0.07 $0.09 -$0.29 $0.32 -$0.03 2003 -$0.60 -$0.25 -$0.09 -$0.16 $0.02 -$0.13 -$0.18 $0.27 -$0.11 2004 -$0.68 -$0.27 -$0.09 -$0.15 -$0.19 -$0.29 $0.03 -$0.33 -$0.34 $0.21 -$0.16 2005 -$0.68 -$0.27 -$0.12 -$0.24 -$0.08 -$0.39 -$0.06 -$0.44 -$0.21 $0.17 -$0.18 2006 -$0.73 -$0.23 -$0.11 -$0.18 -$0.07 -$0.47 -$0.04 -$0.41 $0.06 $0.14 -$0.23 2007 -$0.84 -$0.42 -$0.41 -$0.11 -$0.23 -$0.06 -$0.39 -$0.02 -$0.36 -$0.04 $0.29 -$0.22 2008 -$0.79 -$0.43 -$0.60 -$0.03 -$0.27 -$0.07 -$0.49 -$0.10 -$0.35 -$0.03 $0.27 -$0.21 2009 -$0.73 -$0.17 -$0.76 -$0.11 -$0.34 -$0.12 -$0.57 -$0.50 -$0.07 -$0.24 -$0.27 $0.34 -$0.27 2010 -$0.77 -$0.46 -$0.77 -$0.19 -$0.44 -$0.13 -$0.54 -$0.62 -$0.14 -$0.29 -$0.27 $0.29 -$0.33 2011 -$0.75 -$0.42 -$0.79 -$0.17 -$0.52 -$0.18 -$0.44 -$0.78 -$0.10 -$0.33 -$0.38 $0.49 -$0.34
Decentralization Ratio 2001 0.30 0.54 0.61 0.18 0.46 0.60 0.40 0.65 0.60 2002 0.30 0.53 0.62 0.18 0.47 0.61 0.40 0.65 0.60 2003 0.30 0.53 0.62 0.19 0.45 0.63 0.40 0.65 0.63 2004 0.30 0.52 0.63 0.35 0.37 0.17 0.42 0.63 0.42 0.65 0.66 2005 0.31 0.53 0.63 0.35 0.38 0.16 0.40 0.63 0.47 0.65 0.69 2006 0.32 0.55 0.64 0.37 0.37 0.14 0.38 0.63 0.53 0.65 0.74 2007 0.33 0.27 0.57 0.64 0.38 0.37 0.14 0.35 0.62 0.54 0.64 0.74 2008 0.37 0.28 0.59 0.62 0.38 0.37 0.14 0.36 0.61 0.54 0.63 0.74 2009 0.40 0.28 0.62 0.61 0.37 0.37 0.67 0.14 0.37 0.60 0.54 0.62 0.74 2010 0.44 0.29 0.64 0.59 0.37 0.36 0.64 0.14 0.38 0.63 0.54 0.61 0.74 2011 0.48 0.29 0.66 0.58 0.37 0.36 0.62 0.15 0.39 0.65 0.59 0.60 0.75
Except for the congestion measure, all predictors representing the external factors appear to have no significant influence on the amount of net benefits. While these exogenous variables were added mainly for the purpose of increasing the explanatory power of the regression model, and they are not the main subject of consideration in this study, it is still worthy to notice one
89 interesting result. The negative and statistically significant coefficient for the Travel Time Index
(tti). Even though that variable is considered as an external factor in the transit context, it somehow represents the characteristics of the road network, which are at least partially controlled by city officials and planners. The value of tti coefficient suggests that cities experiencing lower volumes of congestion operate transit systems that generate higher benefits.
Such results indicate that more attractive transit service yields lower congestion, as people have better alternatives to driving.
It is also important to notice, that the other-than-expected results for decentralization, and for some of the external factors might be caused by the research design, specifically, by adopting a multiple-year period of analysis and by limiting the analysis level to entire transit systems. A more detailed evaluation of planning decisions made within particular service areas might reveal a stronger influence of specific internal and external factors on transit benefits than determined by this analysis. The supplemental, route-level analysis attempted to deepen the understanding on the economic outcomes of particular service types, although it also carried some limitations, due to limited data availability. The route-level analysis suggests that in the majority of the analyzed metropolitan areas, non-CBD service is actually cheaper to operate on a revenue mile basis, while it is also slightly more expensive if evaluated on an unlinked passenger trip basis.
The farebox recovery is higher for the CBD routes, but the operating costs are higher as well.
Another interesting take-away from the route-level analysis is that the suburban rail connectors have lower operating costs than the remaining services, for both cost categories (per mile and per boarding). These costs are also usually lower if the suburban rail feeders are distinguished from the group of all routes serving a rail station. It means that if a route provides access both to the
CBD and to a rail station, it is actually more expensive to operate than if it would serve as a
90 suburban rail feeder. The farebox recovery ratio for the suburban rail connectors is usually slightly lower than for the remaining services, but the actual fare revenues (per mile or per boarding) are higher in most cases. These results are consistent with the previous findings on the advantages of decentralized, multi-destination networks, and indicate that those routes are actually performing relatively well in the terms of direct operating costs, despite serving low- density, suburban areas.
The findings of the route-level analysis for particular systems are l corresponding to the results of the regression analysis, and the system-level benefit-cost analysis, in general. Systems generating higher transit benefits consume lower operating expenses, regardless of route category and service designation. Portland and San Diego are the only two cities where the operating costs are lower for the Non-CBD routes, both on per mile and per boarding basis; the same applies to the farebox recovery. These lower-than-average operating expenses are related to the results of the benefit-cost analysis: lower costs increase the amount of the final net benefit value, and simultaneously, higher-than-average ridership allows distribution of the fixed costs over a larger number of service and patronage, reducing the average operating costs.
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CHAPTER FOUR
ROLE OF TRANSIT OWNERSHIP AND MANAGEMENT IN INFLUENCING BENEFITS AND COSTS OF MULTIMODAL TRANSIT
This stage of the study evaluates the influence of another category of internal planning factors on transit economic outcomes; specifically, it focuses on the role of particular transit ownership forms and organizational decisions in reducing transit deficits and increasing benefits of transit investments. The analysis places special attention on the non-traditional, market- oriented ownership strategies, including service contracting and outsourcing. It also evaluates the economic outcomes of different approaches to regional transit governance. That portion of the study was motivated by increasing interest in successful strategies for reducing transit costs as well as the emerging shift in policy, which comprises expanding the focus of framework to a broader, regional scale. Additionally, the author intended to at least partially address several gaps in the literature on transit management, discussed later in this chapter.
The assessment of transit privatization and regional governance begins with a panel
regression model, which resembles the analysis conducted in the previous stage of the study. The
variables reflecting the level of service privatization and the level of regional integration are
incorporated into the model, and their relationship with the economic outcomes is examined.
Next, the author performs a brief policy analysis, focusing on the organizational structures and
ownership forms adopted within the case transit systems. Such an approach allows to address the
study’s second major research question, as well as identifies the conditions required to
implement and maintain a successful market-oriented transit ownership and governance
mechanism.
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4.1 Literature Review
4.1.1 Forms of Transit Organization and Ownership in the United States
Transit services emerged in the U.S. cities as privately operated enterprises. During the first few decades of operation, the transit companies were able to generate significant net profits without receiving any public assistance. Later, however, the profits began to rapidly decrease, due to a combination of several external and internal factors, including the growth of mass individual motorization, new labor regulations, increasing costs of infrastructure maintenance, and regulations forcing the companies to keep their fares fixed regardless of inflation. Declining profitability eventually led to substantial service reductions, replacement of electric modes with diesel buses, and in some cases transit system closures (Jones, 2008). In the 1920s, the first few cities publicized their transit companies, recognizing the non-direct benefits of transit and committing to providing transit subsidies that covered the difference between direct revenues and costs. However, these were rather exceptional cases, and in a majority of metropolitan areas transit continued to lose its importance. The nationwide transformation of transit companies from private to public ownership took place only in the 1960s, along with a gradual shift in the general perception of the transit’s importance for the society, and the provision of new funding opportunities by federal and state government. By 1970, local or state subsidiaries virtually owned all mass transit services in the U.S.
While public ownership of transit services allowed to sustain at least a limited amount of transit service in the era of the automobile supremacy, and to facilitate large-scale transit investments based on federal and state grants in the following decades, it had no substantial effects on improving transit feasibility and cost-effectiveness. Broad availability of subsidies coming from all levels of government, and a positive climate for spending public funds on transit
93 investments and operations reduced the incentive for cost reductions or actions leading to improving the service efficiency (Bly and Oldfield, 1986). Intense bureaucratization and union agreements brought additional burdens on transit budgets (Cox, 1997). Public ownership also made transit vulnerable to problems, such as a lack of political consensus over service funding and issues with regional governance in larger metropolitan regions, which were usually partitioned into multiple municipalities or even multiple counties. Consequently, multiple transit agencies operate in some of the metropolitan areas. The separation, in many cases, creates additional hassles for the riders (lack of convenient transfer opportunities, and lack of a single fare system), is what ultimately results in lower overall ridership and reduced fare revenues. It also increases the transit inefficiency, as the planning and management functions are duplicated across the multiple agencies. Merging these functions is supposed to benefit the riders and to reduce the unit costs of transit agencies (Iseki et al, 2011; Meyer et al. 2005; Miller et al. 2005;
Pucher and Kurt, 1989). From the perspective of a regional planning theory, establishing unified metropolitan transit governance additionally makes it easier to address broader planning objectives in the context of the entire region, not only the core city.
Although there are many notorious cases of wasteful and inefficient public transit management, some cities decided to cope with the problems discussed in the previous paragraph.
Inspired by positive experiences of other developed countries, they have adopted alternative strategies of transit management, modifying organizational and ownership structure of their transit services. Specifically, these cities decided to implement one or more of the following strategies: 1) contracting out some or all of their transit services to private companies, 2) separating the managing and planning agency from the service provider, and 3) establishing unified regional transit systems.
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In the contracting model, the public entity asks for bids from private contractors for
operating a specified amount of the transit service. The biding process is supposed to motivate
the private companies to compete against each other by offering lower bids, and consequently, to
provide the service at lower costs than a traditional public agency. Service privatization in the
contracting model does not mean that transit becomes a self-sustaining business, as it was in the
pre-automobile era. Operating costs are still higher than the fare revenues, and the difference is
covered with public subsidies; however, the competition is supposed to reduce these costs and
therefore lower the amount of subsidies required to operate the service (Cox, 2012; Love & Cox,
1993).
There are several possible schemes of transit service contracting and outsourcing. These
schemes could be categorized by the roles played by the local or state authorities and the
contractors. Three typical ownership and organizational structures involving service privatization
could be identified in the U.S. setting (Iseki, 2011):
- The city (or other jurisdiction – county, state) operates its own public transit agency
which directly operates a portion of the service. Contractors operate the remaining
portion.
- The city operates its own transit agency, which retains planning, organizational and
supervisory functions, but the entire service is contracted out.
- The city delegates most planning and organizational functions of the transit agency to
a private contractor, retaining control only over strategic decisions (e.g. construction
of a new rapid transit line).
As already mentioned, transit service privatization proved to be successful in several parts of the developed world, including the United Kingdom, Australasia, the Netherlands, and
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Scandinavia (Currie, 2012; Wallis, 2003), although not necessarily all forms and mechanisms of
transit privatization turned out to be successful. In some cases from overseas, implementing
market-oriented transit management concepts turned out to be a failure. In the UK, transit
services outside of London were completely privatized and deregulated in the 1980s, and private
companies took over full responsibility for planning, routing, and operating transit services.
Subsequently, less popular routes were gradually eliminated, while private carriers were focusing
on “sweeping the crème” by serving the most popular corridors, which has eventually led to
destructive competition (Currie, 2012). In Australia, some of the contracting agreements have
failed due to the irrational policies adopted by the city authorities, or inadequate allocation of
planning and supervisory functions between the public authorities and the private sector (Currie,
2009). Although the British model of full deregulation is not present in any of the American
cities, both of these cases indicate that not all types of transit service contracts are beneficial for
the public entity and for the riders, and that inappropriate management and weak control over
contractors might have negative consequences for transit operations. Another possible pitfall of
contracting, discussed by Cervero (1988), is delegating the most frequent services (with high
service volumes, and lower unit operating costs) to contractors, and leaving the less frequent and
more costly routes within the publicly owned agency, as the contractors are not interested in
running the services that are more complicated and less efficient to operate.
In the United States, the privatization strategy has been adopted both by some of the large, multimodal transit systems, as well as smaller agencies, although the overall, nationwide share of contracted fixed-route services is relatively low if compared with other developed countries and the effects of transit privatization in the US have not been fully investigated yet.
While multiple studies evaluated some aspects of contracting, there are still some gaps and
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unexplored themes in the literature, which require further research. A majority of the studies
evaluated the role of contracting in reducing the direct operating costs. Typically, these studies
indicated that contracting by reducing the average operating costs (Downs, 1988; Ernst and
Young, 1993; Iseki, 2004; Peskin, Mundle, and Varma, 1993; Teal, 1985; Teal and Nemer, 1986;
Webster, 1988;); however, there were also some contrary findings (McCullough et al, 1998;
Sclar, 2000; Teal, 1991). Some scholars decided to approach the issue from a broader
perspective, and examined the influence of contracting on other characteristics of transit services,
such as safety or labor provisions (Nicosia, 2002; Frick et al, 2006). However, there has been
relatively little qualitative research and policy analysis performed to determine how particular
cities adopted the privatization model, and how they had distributed the managerial, planning,
and organizational roles among the public and private parties. The economic evaluation of transit
privatization has been limited to direct operating costs, a measure which somehow reflects the
transit service efficiency, but does not represent the full spectrum of transit benefits and costs, as already discussed in Chapter 2.
Regional coordination and cooperation appears to serve as another internal planning strategy that might positively influence transit ridership, performance, and possibly, economic outcomes. As already indicated in the study’s case discussion (Chapter 1), the 13 case metropolitan areas have adopted different forms of regional transit governance. In some cities, a single, unified agency operates all transit services. In other cities, the core agency operates rail lines and buses serving the inner parts of the metro areas, while outlying towns or counties maintain their own, separate transit companies. The model of regional transit association based on the German verkehrsverbund concept is a hybrid between those two approaches: single regional authority, subsidized by all area’s municipalities, coordinates planning and managerial
97
decisions over the entire regional transit network, while particular services are operated by
several independent entities (Larwin, 2012; Pucher and Kurth, 1989). Under the
“verkehrsverbund” model, the authority maintains a single, unified fare system, collects the fare revenues, and receives subsidies from the typical public sources of transit funding.
Simultaneously, it allocates these funds across particular operators to compensate their costs.
The available literature suggests that improving regional coordination, sharing
managerial and operating functions, and creating seamless, unified regional networks has
positive effects, both for the public agencies operating transit services, as well as for the riders
(Cook, Lawrie, and Henry, 2003; Meyer et al, 2005; Miller, Englisher, and Kaplan, 2005).
Economies of scale should allow reductions of maintenance and operating costs, as some
duplicative functions of multiple operating agencies are performed by a single entity. Patrons
should expect increased accessibility and connectivity across the entire metropolitan area, as the
unified regional networks tend to provide better suburban service, contrary to the systems
partitioned into multiple agencies, which are usually focused primarily on serving the core city.
However, the research providing support for these hypothetical deliberations is limited. The
literature on regional governance usually discusses the advantages of regional transit
coordination in theoretical context and presents snapshots of the existing governance schemes,
providing comprehensive information on the stakeholders participating in the managerial
structure. However, it does not provide much empirical evidence based on a quantitative
evaluation (incl. economic assessment) of specific regional governance strategies adopted by
U.S. metropolitan areas. These studies recognize the importance of coordination, and its possible
impacts on cost reduction, but these expected outcomes are not supported with any strong
evidence, such as a multiple-case evaluations of costs and other outcomes (Iseki, 2011).
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In sum, scholars have not explored deeply at least some aspects of transit service contracting and regional governance. The influence of contracting has been assessed primarily by measuring the direct operating costs, and the evaluations of regional transit management coordination were limited to qualitative, descriptive analyses. This study intends to recognize managerial and organizational decisions as another possible internal influence on transit performance and economic feasibility, and contribute to the broader discussion on the advantages of privatization and regional coordination, by providing new evidence and insights on the effectiveness of these strategies.
4.2 Research Methodology and Model Specification
The research methodology utilized in this stage of analysis follows the approach adopted previously for evaluating the role of internal service decisions. An analogical panel regression model was estimated to determine the relationship between the level of contracting, organizational structure, and the amount of net benefits. Additionally, this stage includes a brief policy analysis focused on the forms of non-traditional transit ownership and management structures, their emergence and current functionality. The policy analysis aims to broaden the understanding of successful multimodal transit management strategies and to identify the best practices for improving transit feasibility by modifying organizational and ownership forms.
The panel regression model was specified in the same way as the model discussed in
Chapter 3. The explanatory variables reflecting the phenomena investigated in this stage of study were included in the model along with the variables added in the previous stage. The model has been estimated using OLS standard errors, as well as the cluster-robust errors. The author conducted two estimations of the model for each of the error types: first with the internal
99 variables used in the previous model, and a second one without those variables. Such an approach gave some idea of the interdependences between the two groups of internal explanatory variables.
The following variables illustrating strategic decisions with regards to service contracting and managing structures were added to the model:
- contract (reflecting percentage of fixed-route bus service volume that is contracted
out to third-party companies; the service volume is measured in revenue miles). Table
4.1 presents the observations for contract variable. Observations were taken from
FTIS (2014).
- regional - variable that represents the number of independent entities facilitating
planning and managerial functions over transit service in a specific metropolitan
region. An independent agency with planning functions is not homogenous to a
transit agency or other company providing transit services. A single managing entity
could manage and plan a network operated by multiple agencies, as explained earlier
in the text. Observations for the regional variable are presented in Table 4.2.
Considering the majority of previous studies discussed by the literature review, which
had determined positive influence of contracting on transit economic outcomes, the author
hypothesized that contract will be positively correlated with the net benefits (ben_per_pm).
While the role of regional transit authorities on economic outcomes of transit operations has not been clearly determined, the consolidation of transit planning and managerial functions is expected to reduce the operating and capital costs as noticed earlier in this chapter. Therefore, the regional variable is assumed to be negatively correlated with ben_per_pm, as a lower number of independent entities reflects a higher degree of regional consolidation.
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Table 4.1 Share of Bus Service Volume (Revenue Miles) Operated by Private Contractors
Buffalo Charlotte Dallas Denver Houston Minneapolis Phoenix 2001 0.00% 0.06% 30.49% 2002 0.00% 0.04% 36.04% 2003 0.00% 28.47% 37.15% 2004 0.00% 0.41% 38.71% 17.88% 21.41% 2005 0.00% 0.06% 45.29% 20.23% 20.30% 2006 0.00% 2.69% 47.26% 21.42% 22.08% 2007 0.00% 1.18% 0.08% 43.92% 22.56% 22.80% 2008 0.00% 0.00% 0.09% 44.89% 22.71% 24.12% 2009 0.00% 0.00% 0.22% 45.14% 22.04% 23.94% 99.71% 2010 0.00% 0.00% 0.15% 45.31% 21.84% 23.96% 99.69% 2011 0.00% 4.88% 0.16% 45.57% 22.05% 24.50% 99.66%
Pittsburgh Portland Sacramento Salt Lake San Diego St. Louis 2001 6.60% 0.00% 28.44% 0.00% 18.21% 11.79% 2002 6.94% 0.00% 30.65% 0.00% 19.59% 12.92% 2003 7.91% 0.00% 30.68% 0.00% 28.65% 13.21% 2004 4.63% 0.00% 28.90% 0.00% 39.69% 14.04% 2005 6.09% 0.00% 30.49% 0.00% 40.58% 15.80% 2006 6.22% 0.00% 38.57% 0.00% 39.40% 15.87% 2007 6.50% 0.00% 40.59% 0.00% 38.76% 15.15% 2008 7.83% 0.00% 42.06% 0.00% 39.28% 15.08% 2009 8.10% 0.00% 43.96% 0.00% 40.18% 16.11% 2010 6.87% 0.00% 43.85% 0.00% 43.20% 16.98% 2011 8.79% 0.00% 46.98% 0.00% 63.81% 14.89% Source: FTIS (2014)
Table 4.2 Number of Independent Transit Organizational Entities within the Case Metropolitan Areas
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Buffalo 11111111111 Charlotte 1 1 2 2 3 Dallas 22333333334 Denver 11111111111 Houston 1 1 1 2 2 3 3 3 Minneapolis 1 1 1 1 1 1 1 1 Phoenix 1 1 1 Pittsburgh 44555556666 Portland 23333333333 Sacramento 55556777778 Salt Lake 11111111111 San Diego 11111111111 St. Louis 22222222222 Source: FTIS (2014) and Transit Agency Data (2014).
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Analogically as in the previous stage, the author has tested the entire set of variables for multicollinearity. The results are presented in Table 4.3. None of the pairwise correlations for the two additional variables added to the model in this stage indicates an issue with multicollinearity.
Table 4.3 Pairwise Correlation for Explanatory Variables (with Ownership and Management Variables)
dec headway revmpe~m pop_dens unempl veh_hh med_inc gas tti contract regional
Decentralization dec 1.00 Headway headway 0.35 1.00 Service Density revmpersqm 0.02 -0.29 1.00 Pop. Density pop_dens 0.18 -0.25 -0.04 1.00 Unemployment unempl 0.07 0.09 0.04 -0.05 1.00 Zero-veh. h-holds veh_hh -0.53 0.14 -0.03 -0.28 0.02 1.00 Median Income med_inc 0.41 0.10 0.33 0.21 0.13 -0.57 1.00 Gas Price gas 0.02 0.06 0.10 -0.03 0.31 0.05 0.49 1.00 Congestion index tti -0.27 -0.39 0.18 0.47 -0.37 -0.09 -0.18 -0.34 1.00 Contracting Ratio contract 0.48 0.50 0.10 0.06 0.20 -0.23 0.43 0.16 -0.11 1.00 # of regional auth. regional -0.16 0.21 -0.15 -0.03 0.19 0.11 -0.13 0.10 0.22 -0.02 1.00
4.3 Model Results
Table 4.4 presents the model results. The left panel includes results for the combined model including both network planning and contracting variables, while the right panel includes the contracting-only model results.
These results indicate that systems that contracted out some of their services, as well as consolidated the planning and managerial functions, are generally expected to generate higher net benefits than the remaining networks. These variables appear to be significant regardless of the adopted error type. In the iteration including all internal variables and cluster-robust errors, the t-score for org is slightly below the confidence level, however, analogically as in the case of the headway variable (discussed in Chapter 3), org is measured directly (without sampling), and therefore, the higher level of significance and lower errors (estimated in the OLS iteration) are acceptable for that variable.
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Table 4.4 Model Results (with Ownership and Management Variables)
With OLS standard errors Dependent Variable: ben_per_pm (net benefits per passenger mile)
with network planning variables without network planning variables Variable Coeff St error t P>[t] Coeff St error t P>[t]
Ratio of contracting 0.740 0.25 2.96 0.004 0.585 0.26 2.28 0.025 Indep. organizations -0.050 0.02 -2.10 0.039 -0.082 0.02 -3.40 0.001
Decentralization Ratio 0.100 0.31 0.33 0.744 Average headway -0.010 0.00 -2.19 0.031 Service Density 0.001 0.00 3.15 0.002
Population Density 0.263 0.22 1.21 0.230 0.313 0.23 1.37 0.174 Unemployment Ratio 0.005 0.02 0.32 0.746 0.022 0.02 1.35 0.179 Zero-vehicle Households -0.011 0.02 -0.43 0.667 0.018 0.02 0.75 0.457 Median Income 0.003 0.01 0.38 0.705 -0.005 0.01 -0.58 0.565 Average Gas Price 0.121 0.17 0.72 0.472 0.034 0.17 0.20 0.844 Travel Time Index (TTI) -1.658 0.60 -2.74 0.007 -1.437 0.59 -2.42 0.017
Dummy time variables: 2002 -0.042 0.08 -0.53 0.595 -0.074 0.08 -0.89 0.377 2003 -0.147 0.06 -2.61 0.011 -0.121 0.06 -2.04 0.044 2004 -0.220 0.06 -3.94 0.000 -0.169 0.06 -2.94 0.004 2005 -0.238 0.08 -2.99 0.004 -0.154 0.08 -1.94 0.055 2006 -0.282 0.15 -1.86 0.066 -0.117 0.15 -0.78 0.439 2007 -0.305 0.16 -1.94 0.056 -0.112 0.15 -0.72 0.471 2008 -0.503 0.28 -1.79 0.078 -0.218 0.28 -0.77 0.445 2009 -0.394 0.13 -3.05 0.003 -0.300 0.13 -2.27 0.026 2010 -0.526 0.24 -2.22 0.029 -0.393 0.24 -1.62 0.108 2011 -0.564 0.29 -1.95 0.055 -0.378 0.29 -1.29 0.202
Constant 1.151 0.92 1.25 0.213 1.147 0.80 1.43 0.155
Number of observations: 123 Number of observations: 123 R-square: within = 0.64 R-square: within = 0.59 between = 0.16 between = 0.03 overall = 0.25 overall = 0.07
Correlation: Correlation: F(18,92) = 7.58 F(18,92) = 7.3 Prob > F = 0.0 Prob > F = 0.0
sigma_u = 0.35 sigma_u = 0.38 sigma_e = 0.10 sigma_e = 0.10 rho = 0.93 rho = 0.93
F test that all u_i=0: F test that all u_i=0: F(12, 92) = 23.78 F(12, 92) = 26.35
Prob > F = 0.0 Prob > F = 0.0
Continued on Next Page
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Table 4.4 – Continued
With Cluster-Robust Standard Errors (C-R S.E.) Dependent Variable: ben_per_pm (net benefits per passenger mile)
with network planning variables without network planning variables Variable Coeff C-R t P>[t] Coeff C-R t P>[t] S.E. S.E. Ratio of contracting 0.740 0.36 2.07 0.060 0.585 0.20 2.88 0.014 Indep. organizations -0.050 0.03 -1.87 0.085 -0.082 0.02 -4.21 0.001
Decentralization Ratio 0.100 0.57 0.18 0.864 Average headway -0.010 0.01 -1.31 0.215 Service Density 0.001 0.00 1.80 0.096
Population Density 0.263 0.34 0.78 0.451 0.313 0.36 0.87 0.400 Unemployment Ratio 0.005 0.02 0.21 0.834 0.022 0.03 0.88 0.395 Zero-vehicle Households -0.011 0.02 -0.58 0.572 0.018 0.02 1.11 0.291 Median Income 0.003 0.01 0.34 0.737 -0.005 0.01 -0.79 0.447 Average Gas Price 0.121 0.14 0.88 0.398 0.034 0.15 0.22 0.830 Travel Time Index (TTI) -1.658 1.11 -1.50 0.159 -1.437 1.26 -1.14 0.277
Dummy time variables: 2002 -0.042 0.08 -0.53 0.595 -0.074 0.09 -0.82 0.430 2003 -0.147 0.06 -2.61 0.011 -0.121 0.07 -1.66 0.123 2004 -0.220 0.06 -3.94 0.000 -0.169 0.05 -3.56 0.004 2005 -0.238 0.08 -2.99 0.004 -0.154 0.04 -3.46 0.005 2006 -0.282 0.15 -1.86 0.066 -0.117 0.09 -1.30 0.220 2007 -0.305 0.16 -1.94 0.056 -0.112 0.11 -1.02 0.329 2008 -0.503 0.28 -1.79 0.078 -0.218 0.24 -0.90 0.386 2009 -0.394 0.13 -3.05 0.003 -0.300 0.07 -4.02 0.002 2010 -0.526 0.24 -2.22 0.029 -0.393 0.18 -2.16 0.052 2011 -0.564 0.29 -1.95 0.055 -0.378 0.24 -1.60 0.135
Constant 1.151 0.92 1.25 0.213 1.147 1.31 0.88 0.398
Among the four systems generating the highest transit benefits, Denver, Minneapolis, and
San Diego have contracted a significant portion of their service, and established strong coordinances over the entire metropolitan transit system. Similarly as in the previous stage of analysis, fourth of the most feasible systems. Portland emerged as a system diverging in some ways from the other three cases. During the period of analysis, contracting was not present at all in the Portland region, and there was more than one entity responsible for service planning and
104 management. Still, the two major agencies serving the Portland region have, at least partially integrated their fare systems and employed schedule and routing coordination, which means that the key objectives of regional coordination relevant for the patrons have been met.
The remaining systems, generating lower amounts of net benefits, have adopted various approaches to contracting and regional governance. Phoenix is the only other system which intensively employs both strategies, contracting out virtually all of its service. It is possible that other factors, which could be investigated by further research, wash out the benefits of contracting and regional coordination on Phoenix; also, specific arrangements made between the public agency and contract recipents might be responsible for other-than-expected results. The majority of the remaining systems do not contract more than 20% of service (aside from
Houston, none of the services are contracted out by the core agency). None of them have created a regional coordinating authority, and most of them actually feature partitioned governance over metropolitan transit, including, in particular, Pittsburgh and Sacramento.
Overall, these results allow the ability to address both major research questions of this study, indicating that both frequent and expansive transit networks, as well as the presence of privatization and regional coordination of transit operations, are positively influencing the amount of economic outcomes (measured as net social benefits) generated by a transit system.
Analogically as in the previous stages of study, the author acknowledges some limitations of the adopted methodology, related mainly to the cross-sectional study design and simplifications made with regards to adopted measures. Further research should investigate which specific organizational decisions have the highest impact on the economic outcomes of multimodal transit operations, and what excactly makes the contracted service less expensive than a service operated directly by the public entity.
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4.4 Policy Analysis
The author decided to perform a brief qualitative analysis of the transit organizational strategies adopted in each of the case cities, aiming to expand the understanding of the role of contracting and regional coordination in determining transit economic outcomes. As noticed earlier in the text, the 13 multimodal transit systems analyzed by this study differ substantially in the terms of adopted ownership forms and governance structures. The goal of this analysis is to determine factors influenced by the planners and policy-makers to choose specific organizational strategies for their transit systems. The literature review has revealed that the up-to-date research did not address that research issue comprehensively. This analysis intends to at least partially fill that gap and bring directions for further policy assessments, as well as provide supplemental insights on the study’s major research theme, comprising the search for best practices in improving economic feasibility with the utilization of internal planning decisions.
4.4.1 Transit Ownership and Management Policies in the 13 Case Metropolitan Areas
The policy analysis comprises a case-by-case overview of the emergence and current functionality of the ownership structures and regional cooperation mechanisms. Basic information regarding ownership and regional governance in the case of cities was already presented in Chapter 1. This section expands that discussion by giving a brief historical overview of policy adoption and focusing on the role of particular stakeholders in shaping the current management and ownership structures. The analysis has utilized two major methods of obtaining the relevant information. They are as follows 1) reviewing documents, reports, and articles discussing local patterns of transit ownership and regional cooperation, and 2) exchanging information through e-mail correspondence and phone interviews with local informants, specifically, transit agency or metropolitan planning organization employees, who agreed to
106 discuss the ownership structure and approaches towards service privatization (the identity of the informants is not revealed).
Buffalo: The transit system serving Buffalo metropolitan area is operated solely by one
agency (Niagara Frontier Transportation Authority – NFTA), which was established in 1967,
along with the acquisition of the former private transit companies by the local authorities.
Creation of NFTA was facilitated by the State of New York, which aimed to enhance the transit
systems serving the major upstate urbanized areas. The state retains control over the authority.
The agency covers the urbanized parts of the Erie and Niagara counties (Bregger, 2008; Eire and
Niagara, 2006; GBNRTC, 2013; NFTA, 2014).
The NFTA has never contracted out any of its fixed-route services. The agreement with
the local labor union explicitly prohibits the agency from outsourcing its fixed routes to a third
party contractor (ATU Local 1342, 2014; Informant #1).
Charlotte: A vast majority of transit operations in the Charlotte metro area are facilitated
by the core agency, Charlotte Area Transit System (CATS). CATS was established in 2000,
following the efforts of Mecklenburg County (the area’s core county) to expand transit to its
rapidly growing suburbs. CATS predecessor, Charlotte Transit System, was owned by the city of
Charlotte, and provided transit services only within the city limits. The new agency aimed to
stimulate regional cooperation and expansion of transit services. While the outlying areas of the
Mecklenburg County experienced a sizeable increase in service supply throughout the study’s
period of analysis, further expansion to surrounding counties has been limited to a few express
routes connecting to Charlotte CBD. These routes are equally funded by CATS and the outlying
municipality. Two of the outlying counties, Cabarrus and Catawba, have established their own,
local transit systems during the period of this study.
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The analysis indicates that foundations for regional transit integration have been created in the area’s transit management structure. The outlying counties are represented in CATS governance body (although they have no voting power), and transit improvement plans include adding more service for serving those counties. However, it seems that funding remains the major obstacle for further expansion. CATS limited its interest in running transit services outside of Mecklenburg County to providing CBD-bound commuter routes in the designated five corridors. No provisions have been made so far for adding more regional service (Booz Allen
Hamilton, 2011; CATS, 2012; CRTPO, 2014; Maryland DOT, 2014; NC DOT, 2002).
As reported in Table 4.1, the amount of contracted transit service in Charlotte is insignificant. To comply with state and federal regulations regarding labor unions, CATS utilizes the contracting scheme for its human resources. A third-party company, contracted by CATS, hires all employees. The state law prohibits city agencies from collectively bargaining labor contracts, while the federal law mandates bargaining for agencies receiving federal funding. The agency retains full control over the system operations, management, maintenance and planning
(CATS, 2012). The labor outsourcing appears to be a mechanism facilitated primarily to satisfy both contradictory laws, but has no analogies with the typical third-party service contracting performed in other cities.
Dallas: The Dallas–Fort Worth Metroplex emerged as one of the most complex cases of ownership and regional coordination among the analyzed systems. There is no single entity maintaining the region’s transit network, and the network is spatially partitioned into three separate systems (Dallas, Fort Worth, and Denton County), with only a few connections between the Dallas system and two other networks. Large urbanized portions of the Metroplex were not covered by any fixed-route transit service during the period of analysis, including significant
108 clusters of population and employment, such as the city of Arlington with approximately
380,000 residents and over 100,000 jobs.
As noticed in Chapter 1, the three core agencies maintain a partially unified fare system, however, they have not made any further efforts towards integration and consolidation of the bus and light rail services, as well as removing the gaps between their service areas. The importance of regional transit coordination and expansion of transit services to the currently unserved areas is echoed in long-term transportation plans (NTCOG, 2011), as well as in other future strategies
(e.g. the agenda for creating a regional commuter rail system); however, so far the transit governance remains to be partitioned into three large agencies and several other, smaller operators. The lack of full integration and coordination of transit services within the Metroplex might be explained by the fragmented governance structure within all of the three major agencies. Each of them was established as a conglomerate of the core city, and adjacent jurisdictions, which have decided whether they want to join the agency (in some cases that decision was made by a popular vote). The agency members are required to contribute to the agency with a specific amount of funding generated in the form of a sales tax, which appeared to be one of the main reasons some of the smaller municipalities were prevented from joining the transit systems, as the Texas law establishes a cap on the maximum amount of the sales tax.
Because of the funding limitations or for other reasons, some of the area’s municipalities refused to join a transit agency. Others dropped out from the agencies after a few years of membership, which has led to the preservation of the partitioned and incomplete transit coverage in the
Metroplex area (DART, 2006; Dallas News, 2013; DCTA, 2014; FWTA, 2013; NTCOG, 2004,
2014)
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Table 4.1 reported insignificant shares of contracted service in the Dallas area (below one
percent for most of the analysis years). Contracting seems to be limited to certain types of
service, such as hospital or university shuttles, which are presented to the public as a part of the
DART network. Similarly to Charlotte, the Fort Worth Transportation Authority staff is hired
through a third-party company having a contract with the agency for providing the labor force.
The service, however, is operated and managed by the authority itself (FWTA, 2013).
Denver: The transit system in Denver metropolitan area is maintained by a single agency,
Regional Transportation District (RTD), which performs planning and supervisory functions as
well as operates some of the service. The agency’s service area covers the entire Denver
metropolitan region and includes eight counties. RTD was founded in 1969 as an agency of the
State of Colorado. The state has designated the agency’s boundaries; other jurisdictions may join
the district upon request. The state has also enacted a transit-dedicated sales tax in the district
area, which is one of the primary funding sources for RTD. (Maryland DOT, 2011; RTD, 2012,
2014)
Denver has one of the highest contracting ratios among the analyzed cases. Since 1989,
RTD was required by the state to contract out a specified percent of its bus services to private companies. Initially, the minimum contracting requirement was set at 20% of the fixed-route service In 1999 it was increased to 35%, and in 2003 it was changed to 50% of all bus operations
(including demand-response service). Finally, in 2008 the legislation was modified again, lifting the requirement, and simultaneously authorizing the district to contract up to 58% of all services.
Even though RTD is no longer required to facilitate contracts, it continues to do so, as indicated in Table 4.1 (Marsella, 2012; RTD, 2012, 2014; Savas and McMahon, 2002). A brief overview of the agency’s route-level statistical data did not indicate any pattern of “cream sweeping,”
110 which is discussed earlier in literature review The average operating costs, service volume, and other service characteristics on routes operated by particular contractors do not seem to be substantially different from the routes served directly by RTD (Transit Agency Data, 2014).
Houston: Similar to Charlotte, a vast majority of transit services in the Houston metropolitan area is allocated to the area’s core county (Harris), and is operated by a single agency, the Metropolitan Transit Authority of Harris County (Metro). A few other local agencies operate a limited volume of service in the outskirts of Harris County and in some of the surrounding counties. These agencies run both local routes and inter-county routes connecting to the Metro system. The existing governance scheme emerged in the 1970s, when Metro was created. The agency was initially assumed to serve as a regional authority, however, some of the
Harris County municipalities, and all surrounding counties, were uninterested in joining Metro, eventually establishing their own agencies, or restraining from providing any fixed-route transit services. Analogically, in Dallas, the required one-percent sales tax contribution constrained
those jurisdictions from becoming a part of the Metro network. The dominant (although
geographically limited) role of Metro as the core agency follows the very strong position of the
city of Houston in the local governance system. The importance of increasing regional transit
coverage and creating a seamless, unified transit system is being discussed by the strategic plans.
Some patterns of cooperation, including fare honoring policies, have been established, although
no actions were taken as far as a full integration of all transit systems serving the area. (H-GAC,
2011a, 2011b; METRO, 2014; Murray, undated; Lewis, undated; TAMU, 2006, 2013)
Metro has contracted out a small portion of its services since the early 1990s, and
delegated a significant amount of its services to a private company in 1997. Since then,
111 approximately 20% of the service volume has been contracted out (Savas and McMahon, 2002;
FTIS, 2014).
Minneapolis: A single, regional authority, The Metropolitan Council, was created by the
State of Minnesota in 1967 to coordinate the transit service in Minneapolis-St. Paul metropolitan area. A majority of the area’s transit services are operated by Metropolitan Transit (Metro
Transit), which, technically, is a branch of the Metropolitan Council. Metro Transit operations are focused primarily on the area’s core urbanized area, which includes the two major cities,
Minneapolis and St. Paul. In 1981, the state responded to the concerns of the outlying jurisdictions, which claimed that the core agency unfairly allocated the service outside the urbanized core. These jurisdictions were allowed to opt-out from the Metro network and create their own transit agencies. Currently, there are several local transit agencies operating in areas surrounding the urban core, which includes local routes within the specific municipalities, as well as express routes running to downtown Minneapolis and St. Paul. The Metropolitan Council continues to coordinate the entire regional network, and deliver it to the public as a seamless transit system with unified route structures, schedules, and fares. The Council retains a majority of the planning and managerial functions over the entire system. However, some of these functions were transferred to the local agencies in 2008, by creating the Counties Transit
Improvements Board (CTIB), which participates in the regional transit planning process. Apart from establishing the governance structure, the state has created provisions for transit funding in the area, imposing a tax on motor vehicle sales, which is one of the main sources of revenue for the area’s transit operations. The state has also authorized local jurisdictions to enact sales taxes dedicated for transit funding (Metropolitan Council, 2010; Planning to Succeed, 2011; State of
Minnesota OLA, 2011).
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Contracted services in Minneapolis accounted for approximately 20% of the total service volume during the period of analysis. There are two schemes of transit contracting present in the
Minneapolis transit system. The Metropolitan Council contracts out some of the routes through one of its subdivisions, Metropolitan Transportation Services (MTS) instead of assigning them to
Metro Transit. Interestingly, these routes are usually the services with lowest ridership and service volume, which Metro finds inefficient to operate. It appears to be a situation exactly opposite to the “cream sweeping” discussed earlier in this chapter. MTS serves as a coordinator of the contracting process, and private companies operate those routes.. Besides, most of the local, suburban agencies contract all of their services to the private sector.
Phoenix: The ownership and organizational structure of the transit system serving
Phoenix metro area combines the unified regional governance strategy with the contracting model. The entire transit network serving the Phoenix area is coordinated by a single entity, doing business as Valley Metro. Operations are divided among several divisions, and the service allocated to each of those divisions is contracted out to private companies. The structure generally follows the German verkehrsverbund model discussed in the previous section.
The current governance structure has its roots in the mid-1980s, when state legislation established a new sales tax designated for funding transportation projects. The city of Phoenix had created the Regional Public Transportation Authority (RPTA) to facilitate transit improvements and investments funded by the new resources. The city-owned Phoenix Transit
System (PTS) operated transit services, at that time. In the following years, other cities began to introduce the new transit tax, and to expand or establish their own transit networks. In most cases, those cities contracted the operations with the PTS; however, they desired to avoid using the PTS brand for the routes running outside of the Phoenix city limits. To address that issue,
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RPTA has facilitated the creation of Valley Metro, which initially served just as a brand for the area’s transit services, but soon after emerged as the “umbrella” organization for unified regional transit network. (MAG, 2003, 2014; TAMU, 2009; Valley Metro, 2014)
Phoenix has the highest amount of contracted services among the analyzed cases. As already noticed, virtually all bus services operating within the Valley Metro system are contracted out. Phoenix is also the only system in the case set where a private contractor also operates light rail services.
Pittsburgh: Among the analyzed cases, Pittsburgh appears to be one of the two most
partitioned transit systems (along with Sacramento) in the means of ownership and management.
The core agency, Port Authority (PAT), operates only within the core Allegheny County, while
the remaining, outlying counties maintain their own, suburban transit agencies.
PAT was created in 1956, and shortly after, began to acquire private transit companies
operating in Pittsburgh and Allegheny County. In the following decades, PAT focused on
developing modern regional rapid transit system, initially planning to build a “Skybus” network
and eventually created dedicated bus rapid transit guideways and the modern light rail system.
While PAT expanded its services within the Allegheny County, it never attempted to move
further and add the surrounding counties to its network. Each of those counties continues to
operate their own services independently from PAT. The lack of regional coordination is
reflected in the area’s network structure. Most of the suburban agencies operate local routes
within their jurisdictions, as well as express routes connecting to downtown Pittsburgh. Those
express routes duplicate the routes within the PAT network (including the rail lines), which is
oriented primarily on providing access to the CBD, as indicated earlier in the text.
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The literature review has revealed that issues with the transit network fragmentation in the Pittsburgh region have been recognized by the decision-makers, and that consolidation of the regional system has been discussed throughout the recent decades among the regions’ agencies, as well as at the state level. The proponents of regional coordination frequently referred to the
Southeastern Pennsylvania Transportation Authority (SEPTA), serving the state’s largest metropolitan area, Philadelphia, as a model of regional transit governance that could be adopted in Pittsburgh. The Pennsylvania Department of Transportation has recently incentivized the agencies (by funding preliminary studies) to explore the opportunities of creating a new, regional authority, or at least some partial consolidation across the neighboring suburban agencies. As suggested by some of the local policy analysts, the dominant role of Port Authority seems to be the main obstacle for the pan-regional consolidation. PAT has been struggling with a huge budget deficit, partially caused by providing extraordinary benefits for employees, very strong role of labor unions within the agency, and inefficient management. Other area’s agencies are aware that consolidation with PAT would require them to participate in covering Port
Authority’s debts. Simultaneously, the PAT labor unions resist changes in the governance structure as well, expressing concerns that such transformation would reduce their current benefits and privileges.
Contracted services in Pittsburgh account for less than 10% of the total metropolitan
service volume. During the period of analysis, two of the smaller agencies (City of Washington,
and Mid Mon Valley Transit Authority) contracted all of their services, and three other agencies
(Beaver County Transit Authority, Fayette Area Coordinated Transportation, and Westmoreland
County Transit Authority) contracted a portion of their operations. The core agency, Port
Authority, was not contracting any of its service. As indicated by the literature, PAT has
115 considered the possibility of service privatization, but those plans were scrapped due to strong opposition of the unions (Allegheny Institute, 2011; Commonwealth of Pennsylvania, 2014;
FTA, 2014; Maryland DOT, 2011; Pittsburgh City Paper, 2011; Pittsburgh Magazine, 2002;
SPC, 2006, 2011, 2014a, 2014b; TribLive, 2013)
Portland: Tri-County Metropolitan Transportation District of Oregon (TriMet) provides
the majority of the transit services in Portland’s metropolitan area. TriMet was established by the
State of Oregon in 1969, after merging several independent bus companies operating in the three
core counties, and transferring their ownership from private parties to the public authorities. The
legislation authorized the agency to enact transit-dedicated payroll taxes. In the mid-1980s, some
of the suburban communities began to contest the funding mechanism, claiming that they receive
a very small portion of service despite contributing to the TriMet budget via taxes. The state
allowed these jurisdictions to leave TriMet under the condition that they would create their own
transit systems. As a result, a few small suburban agencies were established. These agencies are
functioning separately from TriMet, although they orient their service on providing connections
to the outlying TriMet light rail and commuter rail stations, instead of running duplicative
service to Portland CBD. However, none of those agencies maintains any fare cross-honoring
policies with TriMet.
On the Washington side of the Portland metropolitan area, transit services are maintained
by Clark County Transit Authority, branded as C-Tran, which was established in 1981. The
agency operates a local route network within the county, as well as express routes running to
downtown Portland. TriMet and C-Tran established fare cross-honoring and provided convenient
transfer opportunities between the two systems, which will eventually include a TriMet light rail
line running into Clark County. However, the literature review did not indicate any attempts of
116 consolidation for the two agencies (C-TRAN, 2010; Metro Oregon, 2014; NC DOT, 2002;
TriMet, 2010).
Both TriMet and C-Tran operate all of their regular service directly. Similarly as in the case of Buffalo, agreements between the agencies and the local labor union prohibit contracting fixed-route services (ATU Local 757, 2014, Informant #2).
Sacramento: The ownership structure of the Sacramento transit network resembles the
Pittsburgh case. The service area of the central agency, Regional Transit District (RT), is limited
to the core of the urbanized area, specifically, the city of Sacramento and the northern part of the
Sacramento County. Several other agencies operate local network in the outlying jurisdictions
and express routes running to downtown Sacramento, or connecting to the light rail.
RT was created along with passing the Transportation Development Act by the State of
California in 1971, which provided funding for transit operations, including both direct subsidies
from the state budget, as well as local transit-dedicated sales taxes. RT commenced its operations
in 1973. Initially, RT provided services in areas outside of the core Sacramento County;
however, later the local jurisdictions established their own agencies and acquired those services,
shrinking the RT service area to the region’s core. Other local transit networks emerged in the
area, operating independently from RT since their creation.
While there is no single regional transit authority in the Sacramento region, some coordinating functions are being performed by the local metropolitan planning organization, the
Sacramento Council of Governments (SACOG). Still, a unified fare system does not exist in the region, and only selected agencies have reached agreements regarding ticket cross-honoring.
Despite coordination attempts made by the SACOG, the network structure reflects the governance fragmentation: the suburban agencies focus on providing access to the region’s core
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(which results in running duplicative service), while they do not operate almost any cross-county routes (SACOG, 2000, 2006, 2012, 2013, 2014; SacRT, 2009)
The core agency, RT, did not contract out any of its services during the period of analysis. The suburban agencies contracted all of their fixed-route operations, except for Placer
County Transit, which contracted less than 20% of its service.
Salt Lake City: The entire Wasatch Front metropolitan region (including Salt Lake,
Provo-Orem, and the Ogden-Clearfield metropolitan statistical areas) is being served by a single agency, Utah Transit Authority (UTA). UTA was created by the cities of Salt Lake, Murray, and
Sandy in 1970, shortly after the State of Utah passed legislation allowing local jurisdiction to create public transit authorities. The state also established funding provisions for the agency, based on sales tax revenues. UTA regional expansion began in 1973, when the agency took over transit operations in the northern part of the region (Weber and Davis counties). Counties located south of Salt Lake initially maintained a separate agency, but in 1985 they agreed to adopt the transit-dedicated taxation and merged their agency into UTA. Since then, UTA remains to be the only fixed-route operator in the region. The entire UTA service is operated directly by the agency. The literature review revealed that there was some discussion over the possibility of contracting out UTA services, initiated by one of the state representatives. However, the agency did not pursue that idea and continued to operate all fixed-route services on its own (UTA, 2014;
UtahRails, 2014).
San Diego: Governance and ownership structure of the transit system serving the San
Diego area resembles the Phoenix case discussed earlier in the text. The regional transit is maintained by a single “umbrella” organization, the Metropolitan Transit System (MTS), and is operated by several companies, including public agencies and private contractors.
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The San Diego regional transit governance mechanism has roots in the early 1970s. The
1971 Transportation Development Act, discussed earlier in the Sacramento section, stimulated the growth of the agency serving the region’s core (San Diego Transit Corporation), and allowed local jurisdictions (Chula Vista, National City, and Oceanside) to create their local transit systems. These systems were initially uncoordinated. In 1975, the State established MTS (then, the Metropolitan Transit Development Board, MTDB) as an institution responsible for coordinating the area’s transit systems, and developed the light rail system. The regional governance model was inspired by the verkehrsverbund concept (discussed in the previous section), which the local planners and decision-makers had acquainted exploring the successful
German experiences with developing modern transit systems. San Diego pioneered the adoption of the verkehrsverbund model in the U.S. setting, and continued maintaining that model throughout the present times.
Currently, MTS serves as the regional coordinator, and simultaneously operates the light rail and a portion of the bus service. The MTS bus subsidiary (San Diego Transit) directly operates some of the service, and contracts out the remaining part. The share of the San Diego
Transit contracted service has substantially increased during the period of analysis, from 24% to
51%. Two other regional agencies coordinated by MTS, Chula Vista Transit and National City
Transit, contracted their entire service. The North County Transit District operated its service directly until 2010, when the entire service was contracted out. (Booz Allen Hamilton, 2011;
Larwin, 2012; SANDAG, 2001; 2004, 2007, 2011; Thompson, 2003)
St. Louis: A majority of the transit network in the St. Louis metropolitan area is
maintained by the Bi-State Development Agency, which brands its transit business as Metro.
Established in 1949, BSDA was one of the first regional planning associations in the nation
119 aimed at facilitating collaborative planning across the entire metropolitan area, treating it as a whole, regardless of the multiple county and state boundaries. The federal government stimulated the creation of BSDA. The Bi-State Agency entered the transit business by taking over the area’s transit operations from the private companies in the 1960s.
Currently, Metro provides transit services within the urbanized core of the metro area, which includes St. Louis city, St. Louis County, and St. Clair County. One other county,
Madison, maintains its own transit agency, which connects to the Metro system. Both agencies maintain a ticket cross-honoring policy, although it is limited to certain types of fares. Other counties in the St. Louis metropolitan region are not served by any fixed-route transit system. In the terms of regional transit expansion, St. Louis appears to be a similar case as Dallas and
Houston. The outlying counties are unable to meet the Metro funding requirements, and therefore, they remain outside of the system. One of the counties, St. Charles, attempted to join the Metro system, although its residents rejected the proposed sales tax intended to fund the transit service. Opposite to some of the other similar metropolitan agencies, Metro does not possess the power to impose taxes or mandate all jurisdictions to provide transit funding. Long- term plans assume expanding Metro’s network to the currently un-served counties, though, those counties would have to participate in service funding (Booz Allen Hamilton, 2011; BSDA,
2014a, 2014b; Day and Stauder, 2003; East-West Gateway, 2011).
Metro contracted out a small portion of its service in the early 1990s, but has not contracted out any service since then, including the period of analysis. Analogically as in the case of several other systems, fixed-route service contracting is prohibited by an agreement with the labor union.
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4.5 Conclusions
This stage of the study has investigated how the presence of service contracting and regional governance influence the amount of net benefits generated by a particular transit system.
It has also analyzed how particular models of governance emerged in the case metropolitan areas, and what has motivated the local transit managers to contract transit services (or what restrained them from contracting).
The statistical analysis has indicated that higher amounts of service contracting yield higher net benefits. This suggests that contracting contributes to reducing transit deficits, which corresponds to most of the previous findings on the contracting advantages, provided by the referred literature. The analysis has also identified a larger number of independent entities involved in planning and managing the transit systems as a negative influence on the economic outcomes. These findings are also reasonable and consistent with the theoretical assumptions provided by literature. Larger number of independent agencies results in duplicating the supervisory and organizational functions, as well as the services itself. Consolidation allows taking advantage of the economies of scale, and reducing service duplication, which results in improved region-wide transit service. It also fits better within the current demographic and economic patterns of America’s large metropolitan regions, which experience continuous decentralization and emergence of important centers of economic activity in the outlying areas.
Consequently, the comprehensive regional network responds better to the travel demand, which yields to higher benefits for the society and lower operating deficit for the transit agencies.
The policy analysis has revealed several types of multimodal transit system governance, as well as various approaches to transit service privatization, which were synthesized in Table
4.5.
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Table 4.5 Summary of Policy Analysis on Governance and Ownership Structure
Governance Emergence of the Governance Financial aspects: Structure Pattern and its Current Funding sources, Functionality Presence of contracting Unified management, All three governance structures All three systems are funded by Multiple agencies were created by the state. dedicated sales or payroll taxes, enacted by the state Minneapolis In Minneapolis and San Diego, Phoenix outlying jurisdictions were Phoenix contracts out its entire San Diego authorized to create their own service, San Diego approx. agencies, but were 50%. In Minneapolis, the core simultaneously required to agency contracts out only the participate in the regional least-efficient service (~10%); transit system. outlying agencies contract out all or most of their service. Unified management, Again, state played a significant In Denver, the state required the Single agency role in facilitating the creation agency to contract out some of of the unified, regional its service until 2008. The Buffalo management. agency continues to contract a Denver significant portion of the Salt Lake City service.
In Buffalo and Salt Lake, no service is contracted. Collective bargaining agreement with powerful the labor unions restrains the agency from contracting.
Continued on Next Page
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Table 4.5 – Continued
Partitioned management Core agencies in Portland and Core agencies in Portland and Dominating core agency + Sacramento were initially Sacramento are partially funded at least one other agency created as regional agencies; by the state. facilitating significant however, at some point the portion of the area’s outlying jurisdictions opted out In all four regions, the core transit service and established their own agency does not contract out agencies. any service, while the majority Dallas of the remaining, smaller Pittsburgh Dallas and Pittsburgh have not agencies contract all or most of Portland established any regional their service. Sacramento governance structures so far; outlying communities are unwilling to join the core agency primarily for financial reasons.
Partitioned management, Regional agencies were created In all three cases, the outlying Dominating core agency + by the state (St. Louis) or the jurisdictions have not accepted few small agencies + lack core cities/counties (Charlotte, any funding provisions such as of any service in majority Houston), but the majority of sales tax. Therefore, they are of the surrounding the outlying communities not able to meet the funding counties/municipalities refused to join those agencies requirements of the major and established their own agency. Charlotte systems or did not provide any Houston fixed-route service. In Houston and St. Louis, core St. Louis agencies contract approx. 20% of service. No service is contracted in Charlotte.
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As illustrated by Table 4.5, some interesting patterns of adopting specific transit management philosophies are identified. The agencies’ approach to contracting varies substantially. For example, in one region, Denver, contracting mechanism was imposed by the state legislature. While no other state enacted such requirements, some other cities were still able to adopt the contracting scheme. The regional transit federation model, in which the planning and operating roles are separated, certainly favors service privatization. Minneapolis, Phoenix, and
San Diego contract out substantial portions of their services. Simultaneously, systems dominated by a core agency that retains both planning and operating functions, tend to be reluctant to privatization, which is in most cases, explicitly prohibited by the collective bargaining agreements with the labor unions. The federal law prohibits the recipients of transit capital from any changes in the labor agreements that make the workers worse off, unless they agree to such changes (TRB, 2014; U.S. Code, 2008). Whenever a collective labor agreement was in force when the agency received federal assistance for the first time, the agency cannot withdraw from such agreement. In practice, that means that any substantial changes to the labor agreement have to be accepted by the labor unions. As the unions tend to oppose service contracting, or other decisions affecting their benefits, many agencies are unable to implement the contracting scheme, or earn any substantial savings from contracting. Therefore, even if there is clear evidence for the economic advantages of contracting, such planning strategy will be problematic to implement. Still, there are some successful stories of implementing the contracting scheme in several cities. It appears that separating planning authorities from operating entities might be one of the possible ways to address the aforementioned obstacles. In addition, as indicated by the case of Denver, delegating the service private contractors does not necessarily mean violating the federal assistance regulations. Both private companies contracted by RTD Denver allow their
124 employees to unionize, which means that the federal requirements are satisfied. Still, their contracts are more flexible, and less financially exhaustive, which reduces the costs of service operated by those companies (Marsella, 2012). Further research should investigate more deeply the specific patterns of contractual agreements, and the decisions made to comply with the federal requirements for funding eligibility in those several areas that had successfully established the contracting model.
The analysis has also revealed the important role of state government in establishing regional transit authorities and agencies. All successful regional governance structures mechanisms were either directly established by the state, or formed after the state had established funding mechanisms for the regional transit system. The remaining regions were so far unsuccessful in consolidating their networks. Even if the core agency attempted to expand its services to the neighboring jurisdictions, or to incorporate the surrounding smaller systems, the smaller jurisdictions were unable to establish any reliable funding mechanisms, and therefore, could not satisfy the financial requirements for joining the major agency.
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CHAPTER FIVE
CONCLUSIONS AND IMPLICATIONS
5.1 Key Findings and Implications
The main goal of the study was to determine how internal planning decisions
(made in regards to network design, service planning, transit ownership, and governance) affect the economic outcomes of multimodal bus and light rail transit systems. I selected “net benefits per passenger mile” as the primary indicator of a transit system’s feasibility. Net benefits were defined as the difference between benefits, (including transit agency’s revenues and non-direct social transit benefits) and the costs (including current operating expenses and annualized capital costs). After analyzing data from 13 multimodal transit systems collected between 2001 and
2011, the study has determined that specific planning decisions, including higher service frequency and density, as well as higher amounts of contracted service and consolidation of planning and managerial functions, are positively correlated with net benefits generated by multimodal transit systems significantly. Simultaneously, the level of service decentralization, reflected by the percent of service outside the central business district, appears to have no significant impact on the amount of transit benefits of the analyzed systems.
The study also revealed substantial differences between the net benefits generated by particular bus and light rail transit systems, and identified several possible factors explaining these differences. The following paragraphs will briefly synthesize the findings related to specific systems, and draw further implications and conclusions. Table 5.1 presents an overview of the key results of the quantitative analyses conducted throughout the three major stages of the study.
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Table 5.1 Summary of Study’s Key Quantitative Results
Service Planning Characteristics
Average headway (in Service Density (Rev. Miles Decentralization ratio minutes) per Service Area sq m, 000s)
2001 2006 2011 2001 2006 2011 2001 2006 2011 Buffalo 30% 32% 48% 26.1 22.6 23.8 191 199 233 Charlotte 29% 21.5 200 Dallas 54% 55% 66% 13.3 11.1 11.9 321 366 324 Denver 61% 64% 58% 22.3 21.8 21.8 192 237 295 Houston 37% 37% 14.5 16.2 417 264 Minneapolis 37% 36% 16.9 17.2 673 700 Phoenix 62% 21.8 408 Pittsburgh 18% 14% 15% 17.4 17.9 20.6 362 273 209 Portland 46% 38% 39% 12.6 10.5 11.1 526 663 672 Sacramento 60% 63% 65% 37.0 33.1 30.9 224 188 202 Salt Lake 40% 53% 59% 13.0 13.9 21.3 81 166 138 San Diego 65% 65% 60% 21.0 32.5 21.9 573 539 377 St. Louis 60% 74% 75% 19.4 25.9 22.0 297 351 405
NET BENEFITS per Ownership and Organizational Form passenger mile Number of Independent Share of Contracted Service Transit Organizational Volume Entities 2001 2006 2011 2001 2006 2011 2001 2006 2011 Buffalo 0% 0% 0% 1 1 1 -$0.53 -$0.84 -$0.75 Charlotte 5% 3 -$0.42 Dallas 0% 3% 0% 2 3 4 -$0.24 -$0.30 -$0.79 Denver 30% 47% 46% 1 1 1 -$0.09 -$0.17 -$0.17 Houston 21% 22% 1 3 -$0.24 -$0.52 Minneapolis 22% 24% 1 1 -$0.13 -$0.18 Phoenix 100% 1 -$0.44 Pittsburgh 7% 6% 9% 4 5 6 -$0.16 -$0.56 -$0.78 Portland 0% 0% 0% 2 3 3 $0.05 -$0.10 -$0.10 Sacramento 28% 39% 47% 5 7 8 -$0.10 -$0.50 -$0.33 Salt Lake 0% 0% 0% 1 1 1 -$0.40 $0.01 -$0.38 San Diego 18% 39% 64% 1 1 1 $0.30 $0.10 $0.49 St. Louis 12% 16% 15% 2 2 2 -$0.21 -$0.29 -$0.34
Panel regression results (dependent variable: net benefits per mile)
service planning variables ownership variables all internal variables
Variable Coeff. Std.Err t-stat Coeff. Std.Err t-stat Coeff. Std.Err t-stat Decentralization -0.100 0.314 -0.32 0.100 0.305 0.33 Headway -0.010 0.005 -2.12 -0.010 0.005 -2.19 Service Density 0.001 0.000 3.50 0.001 0.000 3.15
Contracting 0.585 0.256 2.28 0.740 0.250 2.96 Regional Entities -0.082 0.024 -3.40 -0.050 0.024 -2.10
Pop Density 0.199 0.229 0.87 0.313 0.229 1.37 0.263 0.218 1.21 Unemployment 0.022 0.015 1.47 0.022 0.016 1.35 0.005 0.016 0.32 Zero-vehicle HHs -0.018 0.026 -0.70 0.018 0.024 0.75 -0.011 0.025 -0.43 Median Income 0.011 0.009 1.23 -0.005 0.008 -0.58 0.003 0.008 0.38 Gas Price 0.105 0.177 0.59 0.034 0.173 0.20 0.121 0.167 0.72 TTI (Congestion) -2.038 0.633 -3.22 -1.437 0.594 -2.42 -1.658 0.605 -2.74 127
As illustrated by Table 5.1, which combines the key results presented in Chapters 2-4,
San Diego was the only system that generated positive net benefits during the entire period of the analysis. Three other systems (Denver, Minneapolis, and Portland) generated relatively satisfactory benefits, ranging from approximately -$0.20 to $0.05 per passenger year. Transit networks in Denver, Minneapolis, Portland, and San Diego seem to employ similar planning strategies. They provide frequent and expansive service, although they do not necessarily have a majority of their service volume allocated to non-CBD routes. Except for Portland, they are governed by a regional authority that coordinates the transit service within the metropolitan area, and they contract out at least 20% of fixed-route bus service during the analysis timeframe. The situation with ownership and management is slightly different in Portland, where entire service is directly operated by the local agencies and it is not governed by a single authority. Still, the area’s two major agencies established some patterns of cooperation.
Systems in Buffalo, Dallas and Pittsburgh turned out to yield the lowest average net benefits among the analyzed cases. As already noticed earlier in the text, the low efficiency of
Buffalo and Pittsburgh systems could not be explained only with the declining regional economy, as the lower-than-average welfare actually results in a higher share of transit- dependent population. In all of these cities, frequency and service density are below the average, and the share of contracted services is insignificant. Additionally, transit governance in the
Pittsburgh and Dallas metro areas is partitioned among several agencies. The lack of consolidated regional management limits the opportunities of seamless regional travel
(particularly in Dallas, where significant portions of the metropolitan region is not served by any fixed-route transit), and forbids the agencies from utilizing the advantages of economies of scale.
The remaining seven systems, located around the midpoint of the net benefit classification,
128 usually employ some of the internal planning strategies that have a positive influence on economic outcomes, simultaneously restraining from making other planning decisions that could increase the amount of generated benefits.
The results of this study are generally consistent with previous works that have identified the advantages of market-oriented planning decisions focused on adapting the service to the existing land-use patterns, and on adjusting other internal parameters directly controlled by the planners and policy-makers, such as frequency, interconnectivity, and coverage. As determined by the previous evaluations, such networks tend to attract more riders and yield better performance results when compared to the systems employing other strategies (for example, focusing primarily on providing peak-hour service on selected corridors). This study indicates that systems adjusting their internal service parameters towards the optimum are also likely to generate higher net benefits. The only outcome of the study that is not consistent, at least with a part of the discussed literature, is the insignificance of network decentralization in influencing the economic results. Most of the previous studies have recognized the level of decentralization as a strong, positive determinant of ridership and average load. However, one of the earlier analyses (Brown and Thompson, 2008a) indicated that decentralization is not a significant predictor of ridership in metropolitan areas with a population exceeding 1 million (all regions analyzed by this study fall into that category). As determined by this analysis, decentralization itself does not guarantee satisfactory economic outcomes. Several multi-destination systems, with decentralization ratio exceeding 60% (Dallas, Phoenix, Sacramento, and St. Louis), yield much lower average benefits than some of the systems with smaller degree of decentralization
(e.g. Minneapolis and Portland). The author speculates that decentralization itself does not guarantee better economic results; it might be successful in increasing feasibility if combined
129 with other decisions. As suggested by the Brookings Institute report on job accessibility (Tomer,
2011, 2012), a mismatch between transit service coverage and the employment locations is a serious issue in American cities. Therefore, it is possible that, even if the majority of service is running outside the CBD, it still will not be serving important trip destinations. Adding the aspect of job accessibility and other features important from the riders’ perspective is one of the key directions for further research on the role of internal and external factors.
The unexpected outcome for the decentralization variable might also be caused by some
specific characteristics of the selected case set (the previous research analyzed different case
sets) or by the imperfectness of the research methodology. Due to the unavailability of uniform,
up-to-date definitions of the central business district, the adopted “Non-CBD service” measure
might not reflect correctly the actual level of decentralization. Nevertheless, the supplemental
analysis of route-level operating costs has indicated that the suburban bus services, including
especially the routes connecting to rail lines, are not necessarily much more expensive to operate
than the services headed to the urban core. They are also performing at comparable levels in the
means of ridership and average vehicle load. Such results are contrary to the findings of several
studies, including the frequently cited work of Pushkarev and Zupan (1977), which has claimed
that radial service focused on the dense urban core is the only type of transit that is feasible to
operate in U.S. cities.
The results of this study also correspond with international findings on the advantages of
service contracting and regional transit management as well as with a majority of previous
studies on contracting effects performed within the U.S. cities, simultaneously bringing new
insights on the importance of transit organizational and managerial decisions, and identifying
obstacles for broader implementation of non-traditional management strategies. While the up-to-
130 date literature evaluated the influence of contracting using primarily the “current operating expenses” measure, this study shows that contracting has also a positive influence on the larger spectrum of transit economic outcomes. The study has also performed one of the first economic evaluations of various transit governance structures present in the United States. The results indicate that systems coordinated by a single, regional entity, consolidating multiple jurisdictions
(although not necessarily operated by a single agency) are likely to be more feasible than the partitioned networks, or networks operated by a single entity that performs all planning and managerial functions, as well as operates the service.
Additionally, the study has briefly analyzed the policy process related to the implementation of certain ownership and organizational mechanisms in the analyzed systems.
The policy analysis brought several interesting observations. Service contracting is usually associated with the regional governance model, in which the entity responsible for service planning is separated from the service operators. If the core agency serves both as the planning authority and as the service operator, it usually does not contract any service due to the strong role of labor unions, reinforced by federal regulations securing the collective bargaining rights in agencies that benefit from federal transit assistance. Separation of the planning and operating functions seems to make contracting easier, as it allows delegating the service to smaller agencies, in which the role of unions is less significant, or to newly created agencies, which are excluded from the aforementioned federal regulations. As noticed earlier in the text, further research should analyze precisely how some agencies were able to contract a substantial portion of their service despite the federal regulations regarding labor agreements. The author has also focused on the emergence and functionality of the regional governance patterns. The analysis revealed that the consolidated regional authorities, responsible for system planning, appear to be
131 created by top-down decisions, made by state legislatures. Without state support, the large, core cities themselves find it difficult to implement the regional management, and incorporate the surrounding jurisdictions to regional transit systems. Funding issues seem to be major constrains, as the outlying counties and municipalities usually reject enacting transit-dedicated taxation, required for facilitating high volumes of regional services.
In sum, the research findings bring several interesting implications for transit planners, decision-makers, and planning scholars. There are clearly substantial differences between the economic outcomes of specific multimodal bus and light rail systems, even though these systems share many common characteristics and operate within comparable settings. The major take- away from the study is that certain planning decisions made with regards to service design, as well as ownership and managerial structure appear to be, at least, partially responsible for explaining these differences. By replicating these decisions, other, less feasible systems should be able to increase their revenues, provide additional benefits for the society, and reduce the burden of excessive transit subsidization. Finally yet importantly, the study brings additional evidence supporting the positive outcomes of the market-oriented transit planning decisions, showing that they could successfully complement or even replace the popular strategies for increasing transit ridership and efficiency, which are based on adjusting the external ridership factors.
5.2 Opportunities for Future Research
The study design has included three major stages: benefit-cost analysis, evaluation of service planning decisions, and evaluation of organizational decisions. All of the stages provided interesting results and implications, simultaneously brining several opportunities for continuing
132 and expanding exploration of the issues investigated by this study. This includes conducting similar assessments of other types of transit networks, for example, heavy rail systems, or bus- only systems. This also includes a more detailed exploration of the factors affecting economic outcomes of particular systems assessed by the study. Even though the evaluated influence of internal factors on the net benefits was determined to be statistically significant, in some individual cases, these relationships appear not to be that strong. The following paragraphs elaborate on the possible directions for further research.
The benefit-cost analysis revealed inconsistencies in the methodologies utilized
previously by similar analyses. Therefore, the author attempted to identify the best practices for
conducting a transit BCA. As the BCA itself was not the major research theme of the study, the
author did not fully explore all potential benefits of transit investments, as well as the possible
methodologies for their evaluation. Nevertheless some gaps and flaws in the existing methods
have been identified. A deeper investigation of the transit BCA methodology and framework
could address those gaps, and would be a valuable addition to the scholarship on economic
outcomes of transit. Future research should also investigate whether these additional benefit
categories are reasonable, and whether they meet the major theoretical foundations of the BCA.
As noticed in Chapter 2, the benefit-cost analysis results could be substantially different, if other, more liberal or conservative scenarios were adopted. The author attempted to adopt midpoint values whenever the literature provided a range of possible parameters (e.g. for estimating average externalities per passenger mile); however, these midpoint values might not be applicable to this specific case set. An additional, more complex investigation could reevaluate the assumptions made with regards to the consumer surplus and other non-direct benefits, trying to conduct more precise estimations of user benefits and externalities for specific transit systems.
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Further research could also focus on the relationship between internal planning decisions and net benefits at the route level, or at other levels of disaggregation, for example, at particular rider markets. Due to the longitudinal and multi-dimensional nature of this research, the author made some simplifications through the study and did not explore some of the issues to the full extent. That creates additional opportunities for further research built on certain, particular stages of the study.
Several previous bodies of scholarship already evaluated the role of the internal transit planning decisions. This research provided additional insights on those decisions. Still, there are several more opportunities for investigating the internal decisions, and some gaps in the literature still exist. As discussed in Chapter 3, previous studies examine the internal decisions primarily from the transit agency’s perspective, focusing on measures, such as ridership and service effectiveness (average vehicle load). This study provided an economic assessment of those decisions. The other important aspect of internal decisions that has not been studied extensively so far, is the relationship between internal decisions and transit attractiveness for the riders.
Specifically, that includes characteristics, such as accessibility, mobility, convenience (e.g. easiness of transfers), and other parameters important from the rider’s perspective. As already noticed in this chapter, the decentralization (and possibly other internal decisions) might have an insignificant influence on ridership, if the service is not directed towards important trip attractors, and is not adjusted to the actual travel demand.
The additional route-level financial analysis of specific route categories, discussed in
Chapter 3, was limited to a one-year period, and omitted some of the cases due to data unavailability. Further, a more robust assessment of the benefits and costs of particular route
134 types should bring additional insights on the economic outcomes of specific route structures and service strategies.
The last stage of the study included a brief policy analysis, focused on emergence and the
current functtionality of transit ownership and managerial schemes. As indicated in the literature
review, the previous research on service privatization outcomes was limited to assessing the
current operating expenses, while it usually did not explore how the privatization mechanism
emerged, how it functioning currently, how the particular roles (planning, operations) were
distributed between the public authority and private contractor, and what were the overall
economic outcomes of contracting. The literature on transit management and governance
strategies discusses the experiences and governance models that were adopted across the
country, but does not evaluate the economic outcomes of particular governance structures, and
includes only a limited spectrum of cases. While the policy discussion included in this study
attempted to address at least partially the literature gap, it did not fully investigate the nuances of
the decision-making processes regarding ownership and governance structures. Further research
could broaden the policy analysis by focusing more deeply on the roles played by particular
stakeholders, the attitude of the general public, emergence of the funding provisions, and other
elements relevant for understanding the history and current mechanisms of transit management
in certain metropolitan areas.
Analogically as in the case of service planning factors, the governance models could also
be evaluated within the context of effects on the transit agencies and on the communities. It has
been noticed earlier in the text that regional consolidation is supposed to benefit the riders with
better travel options, and consequently, increase ridership and improve service effectiveness.
However, there is not much literature providing comprehensive analyses of the relationship
135 between governance patterns and the key transit outcomes, such as ridership and accessibility.
Further research along these lines would certainly provide additional understanding of the importance of regional governance, and possibly, would add valuable contributions to the ongoing debate on the best practices for improving overall transit outcomes.
136
APPENDIX A
ADDITIONAL RESULTS OF THE BENEFIT-COST ANALYSIS
Table A.1 Total Values of Estimated Benefits and Costs
All figures in millions of 2011 dollars
Char- Minnea- Pitts- Sacra- Salt Lake Buffalo Dallas Denver Houston Phoenix Portland San Diego St. Louis lotte polis burgh mento City
Agency Revenue (Fares + other direct revenues) 2001$ 28.92 $ 64.04 $ 93.64 $ 82.86 $ 103.87 $ 38.77 $ 28.46 $ 76.07 $ 46.96 2002$ 26.71 $ 76.75 $ 93.68 $ 86.26 $ 90.93 $ 47.65 $ 32.02 $ 83.02 $ 50.95 2003$ 24.91 $ 75.38 $ 83.36 $ 93.21 $ 89.13 $ 43.94 $ 33.49 $ 75.58 $ 47.35 2004$ 27.35 $ 80.77 $ 88.22 $ 65.96 $ 83.07 $ 88.34 $ 91.86 $ 35.01 $ 29.50 $ 82.16 $ 46.30 2005$ 27.63 $ 79.55 $ 89.06 $ 61.73 $ 99.17 $ 86.07 $ 85.22 $ 33.10 $ 33.38 $ 79.10 $ 50.09 2006$ 28.35 $ 96.45 $ 105.18 $ 74.72 $ 105.08 $ 88.03 $ 97.98 $ 41.60 $ 51.27 $ 82.54 $ 53.88 2007$ 28.84 $ 30.41 $ 65.44 $ 89.39 $ 79.19 $ 110.69 $ 90.72 $ 128.36 $ 51.34 $ 41.04 $ 160.00 $ 55.58 2008$ 29.88 $ 29.12 $ 71.37 $ 155.51 $ 68.75 $ 111.08 $ 93.16 $ 124.51 $ 52.50 $ 53.55 $ 161.75 $ 69.02 2009$ 31.30 $ 55.44 $ 86.10 $ 141.04 $ 86.95 $ 121.92 $ 67.72 $ 99.59 $ 138.50 $ 56.92 $ 48.16 $ 185.15 $ 48.19 2010$ 33.45 $ 23.15 $ 142.31 $ 117.94 $ 75.42 $ 120.16 $ 72.58 $ 99.60 $ 132.00 $ 53.07 $ 48.48 $ 178.53 $ 49.90 2011$ 32.62 $ 24.75 $ 111.12 $ 121.56 $ 80.05 $ 115.25 $ 72.03 $ 102.82 $ 128.87 $ 49.47 $ 47.05 $ 258.80 $ 53.33
Consumer Surplus 2001$ 21.91 $ 35.49 $ 46.59 $ 52.61 $ 63.01 $ 24.51 $ 15.10 $ 79.90 $ 34.43 2002$ 20.69 $ 29.53 $ 50.15 $ 56.89 $ 63.57 $ 30.94 $ 18.83 $ 74.25 $ 36.13 2003$ 19.72 $ 34.57 $ 50.18 $ 61.00 $ 47.49 $ 25.03 $ 17.64 $ 72.56 $ 35.88 2004$ 21.77 $ 36.44 $ 53.24 $ 46.23 $ 53.62 $ 59.84 $ 64.23 $ 26.20 $ 18.42 $ 78.74 $ 32.81 2005$ 21.92 $ 36.00 $ 53.42 $ 43.35 $ 66.31 $ 57.28 $ 67.67 $ 25.57 $ 19.43 $ 77.23 $ 36.34 2006$ 22.53 $ 64.14 $ 59.21 $ 46.03 $ 71.59 $ 57.21 $ 72.17 $ 30.55 $ 20.77 $ 79.15 $ 38.19 2007$ 22.50 $ 9.98 $ 38.74 $ 70.49 $ 44.26 $ 73.04 $ 56.84 $ 78.00 $ 34.28 $ 21.12 $ 77.24 $ 41.70 2008$ 22.60 $ 12.75 $ 46.14 $ 77.59 $ 43.69 $ 76.54 $ 59.79 $ 80.65 $ 35.64 $ 26.06 $ 80.25 $ 43.04 2009$ 24.30 $ 17.32 $ 44.39 $ 85.12 $ 55.11 $ 78.46 $ 38.01 $ 66.26 $ 89.30 $ 39.60 $ 26.11 $ 87.30 $ 44.94 2010$ 25.23 $ 16.29 $ 44.07 $ 84.87 $ 50.13 $ 77.76 $ 46.10 $ 66.01 $ 91.94 $ 36.50 $ 27.05 $ 86.94 $ 42.03 2011$ 25.46 $ 18.44 $ 48.26 $ 90.65 $ 52.68 $ 77.42 $ 46.11 $ 68.49 $ 93.43 $ 34.07 $ 30.43 $ 84.45 $ 41.91
Congestion savings 2001$ 12.04 $ 115.60 $ 85.30 $ 93.77 $ 96.25 $ 31.17 $ 27.65 $ 126.33 $ 46.75 2002$ 11.63 $ 109.81 $ 83.97 $ 83.12 $ 109.46 $ 29.65 $ 29.00 $ 112.11 $ 52.93 2003$ 11.46 $ 116.02 $ 83.58 $ 78.62 $ 108.57 $ 29.80 $ 34.61 $ 108.31 $ 50.74 2004$ 11.01 $ 108.05 $ 87.79 $ 119.78 $ 71.69 $ 73.17 $ 113.56 $ 30.16 $ 32.08 $ 110.91 $ 52.95 2005$ 11.99 $ 120.36 $ 96.66 $ 117.01 $ 88.51 $ 74.20 $ 111.74 $ 31.35 $ 38.70 $ 113.70 $ 53.28 2006$ 12.52 $ 124.30 $ 103.08 $ 128.28 $ 92.13 $ 75.10 $ 112.65 $ 34.68 $ 56.11 $ 118.73 $ 52.29 2007$ 13.16 $ 17.20 $ 124.99 $ 117.34 $ 127.75 $ 100.01 $ 83.68 $ 107.61 $ 35.19 $ 59.09 $ 119.79 $ 55.46 2008$ 14.27 $ 20.77 $ 121.30 $ 120.85 $ 134.09 $ 110.27 $ 75.73 $ 111.98 $ 39.52 $ 67.40 $ 119.70 $ 58.80 2009$ 13.73 $ 19.99 $ 116.69 $ 116.25 $ 128.98 $ 106.06 $ 63.58 $ 72.86 $ 122.77 $ 42.63 $ 64.83 $ 125.75 $ 56.56 2010$ 13.63 $ 19.85 $ 115.90 $ 115.46 $ 128.11 $ 105.36 $ 63.16 $ 72.37 $ 121.57 $ 39.68 $ 64.40 $ 112.19 $ 56.19 2011$ 15.90 $ 22.62 $ 116.00 $ 118.26 $ 118.70 $ 113.91 $ 73.82 $ 71.43 $ 119.72 $ 36.16 $ 56.17 $ 114.97 $ 55.98
Reduction in Negative Environmental Impacts 2001$ 6.54 $ 29.64 $ 33.66 $ 33.71 $ 36.38 $ 14.01 $ 10.53 $ 44.20 $ 21.20 2002$ 6.07 $ 25.37 $ 31.13 $ 28.43 $ 39.70 $ 12.72 $ 9.78 $ 37.41 $ 24.01 2003$ 5.66 $ 32.25 $ 29.75 $ 26.27 $ 38.41 $ 12.18 $ 11.25 $ 34.56 $ 22.04 2004$ 5.07 $ 28.22 $ 29.50 $ 40.77 $ 22.88 $ 22.75 $ 38.34 $ 11.61 $ 8.87 $ 33.00 $ 20.85 2005$ 5.27 $ 30.34 $ 30.51 $ 36.94 $ 27.52 $ 21.80 $ 35.21 $ 11.19 $ 10.54 $ 31.80 $ 19.59 2006$ 5.16 $ 29.50 $ 31.06 $ 37.87 $ 26.56 $ 20.86 $ 33.46 $ 11.70 $ 16.71 $ 31.45 $ 18.10 2007$ 5.15 $ 5.36 $ 27.87 $ 34.43 $ 34.91 $ 28.36 $ 22.58 $ 30.04 $ 11.19 $ 17.15 $ 30.12 $ 18.46 2008$ 5.20 $ 6.44 $ 23.29 $ 32.72 $ 34.54 $ 29.13 $ 19.11 $ 29.05 $ 11.87 $ 15.54 $ 27.98 $ 18.40 2009$ 5.84 $ 7.08 $ 20.77 $ 31.99 $ 30.99 $ 25.13 $ 19.36 $ 19.43 $ 32.21 $ 13.01 $ 9.75 $ 30.00 $ 19.03 2010$ 4.97 $ 6.80 $ 19.26 $ 31.81 $ 27.45 $ 27.21 $ 17.13 $ 17.72 $ 29.89 $ 11.64 $ 10.87 $ 25.76 $ 15.67 2011$ 5.04 $ 6.84 $ 21.77 $ 32.36 $ 24.73 $ 24.28 $ 16.08 $ 14.00 $ 27.92 $ 10.01 $ 10.92 $ 24.98 $ 16.48
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Table A.1 – Continued
All figures in millions of 2011 dollars
Char- Minnea- Pitts- Sacra- Salt Lake Buffalo Dallas Denver Houston Phoenix Portland San Diego St. Louis lotte polis burgh mento City
Reduction in Accident-related Healthcare costs 2001$ 9.43 $ 43.85 $ 49.00 $ 49.51 $ 54.09 $ 20.81 $ 14.88 $ 66.35 $ 30.97 2002$ 8.72 $ 37.25 $ 45.08 $ 41.37 $ 59.22 $ 18.78 $ 13.72 $ 55.85 $ 35.50 2003$ 8.11 $ 47.19 $ 43.16 $ 38.25 $ 57.30 $ 17.96 $ 16.05 $ 51.60 $ 32.61 2004$ 7.22 $ 41.26 $ 42.77 $ 59.86 $ 33.35 $ 32.96 $ 57.30 $ 17.09 $ 12.47 $ 48.99 $ 30.85 2005$ 7.56 $ 44.55 $ 44.44 $ 54.31 $ 40.49 $ 31.63 $ 52.60 $ 16.40 $ 15.14 $ 47.28 $ 28.99 2006$ 7.41 $ 43.55 $ 45.56 $ 56.04 $ 39.14 $ 30.33 $ 50.06 $ 17.20 $ 24.78 $ 46.89 $ 26.75 2007$ 7.40 $ 7.61 $ 41.13 $ 50.94 $ 51.63 $ 42.07 $ 33.10 $ 44.89 $ 16.42 $ 25.53 $ 44.97 $ 27.35 2008$ 7.48 $ 9.30 $ 34.15 $ 48.46 $ 51.21 $ 43.36 $ 28.07 $ 43.43 $ 17.54 $ 23.11 $ 41.83 $ 27.32 2009$ 8.42 $ 10.26 $ 30.23 $ 47.34 $ 45.61 $ 37.19 $ 27.99 $ 28.54 $ 48.32 $ 19.29 $ 14.13 $ 45.03 $ 28.32 2010$ 7.12 $ 9.92 $ 27.98 $ 47.14 $ 40.16 $ 40.47 $ 24.70 $ 25.92 $ 44.87 $ 17.23 $ 15.91 $ 38.60 $ 23.18 2011$ 7.32 $ 9.98 $ 32.08 $ 48.20 $ 36.05 $ 36.03 $ 23.35 $ 20.35 $ 42.00 $ 14.84 $ 16.08 $ 37.52 $ 24.40
TOTAL BENEFITS (Direct and Non-Direct) 2001$ 78.85 $ 288.63 $ 308.19 $ 312.46 $ 353.60 $ 129.27 $ 96.62 $ 392.85 $ 180.32 2002$ 73.82 $ 278.71 $ 304.02 $ 296.06 $ 362.87 $ 139.74 $ 103.35 $ 362.64 $ 199.52 2003$ 69.87 $ 305.42 $ 290.03 $ 297.35 $ 340.90 $ 128.91 $ 113.04 $ 342.60 $ 188.62 2004$ 72.42 $ 294.73 $ 301.52 $ 332.61 $ 264.60 $ 277.07 $ 365.29 $ 120.07 $ 101.34 $ 353.80 $ 183.76 2005$ 74.38 $ 310.80 $ 314.10 $ 313.33 $ 322.00 $ 270.98 $ 352.44 $ 117.62 $ 117.19 $ 349.11 $ 188.28 2006$ 75.96 $ 357.94 $ 344.08 $ 342.95 $ 334.51 $ 271.53 $ 366.32 $ 135.74 $ 169.64 $ 358.76 $ 189.22 2007$ 77.04 $ 70.56 $ 298.18 $ 362.59 $ 337.75 $ 354.17 $ 286.92 $ 388.90 $ 148.41 $ 163.93 $ 432.12 $ 198.55 2008$ 79.43 $ 78.39 $ 296.26 $ 435.13 $ 332.29 $ 370.37 $ 275.85 $ 389.62 $ 157.06 $ 185.66 $ 431.50 $ 216.58 2009$ 83.59 $ 110.10 $ 298.18 $ 421.73 $ 347.65 $ 368.76 $ 216.67 $ 286.69 $ 431.11 $ 171.45 $ 162.98 $ 473.24 $ 197.04 2010$ 84.40 $ 76.00 $ 349.52 $ 397.21 $ 321.27 $ 370.96 $ 223.67 $ 281.62 $ 420.26 $ 158.12 $ 166.71 $ 442.02 $ 186.97 2011$ 86.34 $ 82.63 $ 329.24 $ 411.03 $ 312.21 $ 366.90 $ 231.39 $ 277.09 $ 411.93 $ 144.54 $ 160.66 $ 520.72 $ 192.11
Capital Costs, Rail 2001$ 22.75 $ 52.84 $ 16.95 $ 27.00 $ 47.69 $ 11.19 $ 12.09 $ 32.70 $ 28.22 2002$ 22.90 $ 57.43 $ 21.54 $ 31.12 $ 51.76 $ 13.37 $ 13.68 $ 33.07 $ 30.89 2003$ 23.00 $ 61.27 $ 30.07 $ 33.57 $ 54.11 $ 16.65 $ 15.55 $ 33.75 $ 34.01 2004$ 23.21 $ 64.40 $ 36.85 $ 14.42 $ 20.56 $ 36.62 $ 56.47 $ 22.14 $ 16.27 $ 34.21 $ 40.49 2005$ 23.42 $ 68.47 $ 44.41 $ 15.45 $ 20.68 $ 40.19 $ 57.80 $ 24.14 $ 16.65 $ 34.21 $ 47.12 2006$ 23.59 $ 73.74 $ 49.07 $ 15.85 $ 21.53 $ 42.00 $ 59.41 $ 25.33 $ 16.98 $ 49.10 $ 53.11 2007$ 23.75 $ 12.67 $ 84.92 $ 53.10 $ 19.73 $ 22.00 $ 46.28 $ 63.88 $ 26.28 $ 19.75 $ 50.05 $ 55.46 2008$ 24.07 $ 14.38 $ 102.76 $ 61.15 $ 24.69 $ 23.16 $ 50.04 $ 71.49 $ 26.52 $ 32.96 $ 51.32 $ 56.31 2009$ 24.22 $ 15.77 $ 128.16 $ 73.83 $ 30.45 $ 29.14 $ 66.22 $ 53.27 $ 77.48 $ 27.46 $ 43.81 $ 52.12 $ 57.22 2010$ 24.63 $ 16.78 $ 149.95 $ 87.81 $ 39.64 $ 32.06 $ 67.42 $ 56.84 $ 79.49 $ 28.36 $ 55.13 $ 52.80 $ 57.61 2011$ 24.77 $ 17.29 $ 161.75 $ 96.82 $ 49.42 $ 40.69 $ 67.73 $ 61.61 $ 82.42 $ 29.44 $ 64.30 $ 53.34 $ 58.16
Capital Costs, Total 2001$ 30.73 $ 95.96 $ 62.82 $ 76.03 $ 66.31 $ 24.59 $ 27.54 $ 46.29 $ 46.28 2002$ 32.36 $ 106.05 $ 67.96 $ 85.22 $ 70.84 $ 27.93 $ 30.01 $ 47.05 $ 52.78 2003$ 33.62 $ 110.62 $ 76.69 $ 87.14 $ 74.20 $ 33.05 $ 32.11 $ 47.61 $ 57.32 2004$ 33.84 $ 116.89 $ 83.27 $ 141.94 $ 82.67 $ 92.15 $ 77.52 $ 41.36 $ 33.61 $ 48.81 $ 64.67 2005$ 34.83 $ 121.46 $ 93.04 $ 149.19 $ 84.09 $ 98.41 $ 79.07 $ 43.63 $ 35.62 $ 49.53 $ 71.03 2006$ 35.12 $ 127.91 $ 101.47 $ 155.10 $ 88.34 $ 100.84 $ 81.56 $ 44.81 $ 37.11 $ 65.06 $ 77.22 2007$ 36.45 $ 30.78 $ 139.63 $ 106.40 $ 163.88 $ 91.15 $ 104.97 $ 86.16 $ 46.00 $ 41.69 $ 67.10 $ 79.34 2008$ 37.23 $ 33.21 $ 158.64 $ 115.11 $ 171.52 $ 97.73 $ 109.39 $ 94.66 $ 48.29 $ 56.13 $ 69.42 $ 80.13 2009$ 37.28 $ 35.39 $ 184.29 $ 127.36 $ 177.26 $ 103.73 $ 117.50 $ 115.10 $ 101.96 $ 51.01 $ 68.55 $ 75.02 $ 81.65 2010$ 38.23 $ 36.88 $ 206.72 $ 142.41 $ 186.44 $ 111.27 $ 119.82 $ 121.75 $ 104.42 $ 52.31 $ 81.99 $ 78.26 $ 82.00 2011$ 40.08 $ 37.75 $ 220.40 $ 153.42 $ 200.19 $ 124.06 $ 120.27 $ 128.01 $ 107.34 $ 53.48 $ 92.06 $ 80.35 $ 84.48
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Table A.1 – Continued
All figures in millions of 2011 dollars
Char- Minnea- Pitts- Sacra- Salt Lake Buffalo Dallas Denver Houston Phoenix Portland San Diego St. Louis lotte polis burgh mento City
Operating Costs, Rail 2001$ 18.46 $ 51.48 $ 19.22 $ 37.10 $ 50.85 $ 32.06 $ 14.24 $ 47.35 $ 28.78 2002$ 18.42 $ 56.16 $ 23.74 $ 37.85 $ 70.34 $ 30.17 $ 28.02 $ 46.71 $ 42.54 2003$ 20.84 $ 70.35 $ 24.53 $ 39.01 $ 67.60 $ 37.13 $ 24.36 $ 47.66 $ 44.87 2004$ 21.76 $ 67.90 $ 25.83 $ 16.83 $ 9.96 $ 42.38 $ 67.83 $ 41.95 $ 23.83 $ 49.81 $ 43.22 2005$ 22.44 $ 79.79 $ 30.91 $ 16.24 $ 19.19 $ 45.49 $ 77.85 $ 47.04 $ 23.85 $ 55.24 $ 48.57 2006$ 23.36 $ 89.59 $ 38.90 $ 15.20 $ 20.89 $ 45.47 $ 78.09 $ 57.04 $ 25.81 $ 61.38 $ 45.48 2007$ 25.28 $ 2.10 $ 86.59 $ 43.94 $ 16.33 $ 23.79 $ 46.29 $ 79.91 $ 51.45 $ 28.41 $ 60.70 $ 55.76 2008$ 24.49 $ 9.92 $ 93.21 $ 43.54 $ 16.57 $ 24.76 $ 46.33 $ 87.88 $ 54.15 $ 28.61 $ 58.45 $ 58.56 2009$ 25.38 $ 17.61 $ 108.75 $ 53.47 $ 16.54 $ 26.21 $ 16.44 $ 54.25 $ 101.47 $ 52.87 $ 30.42 $ 61.37 $ 60.61 2010$ 24.32 $ 16.55 $ 115.52 $ 73.68 $ 15.28 $ 26.55 $ 34.01 $ 51.72 $ 109.73 $ 49.36 $ 28.89 $ 62.84 $ 55.65 2011$ 24.70 $ 16.57 $ 139.90 $ 63.25 $ 17.50 $ 25.72 $ 31.02 $ 48.14 $ 93.40 $ 43.82 $ 34.82 $ 60.40 $ 59.32
Operating Costs, Total 2001$ 88.86 $ 271.38 $ 281.03 $ 298.18 $ 269.24 $ 119.39 $ 120.71 $ 205.77 $ 183.80 2002$ 94.41 $ 303.37 $ 288.12 $ 309.92 $ 310.31 $ 116.56 $ 132.32 $ 210.99 $ 186.05 2003$ 93.23 $ 348.25 $ 290.35 $ 307.31 $ 303.43 $ 134.67 $ 126.83 $ 213.94 $ 186.55 2004$ 98.69 $ 319.81 $ 289.13 $ 308.14 $ 274.62 $ 313.40 $ 312.10 $ 146.08 $ 128.07 $ 245.78 $ 186.34 2005$ 101.70 $ 342.03 $ 306.56 $ 319.63 $ 296.30 $ 326.27 $ 334.59 $ 158.89 $ 127.41 $ 251.70 $ 184.12 2006$ 107.47 $ 356.95 $ 319.24 $ 314.19 $ 296.41 $ 345.29 $ 330.88 $ 174.57 $ 130.71 $ 252.05 $ 186.52 2007$ 118.04 $ 83.45 $ 356.50 $ 332.59 $ 313.16 $ 306.04 $ 339.79 $ 333.49 $ 174.70 $ 140.45 $ 249.07 $ 190.26 2008$ 117.69 $ 96.72 $ 381.80 $ 348.71 $ 321.93 $ 313.21 $ 332.68 $ 353.21 $ 182.88 $ 141.94 $ 250.55 $ 204.34 2009$ 124.36 $ 98.49 $ 393.89 $ 363.32 $ 355.61 $ 323.95 $ 300.51 $ 342.25 $ 376.90 $ 177.65 $ 145.00 $ 246.91 $ 203.16 2010$ 118.46 $ 95.55 $ 411.31 $ 364.91 $ 349.11 $ 323.02 $ 275.92 $ 357.55 $ 389.29 $ 166.11 $ 138.33 $ 248.75 $ 195.91 2011$ 116.65 $ 97.74 $ 414.68 $ 350.85 $ 350.30 $ 318.13 $ 243.64 $ 350.89 $ 350.23 $ 149.40 $ 142.64 $ 233.48 $ 206.68
TOTAL COSTS 2001$ 119.59 $ 367.34 $ 343.84 $ 374.21 $ 335.56 $ 143.98 $ 148.25 $ 252.06 $ 230.07 2002$ 126.77 $ 409.42 $ 356.08 $ 395.15 $ 381.15 $ 144.49 $ 162.33 $ 258.04 $ 238.83 2003$ 126.84 $ 458.87 $ 367.04 $ 394.45 $ 377.63 $ 167.73 $ 158.94 $ 261.55 $ 243.87 2004$ 132.53 $ 436.70 $ 372.39 $ 450.08 $ 357.28 $ 405.55 $ 389.61 $ 187.44 $ 161.68 $ 294.59 $ 251.01 2005$ 136.53 $ 463.49 $ 399.60 $ 468.82 $ 380.39 $ 424.68 $ 413.66 $ 202.52 $ 163.03 $ 301.23 $ 255.15 2006$ 142.60 $ 484.86 $ 420.71 $ 469.30 $ 384.75 $ 446.14 $ 412.45 $ 219.39 $ 167.82 $ 317.11 $ 263.75 2007$ 154.49 $ 114.23 $ 496.12 $ 438.99 $ 477.04 $ 397.19 $ 444.76 $ 419.65 $ 220.69 $ 182.15 $ 316.17 $ 269.60 2008$ 154.93 $ 129.93 $ 540.44 $ 463.81 $ 493.45 $ 410.94 $ 442.07 $ 447.88 $ 231.18 $ 198.07 $ 319.97 $ 284.46 2009$ 161.64 $ 133.87 $ 578.18 $ 490.68 $ 532.87 $ 427.68 $ 418.01 $ 457.34 $ 478.86 $ 228.66 $ 213.55 $ 321.94 $ 284.81 2010$ 156.68 $ 132.43 $ 618.02 $ 507.33 $ 535.55 $ 434.29 $ 395.73 $ 479.30 $ 493.70 $ 218.42 $ 220.32 $ 327.00 $ 277.92 2011$ 156.73 $ 135.48 $ 635.08 $ 504.26 $ 550.49 $ 442.19 $ 363.90 $ 478.90 $ 457.57 $ 202.88 $ 234.69 $ 313.83 $ 291.17
NET BENEFITS
2001 -$40.74 -$78.72 -$35.66 -$61.76 $18.04 -$14.71 -$51.63 $140.79 -$49.76 2002 -$52.95 -$130.71 -$52.06 -$99.09 -$18.28 -$4.76 -$58.98 $104.60 -$39.31 2003 -$56.98 -$153.45 -$77.01 -$97.09 -$36.73 -$38.81 -$45.91 $81.06 -$55.25 2004 -$60.11 -$141.96 -$70.88 -$117.48 -$92.68 -$128.48 -$24.32 -$67.37 -$60.33 $59.21 -$67.25 2005 -$62.15 -$152.69 -$85.50 -$155.49 -$58.39 -$153.70 -$61.22 -$84.90 -$45.85 $47.87 -$66.87 2006 -$66.63 -$126.92 -$76.63 -$126.35 -$50.24 -$174.61 -$46.13 -$83.65 $1.82 $41.65 -$74.53 2007 -$77.45 -$43.67 -$197.95 -$76.40 -$139.28 -$43.02 -$157.84 -$30.75 -$72.28 -$18.21 $115.95 -$71.05 2008 -$75.50 -$51.54 -$244.18 -$28.68 -$161.16 -$40.56 -$166.23 -$58.26 -$74.11 -$12.41 $111.53 -$67.88 2009 -$78.04 -$23.77 -$280.00 -$68.94 -$185.23 -$58.92 -$201.35 -$170.66 -$47.75 -$57.21 -$50.56 $151.30 -$87.77 2010 -$72.29 -$56.43 -$268.50 -$110.11 -$214.27 -$63.33 -$172.07 -$197.68 -$73.44 -$60.30 -$53.60 $115.01 -$90.95 2011 -$70.39 -$52.86 -$305.84 -$93.24 -$238.28 -$75.28 -$132.51 -$201.81 -$45.64 -$58.34 -$74.04 $206.88 -$99.05
Char- Minnea- Pitts- Sacra- Salt Lake Buffalo Dallas Denver Houston Phoenix Portland San Diego St. Louis lotte polis burgh mento City
139
APPENDIX B
ADDITIONAL INPUT DATA FOR REGRESSION ANALYSIS
Table B.1 Observations for Variables Reflecting External Transit Influences
Char- Minnea- Pitts- Sacra- Salt Lake San Buffalo Dallas Denver Houston Phoenix Portland St. Louis lotte polis burgh mento City Diego
Population Density - Core County (persons per sq mi) 2001 771 2476 3627 1709 1438 1272 1127 634 2305 2002 769 2477 3594 1699 1453 1307 1137 641 2294 2003 768 2473 3566 1690 1459 1334 1145 644 2283 2004 765 2470 3555 2042 2086 1675 1444 1355 1156 647 2270 2005 759 2477 3561 2071 2079 1658 1449 1367 1174 649 2253 2006 754 2504 3594 2142 2082 1646 1468 1376 1197 651 2242 2007 751 1578 2523 3643 2173 2094 1642 1499 1387 1218 657 2233 2008 750 1626 2547 3716 2216 2110 1640 1531 1401 1237 668 2232 2009 749 1664 2583 3802 2270 2129 412 1641 1563 1415 1259 676 2234 2010 749 1690 2614 3895 2312 2142 415 1645 1584 1428 1279 686 2234 2011 748 1729 2659 4001 2352 2168 421 1648 1606 1442 1299 694 2232
Unemployment Rate (% ) 2001 4.9 4.7 3.8 4.8 6.0 4.5 4.4 4.2 4.6 2002 5.6 6.5 5.9 5.7 7.8 5.5 5.9 5.2 5.4 2003 5.9 6.6 6.4 5.9 8.3 5.8 5.8 5.2 5.8 2004 5.8 5.8 5.9 6.2 4.4 5.7 7.0 5.5 5.1 4.7 6.0 2005 5.3 5.2 5.2 5.6 3.9 5.2 5.9 4.9 4.1 4.3 5.6 2006 5.1 4.8 4.4 5.0 3.8 4.7 5.0 4.7 2.9 4.0 5.1 2007 4.9 4.8 4.3 3.9 4.3 4.4 4.3 4.9 5.3 2.5 4.6 5.3 2008 5.9 6.4 5.0 4.9 4.8 5.1 5.1 6.0 7.0 3.2 6.0 6.4 2009 8.4 11.2 7.7 8.3 7.5 7.9 9.2 7.2 10.7 11.1 7.5 9.6 9.9 2010 8.5 11.7 8.2 9.1 8.5 7.3 9.7 7.8 10.5 12.5 7.9 10.5 9.8 2011 8.1 10.8 7.8 8.6 8.1 6.3 8.5 7.2 9.3 11.9 6.7 10.0 8.8
Zero-vehicle Households 2001 13.1 5.4 5.7 12.2 6.1 5.5 5.1 7.6 8.3 2002 12.4 5.1 5.9 11.8 6.3 5.7 4.1 6.7 8.3 2003 12.3 4.9 6.0 10.4 6.4 5.8 3.6 7.5 7.6 2004 12.7 4.6 6.0 6.4 6.4 10.1 6.4 5.8 4.4 6.5 7.9 2005 13.3 5.1 6.2 6.6 6.6 11.0 6.6 6.0 4.6 5.9 7.0 2006 12.5 5.1 6.4 6.5 6.8 11.2 7.9 6.3 4.8 6.0 7.4 2007 13.0 5.6 5.2 6.2 6.2 6.9 10.9 7.6 5.7 4.3 6.6 7.5 2008 12.5 5.6 4.9 7.1 5.8 7.1 11.4 7.9 6.2 4.6 6.2 7.1 2009 13.4 5.8 5.2 6.3 6.1 7.3 6.5 11.8 7.8 6.5 4.8 6.2 7.6 2010 13.1 6.4 5.0 6.7 6.1 7.5 6.7 10.8 8.6 6.5 4.5 6.1 7.7 2011 13.5 6.1 5.3 6.6 6.3 7.8 6.9 12.0 8.7 7.1 5.1 6.4 8.0
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Table B.1 - Continued
Char- Minnea- Pitts- Sacra- Salt Lake San Buffalo Dallas Denver Houston Phoenix Portland St. Louis lotte polis burgh mento City Diego
Median Income 2001 $39.3 $47.8 $50.6 $38.5 $45.9 $49.7 $48.4 $46.8 $45.3 2002 $40.8 $48.4 $52.0 $40.2 $47.2 $51.1 $48.2 $50.4 $45.3 2003 $43.1 $49.6 $53.2 $40.7 $48.3 $52.2 $49.0 $49.9 $46.8 2004 $41.3 $48.6 $53.1 $45.2 $57.8 $40.8 $48.2 $52.1 $48.3 $51.0 $48.9 2005 $42.3 $49.7 $54.9 $46.7 $59.7 $41.7 $49.8 $53.9 $49.7 $56.3 $48.7 2006 $42.8 $52.0 $55.0 $50.3 $62.2 $43.3 $52.5 $57.0 $53.5 $59.6 $49.8 2007 $44.8 $53.2 $54.7 $58.9 $53.0 $63.9 $45.6 $55.4 $59.7 $57.5 $61.8 $52.5 2008 $47.9 $55.0 $56.4 $60.3 $56.3 $65.9 $47.8 $58.8 $61.0 $59.5 $63.0 $53.2 2009 $45.8 $51.3 $54.5 $59.0 $54.1 $63.1 $52.8 $46.3 $55.5 $57.4 $58.0 $60.2 $51.7 2010 $46.4 $50.4 $54.4 $58.7 $53.9 $62.4 $58.5 $46.7 $53.1 $56.2 $57.2 $59.9 $50.9 2011 $47.1 $50.7 $55.5 $59.2 $54.9 $63.4 $50.1 $48.9 $54.9 $55.2 $58.2 $59.5 $51.2
Average Gas Price 2001 1.06 0.98 1.11 1.01 1.11 1.12 0.97 1.12 1.09 2002 0.73 0.66 0.78 0.69 0.80 0.73 0.72 0.73 0.72 2003 1.09 1.03 1.11 1.04 1.01 1.14 0.99 1.14 1.05 2004 1.21 1.11 1.14 1.11 1.22 1.14 1.25 1.24 1.14 1.24 1.16 2005 1.44 1.34 1.39 1.34 1.43 1.38 1.36 1.48 1.35 1.48 1.38 2006 1.95 1.86 1.85 1.86 1.84 1.91 1.76 1.92 1.69 1.92 1.84 2007 1.83 1.70 1.71 1.73 1.71 1.67 1.81 2.16 2.00 1.78 2.00 1.69 2008 2.67 2.54 2.52 2.45 2.52 2.53 2.62 2.60 2.63 2.54 2.63 2.52 2009 1.33 1.26 1.25 1.20 1.25 1.42 1.36 1.30 1.49 1.49 1.13 1.49 1.36 2010 2.30 2.22 2.17 2.14 2.17 2.25 2.24 2.26 2.36 2.47 2.17 2.47 2.20 2011 2.71 2.56 2.53 2.46 2.53 2.68 2.59 2.68 2.69 2.78 2.35 2.78 2.61
Travel Time Index 2001 1.19 1.22 1.31 1.31 1.30 1.23 1.25 1.20 1.27 2002 1.20 1.24 1.29 1.31 1.28 1.23 1.26 1.22 1.27 2003 1.22 1.25 1.29 1.29 1.29 1.25 1.26 1.22 1.25 2004 1.22 1.29 1.29 1.29 1.28 1.31 1.29 1.27 1.23 1.23 1.24 2005 1.22 1.30 1.31 1.31 1.30 1.29 1.30 1.27 1.20 1.23 1.24 2006 1.22 1.32 1.30 1.30 1.28 1.28 1.31 1.27 1.20 1.23 1.22 2007 1.20 1.24 1.31 1.30 1.29 1.27 1.28 1.30 1.26 1.20 1.22 1.20 2008 1.15 1.22 1.25 1.23 1.26 1.22 1.27 1.25 1.20 1.14 1.19 1.17 2009 1.17 1.20 1.24 1.24 1.24 1.19 1.17 1.23 1.25 1.19 1.15 1.17 1.17 2010 1.17 1.20 1.25 1.27 1.26 1.21 1.18 1.24 1.28 1.20 1.14 1.18 1.14 2011 1.17 1.20 1.26 1.27 1.26 1.21 1.18 1.24 1.28 1.20 1.14 1.18 1.14
Char- Minnea- Pitts- Sacra- Salt Lake San Buffalo Dallas Denver Houston Phoenix Portland St. Louis lotte polis burgh mento City Diego
141
APPENDIX C
IRB APPROVAL
142
APPENDIX D
INFORMED CONSENT FORM
143
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BIOGRAPHICAL SKETCH
Michal A. Jaroszynski was born and raised in Warsaw, Poland. He earned his M. Sc.
Eng. degree in Rail Logistics from the Warsaw University of Technology in 2009. In the same year, he joined the doctoral program in Urban Planning at the Florida State University. His areas of academic interests and expertise include public transportation and non-motorized modes, as well as related fields, such as urban economics, finance, regional governance, land-use planning, social equity, and sustainability. He is particularly interested in identifying successful strategies for improving the performance, modal share and economic outcomes of mass transit, especially in the context of rail and multimodal transit systems.
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