Benchmark Rankings for Transit Systems in the United States

Benchmark Rankings for Transit Systems in the United States

December 2004

FDOT BC137- 43

TECHNICAL REPORT STANDARD TITLE PAGE 1. Report No.

NCTR - 527 - 03 FDOT BC137-43

2. Government Accession No.

3. Recipient's Catalog No.

5. Report Date

December 2004 4. Title and Subtitle

Benchmark Rankings for Transit Systems in the United States 6. Performing Organization Code

7. Author(s)

Perk, Victoria; and Kamp, Nilgün 8. Performing Organization Report No.

10. Work Unit No.

9. Performing Organization Name and Address

National Center For Transit Research (NCTR) University of South Florida CUT 100 4202 East Fowler Avenue, Tampa, FL 33620

11. Contract or Grant No.

DTRS98-G-0032 13. Type of Report and Period Covered

12. Sponsoring Agency Name and Address Office of Research and Special Programs (RSPA) U.S. Department of Transportation, Washington, D.C. 20590 Florida Department of Transportation 605 Suwannee Street, MS 26, Tallahassee, FL 32399

14. Sponsoring Agency Code

15. Supplementary Notes

Supported by a Grant from the USDOT Research and Special Programs Administration, and the Florida Department of Transportation 16. Abstract

Benchmarking is a well-established practice among public and private entities that are interested in improving their performance. Benchmarking allows agencies to measure their own performance against other similar agencies, assuming that similar types of data are maintained to allow for meaningful comparisons. The benchmarking practice also allows agencies to measure their own progress over time. Transit agencies do maintain considerable data that are required by the federal government in order to receive federal funds. These data could provide the foundation for meaningful benchmarks of performance to be established, which would help transit agencies realize “where they stand” in the industry. However, at this time there is no effort to utilize these data on a national basis that is universally regarded as constructive by the transit industry. Part of the problem is that transit agencies do operate in different economic and geographic environments with different policy goals, making broad comparisons of system performance misleading.

17. Key Words

public transit, National Transit Database (NTD); transit performance measures; benchmarking; peer analysis

18. Distribution Statement

Available to the public through the National Technical Information Service (NTIS), 5285 Port Royal Road, Springfield, VA 22161, (703) 487-4650, http://www.ntis.gov/, and through the NCTR web site at http://www.nctr.usf.edu/.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified 21. No. of pages

45

22. Price

Form DOT F 1700.7

State of Florida D Pub 605 Tallaha ( P

Plan

National Cen

Center for Urba Univer 4202 E. F Tamp ( Project Staff: Victo Nilgü Meli

The opinions, findings and conclusionauthors and not necessarily those of tof Florida Department of Transporta

epartment of Transportation lic Transit Office

Suwannee Street ssee, FL 32399-0450 850) 414-4500

roject Manager: Tara Bartee

ning Administrator

ter for Transit Research n Transportation Research

sity of South Florida owler Avenue, CUT 100 a, FL 33620-5375

813) 974-3120

ria A. Perk, Senior Research Associate n Kamp, Senior Research Associate

ssa Salzler, Research Assistant

s expressed in this publication are those of the he U.S. Department of Transportation or the State tion.

BENCHMARK RANKINGS OF TRANSIT SYSTEMS IN THE UNITED STATES

Table of Contents

INTRODUCTION............................................................................................................. 1

RELEVANT LITERATURE ............................................................................................. 2

SUMMARY OF INPUT FROM ADVISORY COMMITTEE & OTHERS........................... 7

INITIAL BENCHMARK DEVELOPMENT ..................................................................... 10

Source of Data............................................................................................. 10 Peer Group Selection ................................................................................... 10 Initial Benchmarks ....................................................................................... 12

Scoring Procedure .................................................................................... 13 Results of Initial Benchmarking.................................................................... 14

Southeast (SE) Region............................................................................... 14 Northeast (NE) Region .............................................................................. 19 Midwest (MW) Region ............................................................................... 24 Southwest (SW) Region ............................................................................. 29 Northwest (NW) Region............................................................................. 36 Largest Transit Systems in the U.S............................................................ 39

CONCLUSIONS............................................................................................................ 42

REFERENCES.............................................................................................................. 44

APPENDIX A – LIST OF DEFINITIONS..................................................................... A-1

APPENDIX B – DETAILED DATA TABLES .............................................................. B-1

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List of Tables

TABLE 1: Geographic Groups for Peer Comparisons............................................................11 TABLE 2: Initial Benchmarking Criteria..............................................................................12 TABLE 3: Initial Benchmarking Results: SE Region – Group A............................................14 TABLE 4: Initial Benchmarking Results: SE Region – Group B............................................15 TABLE 5: Initial Benchmarking Results: SE Region – Group C............................................16

TABLE 6: Initial Benchmarking Results: SE Region – Group D ...........................................18 TABLE 7: Initial Benchmarking Results: NE Region – Group A ...........................................19

TABLE 8: Initial Benchmarking Results: NE Region – Group A(1).......................................19

TABLE 9: Initial Benchmarking Results: NE Region – Group B ...........................................20

TABLE 10: Initial Benchmarking Results: NE Region – Group C .........................................21

TABLE 11: Initial Benchmarking Results: NE Region – Group D .........................................23

TABLE 12: Initial Benchmarking Results: MW Region – Group A........................................24

TABLE 13: Initial Benchmarking Results: MW Region – Group A(1) ...................................25

TABLE 14: Initial Benchmarking Results: MW Region – Group B........................................26

TABLE 15: Initial Benchmarking Results: MW Region – Group C........................................27

TABLE 16: Initial Benchmarking Results: MW Region – Group D .......................................28

TABLE 17: Initial Benchmarking Results: SW Region – Group A ........................................30

TABLE 18: Initial Benchmarking Results: SW Region – Group A(1) ....................................30

TABLE 19: Initial Benchmarking Results: SW Region – Group B ........................................31

TABLE 20: Initial Benchmarking Results: SW Region – Group C ........................................32

TABLE 21: Initial Benchmarking Results: SW Region – Group D ........................................33

TABLE 22: Initial Benchmarking Results: SW Region – Group E.........................................35

TABLE 23: Initial Benchmarking Results: NW Region – Group A ........................................36

TABLE 24: Initial Benchmarking Results: NW Region – Group B ........................................37

TABLE 25: Initial Benchmarking Results: NW Region – Group B(1)....................................37

TABLE 26: Initial Benchmarking Results: NW Region – Group C ........................................38

TABLE 27: Initial Benchmarking Results: Large Group – (> 500 Vehs)...............................39

TABLE 28: Initial Benchmarking Results: Large Group – (> 1000 Vehs).............................40

TABLE 29: Initial Benchmarking Results: Large Group – (< 1000 Vehs).............................41

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INTRODUCTION As part of its National Center for Transit Research (NCTR) Program, the Center for Urban Transportation Research (CUTR) has been working with the Florida Department of Transportation (FDOT) to conduct analyses regarding benchmark rankings of U.S. transit systems. Benchmarking is a well-established practice among public and private entities that are interested in improving their performances. Benchmarking allows agencies to measure their own performances against other similar agencies, assuming that similar types of data are maintained to allow for meaningful comparisons. The benchmarking practice also allows agencies to measure their own progress over time. Transit agencies do maintain considerable data that are required by the federal government in order to receive federal funds (the National Transit Database, or NTD). These data could provide the foundation for meaningful benchmarks of performance to be established, which would help transit agencies realize “where they stand” in the industry. However, at this time there is no effort to utilize these data on a national basis that is universally regarded as constructive by the transit industry. Part of the problem is that transit agencies operate in different economic and geographic environments with different policy goals, making broad comparisons of system performance misleading. This study aimed to develop a method of measuring commonly maintained performance statistics in a manner that is broadly acceptable to the transit industry, and thereby provide useful information that helps agencies improve their performance over time. In the course of this study, CUTR reviewed previous research that ranked transit agencies or otherwise studied transit performance measurements. Strengths and weaknesses of these studies were evaluated. Also, this study benefited from guidance in the form of an advisory committee composed of representatives of transit agencies and the transit industry throughout the United States. With assistance from the study advisory committee, CUTR used existing NTD information to develop comparative measures that can allow transit agencies to measure their own performance against relative peers. Suggestions in the selection of an appropriate peer group are provided (e.g., geographical location, agency size, service area population size, etc.). In addition, a variety of categories are established (e.g., service availability, quality of service, cost efficiency, etc.), along with a simple index system, that provide transit agencies with an instant means of comparison for their fixed-route motorbus services. This report first discusses existing literature on the subject of transit performance, and then summarizes input obtained from the study advisory committee and other representatives of the

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transit industry. This information is followed by a description of the selection of peer groups and performance measurement categories developed for this study, along with the results for the fixed-route motorbus service of all transit systems that operate this mode in the United States (as of 2001). Finally, conclusions are presented along with suggestions for future refinements of the process developed herein.

RELEVANT LITERATURE The first task of this study was to examine some of the literature related to transit performance measurement and rankings. This section summarizes that literature. One of the more well-known and controversial reports is Hartgen’s and Horner’s Compa ative Performance of Major U.S. Bus Transit Systems: 1988—1995 (1997). This report uses NTD data, and ranked the performance of the 127 largest urban bus operators in the country. The transit agencies were categorized into six groups using American Public Transportation Association (APTA) definitions for mode and population served. The groups are:

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Multi-modal, serving over 2 million persons Multi-modal, serving between 500,000 and 2 million persons Bus only, serving over 2 million persons Bus only, serving between 500,000 and 2 million persons Bus only, serving between 250,000 and 500,000 persons Bus only, serving less than 250,000 persons.

Twelve measures were used to rank the transit agencies, and were chosen to measure the resources available to operate service, the amount of service provided, and the resulting utilization of the service. Measures commonly used in the transit industry were targeted. To determine system performance, the authors computed the ratio of each system’s statistics to the overall mean of the statistic. This yielded 12 ratios for each operator, for each year. The overall system ratio was then computed as the average of these 12 ratios, and the 127 systems were ranked according to this overall performance ratio, with the best performing systems having the lowest ratios. The systems were also ranked within their own peer groups. Some of the criticism of this study included that there was no industry consensus or agreement on the measures that were used, that inconsistencies in the data exist, and that systems have different goals, regional demographics, and regional economies. The authors do not believe that geographic or size differences affect their results. Their top-rated systems were those with lower-than-average costs and subsidies, low fares, high relative ridership, and area-targeted

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service. The authors did acknowledge that their findings did not shed light on the reasons why apparently similar systems differed so much in performance. Hartgen and Collins also performed a similar analysis in 1996 (Comparative Per ormance of Transit Systems is Valuable but Con roversial), using 108 of the largest urban bus operators and reviewing their performance from 1988 to 1993.

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In the Winter 2002 issue of Transportation Quarterly, Hartgen and Neumann engaged in a Point/Counterpoint Exchange on performance. Key points included the comparison of performance measurements used in the private sector versus the public sector. In the private sector, performance measures are reinforced by clear, quantifiable objectives that tend to be widely agreed upon among firms and their ownership. In transportation agencies in the public sector, some broad agreement has been reached on appropriate measures of performance; however, there is less consensus regarding objectives, many of which are not easily quantified and can tend to vary over time even within the same agency. For example, policies change, political leadership changes, and objectives/measures must reflect these changes. Another key point from the Hartgen/Neumann discussion is that the existence of performance measures alone cannot guarantee effective change or improved performance. To be truly effective, performance measures must be integrated into the ongoing planning, management, and decision-making processes of agencies. Other studies have explored the performance measurement and ranking of transit systems. A Transit Performance Index was developed by APTA (1998), which suggested using NTD information to highlight a set of performance indicators that evaluate resource utilization and transit output, including:

Cost per passenger mile Cost per seat mile Load factor In-service productivity (passenger miles per driver).

APTA’s study explains that these measures can be used directly to compare systems, assess changes over time, and identify potential sources of improvement for a given system. To resolve the difficulty in understanding the relationship among the measures and the inter-city comparisons, a model was constructed to transform the key measures into an index between 0 and 1 (with the best performers near 1 and the worst performers near 0). With this index system, different agencies can be compared. Factors that are unique to cities of various sizes can be identified and accounted for in the index. Rey, Perk, and Thompson conducted annual performance reviews, including trend and peer analyses, over a period of several years for the FDOT. The last complete report was the 2000

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Performance Evaluation of Florida’s Transit Systems (CUTR, 2000). In this study, the performance of each transit system in Florida was compared against itself over a 16-year period and against peer systems for the most recent year complete NTD data were available. Fixed-route and demand-response modes were considered separately. The peer selection process developed by Rey, Perk, and Thompson, was tailored to Florida and therefore was concentrated in the Southeastern U.S. Systems were first selected based on the number of vehicles operated in maximum service, and then further analyzed based on seven additional indicators:

Passenger trips Revenue miles Revenue hours Average speed Total operating expense Service area population Service area population density.

Using a simple scoring system based on the standard deviations from the Florida means for each measure, the peer groups were formed. The peer groups were then analyzed using several NTD-based indicators and measures, divided into three categories: general performance indicators, which consisted of absolute values obtained directly from the NTD; effectiveness measures, which indicate the extent to which various service-related goals are being achieved and are developed from the performance indicators (vehicle miles per capita, passenger trips per revenue mile, etc.); and efficiency measures, which involve reviewing the level of resources (labor or cost) required to achieve a given level of output and are also developed from the performance indicators (operating expense per passenger trip, maintenance expense per revenue mile, farebox recovery, etc.). This study simply presented the transit systems’ data along with the peer group means; the intent was not to explicitly rank systems. The study’s authors indicated that performance evaluation based solely on NTD information will not directly measure several relevant considerations such as passenger satisfaction with regard to levels of service, public attitudes toward the agency, employee morale, quality of planning, contributions to economic development, air quality improvements, or other goals that may be important to an agency. In addition, this type of performance review does not measure several aspects of quality of service such as vehicle cleanliness and comfort, operator courtesy, on-time performance, quality of marketing and passenger information support, and level of satisfaction with hours of operation, frequency of service, and geographic coverage of service. It should be noted that the Transit Cooperative Research Program (TCRP) Report 100, Transit Capacity and Quality of Service Manual, Second Edi ion (Kittelson & Associates, Inc., et. al., 2003) includes a framework t

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designed to measure several of these quality of service characteristics from the transit passenger’s point of view. Chu, Fielding, and Lamar created single measures of efficiency and effectiveness for peer groups (1992). Two peer groups were included, one serving large metropolitan areas and the other serving small cities and large towns. The efficiency measure was a function of annual revenue hours divided by the sum of weighted annual vehicle operating expenses, maintenance expenses, general/administrative expenses, and other expenses. The effectiveness measure was a function of annual unlinked passenger trips divided by the sum of weighted annual revenue hours, urbanized area population density, proportion of households without automobiles, and annual financial assistance per passenger. The results showed that a transit system might be relatively efficient but ineffective, and vice versa. Chu also evaluated six alternative approaches to creating a transit performance index (1996), including:

Average Performance Approach—ranks transit systems based on their average performance across a set of performance dimensions (used by Hartgen, for example).

Best Performance App oach—ranks systems by what they do best, and assumes that a system’s priority objective is the one for which it achieves the most.

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Worst Performance Approach—is the opposite of the Best Performance Approach, and ranks systems with respect to the objective for which they achieve the least.

Stated Objective App oach—ranks systems by their priority objective obtained from a system survey, and addresses the issue of a system not being able to achieve its priority objective due to external constraints.

Stated Priority Approach—ranks systems by a weighted average of their performance across a set of performance dimensions (or categories), with the weights derived from a survey of systems.

Total Performance Approach—ranks systems based on how they rank on individual dimensions, and overcomes the potential problem encountered by the previous methods of results being dominated by dimensions that show wide variation across systems.

Other studies have addressed the issue of transit performance more broadly. For example, accurate estimates of transit service area population are essential if an agency uses measures that incorporate density or per-capita data. Horner and Murray investigated issues of spatial representation of potential transit riders using geographic information systems (GIS). The authors concluded that the best estimates are those obtained suing block-group level data based on the location of transit stops. The Transit Performance Monitoring System (TPMS) was designed to collect data on transit customers through an ongoing, systematic program of on-board surveys (McCollom, et. al.). It

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resulted from an effort by the Federal Transit Administration (FTA) to develop and test a plan for collecting data on transit benefits that could be implemented by transit systems at minimal cost. The TPMS involved 14 standard on-board survey questions, and implementation in conjunction with NTD surveys. Some of the results included the following:

Low-income users represent a larger percentage of users at small transit systems (61%) than in medium (51%) or large (40%) systems.

Work trips are a greater percentage of transit trips in large systems (58%) than in small systems (33%).

There are more choice riders in medium (39%) and large (42%) systems than in small systems (33%).

In terms of policy objectives, mobility is most important in smaller systems. In terms of congestion management, on average, 79% of riders are associated with

this objective (large shares of riders associated with this objective in larger systems and in rail services of multi-modal systems).

The National Cooperative Highway Research Program (NCHRP) Synthesis Report 238 addresses performance measures in state departments of transportation (Poister, 1997). The report notes Washington DOT’s transit mobility index (which was under development), which is intended to be able to track the availability, connectivity, and affordability of both fixed-route and paratransit services in the state’s counties. The report found that, in terms of public transit operating, ridership, and financial statistics, most of the measures tracked by state DOTs are the same as those used by individual agencies. The amount of service provided is typically measured by vehicle miles/hours, or revenue miles/hours. Overall capacity can be measured by the number of seat-miles operated. Labor productivity is often measured by revenue hours per employee (or vehicle operator, or maintenance employee). Vehicle productivity is also sometimes monitored, in terms of measures including vehicle hours/miles per vehicle. Indicators of service quality may include on-time performance, and measures incorporating breakdowns and collisions. Many state DOTs track the number of passenger trips per vehicle hour/mile as a measure of overall transit system productivity. Passenger trips per capita can be a measure of the need for (or importance of) public transportation in a given service area. The cost per passenger trip is a standard measure of cost-effectiveness. Also, most state DOTs compare farebox recovery across transit systems and over time. NCHRP Report 446, A Guidebook for Performance-Based Transportation Planning (Cambridge Systematics, Inc., 2000) provides an ideal summary for this literature review. It indicates that no two performance-based planning processes will be exactly alike. This is because agencies across the country have different organizational needs to satisfy, different types and levels of resources, and unique local and regional issues to consider. These differing needs, resources, and issues affect the environments in which transit agencies operate and make it difficult to

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develop a performance review/ranking process that can reveal the true relative performance of these agencies throughout the country.

SUMMARY OF INPUT FROM ADVISORY COMMITTEE & OTHERS As part of this study, input was gathered from an advisory committee consisting of transit industry representatives from around the United States. In addition, others with relevant experience also provided input. Those willing to share ideas and opinions were informed that the intent of this study is to develop a method of measuring commonly-maintained performance statistics in a manner that is broadly acceptable to the transit industry, and to provide useful information that helps agencies improve their performance over time. It was also noted that this particular study’s scope was limited to NTD data representing the fixed-route motorbus mode. There are several national ranking studies available in various industries. Typically, these studies provide a resource for customers to make more informed decisions. In the case of transit, such a study could target a national audience, the media, the industry policy-makers, or it could target individual agencies either focusing on information for management or the oversight board. Committee members were asked about any thoughts on where the greatest need and prospect for added value would be, and whether trend or peer analyses would be most appropriate. Members of the committee responded that one of the major current issues in transit is the balance between ridership and cost-effective service with a reasonable farebox return. Support was voiced for the use of both trend and peer analyses. It was thought that there needs to be a way for agencies to compare and contrast themselves with one another, and that the study should include several peer groups with a detailed set of measurement criteria. It was noted that the most challenging part of this effort would be in the appropriate selection of peers. One member indicated the importance of benchmarking at the local level so agency management and governing boards can determine where a transit system stands relative to its peers and itself over time. Advisory committee members were also asked if they had any opinions as to whether any study, regardless of methodology, would be embraced by the transit industry. The general consensus is that it will be difficult to get the industry to accept any methodology used for performance measurement. Peer comparison, if done well, is not simple. Previous studies have tended to focus on a single measure or small set of measures, and the committee members do not believe that such studies produce meaningful and useful results. There has always been a

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level of resistance to performance rankings, and the low-ranked agencies, especially, will always argue that the studies are inherently flawed or biased in some manner. According to the committee, the argument that all systems are different is often used, and may be valid, but it is not supported in most other industry comparisons. One member stated that the transit industry should have standards or benchmarks that practitioners and managers can use to identify areas that are strong and areas that are weak. In most cases, transit managers succumb to political pressures to run clearly inefficient service instead of making decisions that will result in optimal efficiency and use of available resources. Without a national or industry standard, transit agencies operate in a vacuum with no comparative accountability for efficient operations. Committee members were asked if there are any types of measures that they believe strongly should be included or excluded. Some members provided a list of measures used at their agency. For example, one member noted that the measures provided do well at explaining the “overall picture,” are easy to understand, and others can see the analogy in other industries (most of the measures provided by this member are included in the methodology presented later in this report). One noted that caution should be used in applying cost-per-employee measures, because different agencies outsource different functions, and comparisons would not be meaningful. Another member noted that, because transit agencies often cannot conduct “business” as a private entity would, it is necessary to evaluate transit agencies against their particular constraints, and keeping in mind other various goals of a public service. For example, farebox recovery might not be the best indicator for agencies required to keep lower fares or operate routes with low ridership. Others cautioned against the aggregation of measures for multi-modal agencies, which could reduce the usefulness of results. When asked their opinions on the number of measures that can be usefully absorbed, responses were varied. Some believed a large number of measures should be used, with the research focused on “tell(ing) the story” rather than just assembling large amounts of data. Others believed the number of measures used should not exceed six to ten. Some committee members expressed reservations about using NTD data for benchmarking in this study. It is clear that some NTD requirements necessitate agencies to compile data differently from their internal accounts. Also, some noted limitations in the accuracy of NTD information, and that some agencies remain inconsistent in their reporting despite experience and the validation process. Committee members were informed that this relatively small study would begin with NTD data, and the results would make apparent any need for revisions or additional types of data that could be explored in further research. Other types of data suggested include cost of living, regional wage rates, and the consumer price index (CPI) to normalize wage and/or benefit information.

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At least one of the committee members was not in favor of using peer comparisons. However, this study examines the establishment of peer groups. In categorizing transit agencies into peer groups, committee members indicated that common variables such as population (actual service area population, as defined by a ¾-mile route buffer), population density, geographic region, agency size (in terms of vehicles operated and/or operating budget), and other fleet information might be used. Modes operated, governance structure, and contracting information might also be incorporated. Other variables not derived from the agency itself such as climate, labor market information, cost of living, and transit-dependency levels could also be appropriate (some of these variables would be at least partially addressed by looking at geographic regions). Overall, committee members advised that any type of benchmarking or peer comparison study, such as this one, should not be considered an end in itself. The data used alone are never sufficient without further explanation and analysis. Results of such efforts are best used as a starting point for fully understanding the performance of a particular agency. They can also be presented to the industry and policy-makers as comparative information on how well their transit dollars are being utilized, and can make transit governing boards more accountable for the decisions they make and the priorities they set. It is hoped that the industry will welcome this effort.

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INITIAL BENCHMARK DEVELOPMENT Based on a review of previous transit performance/ranking analyses, input from the study advisory committee, and the scope of services prepared for this study, CUTR developed an initial transit benchmarking system incorporating several measures from the National Transit Database for all NTD reporters in the U.S. that operate fixed-route motorbus. Initial sets of peer groups were also established. This section of the report outlines how the peer groups were selected, which measures were chosen for inclusion in the benchmarking, and the derivation of the results. It should be noted that the process described herein is not considered to be “finished.” It is anticipated that this process will evolve as future research efforts allow deeper analysis and further refinements.

Source of Data This study is limited to the NTD data. Data were compiled using a relatively new tool called the Florida Transit Information System (FTIS) prepared for FDOT by the Lehman Transportation Center at Florida International University. This software tool contains a database with NTD information dating back to 1984, with 2001 being the most current report year available. While additional, more detailed information is available with this tool for Florida transit systems (such as precalculated measures and geographic information systems, or GIS, capabilities), data compiled directly from the NTD report forms are available for every transit system in the U.S. All data used in this benchmarking study (unless otherwise noted) are report year 2001 NTD data compiled from the latest available version of the FTIS at the time this report was written.

Peer Group Selection According to previous studies, advisory committee input, and CUTR’s past experience with transit performance evaluation, it is clear that the appropriate selection of peers is one of the most important tasks in this benchmarking study, yet it is also one of the most difficult. It is the goal of this study to allow for comparisons among transit systems that have some similarities, rather than compare all transit systems across the country with each other. Factors including climate, population characteristics, and cost of living issues have an effect on the environment in which a transit agency operates. It was beyond the scope of this study to collect detailed data such as that mentioned previously for all transit systems; however, grouping first by geographic region does address some of the issues related to climate, populations, and costs. Table 1 shows how fixed-route transit agencies in different states were initially grouped.

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Table 1: Geographic Groups for Peer Comparisons Southeast Southwest Midwest Northeast Northwest

Alabama Arkansas Florida Georgia Kentucky Louisiana Mississippi North Carolina South Carolina Tennessee Virginia West Virginia

Arizona California Colorado Hawaii Nevada New Mexico Oklahoma Texas Utah

Illinois Indiana Iowa Kansas Michigan Minnesota Missouri Nebraska North Dakota Ohio South Dakota Wisconsin

Connecticut Delaware District of Columbia Maine Maryland Massachusetts New Hampshire New Jersey New York Pennsylvania Rhode Island Vermont

Alaska Idaho Montana Oregon Washington Wyoming

Once placed into their respective geographic groups, the transit systems were further divided based on the following variables, obtainable from the NTD (for the fixed-route motorbus mode):

Service area population Service area population density Total Operating Expense Vehicles Operated in Maximum Service Annual Total Vehicle Miles.

Service area population and density provide information on the size of the area served while operating expense, vehicles operated, and vehicle miles convey information on the size of the system. Operating expense, vehicles operated, and vehicle miles were compiled for fixed-route motorbus service only (directly-operated and purchased services). From the ranges for each of the above variables, a mean and standard deviation were calculated. This information was used to generate a simple scoring system based on whether an agency’s value for a variable was within one standard deviation of the mean, between one and two standard deviations above than the mean, or greater than two standard deviations above the mean. For each transit system a composite score was computed as the sum of the five individual scores for the variables being considered. Then, the mean and standard deviation of the composite scores were calculated to provide break points for the peer groups. In general, for each geographic region, peer groups were determined based on whether the transit systems’ scores were greater than one standard deviation above the mean, above the mean but within one standard deviation, or below the mean. This analysis was performed again on the transit systems that fell below the mean of the original composite scores to break

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them down into two additional peer groups per geographic region. In some instances, closer inspection of the group results indicated that, due to an unusually high score on one of the measures in the selection criteria, an agency needed to be re-categorized into the next group (either above or below). Approximately 10 transit agencies were re-categorized in this way. In some cases, the very largest systems in each geographic region (such as New York City Transit, Chicago Transit Authority, etc.) did not have other comparable systems within their regions. As such, 20 of the largest systems from around the country were also put into a separate group and compared among themselves.

Initial Benchmarks The measures selected for this benchmarking process were determined based on the other studies that were reviewed, input from the advisory committee and others, and CUTR’s past experience conducting similar performance analyses. A total of 37 measures were used, which are listed in Table 2. As Table 2 indicates, some measures are absolute values (revenue miles, passenger trips, etc.), while others are ratios derived from these absolute values (passenger trips per capita, revenue miles per route mile, etc.). Several comparisons were completed, some using all 37 measures and some using just the ratios. The results presented in this report were generated from a composite index of the 23 measures shown in italics in Table 2. Appendix A provides a list of definitions for the measures shown in Table 2. Table 2: Initial Benchmarking Criteria

Service Supply/Availability Service Consumption Quality of Service

Revenue Miles Revenue Hours Vehicle Miles Vehicle Hours Total Operating Expense Revenue Miles per Route Mile Average Weekday Span of Service

Passenger Trips Passenger Trips per CapitaPassenger Trips per Revenue Mile Passenger Trips per Revenue Hour Passenger Miles

Average Speed Number of Vehicle System Failures Revenue Miles Between FailuresNumber of Collisions Revenue Miles Between CollisionsAverage Headway

Cost Efficiency Operating Ratios Vehicle Utilization

Operating Expense per Capita

Vehicle Maintenance Expense Non-Vehicle Maintenance Expense General/Administrative Expense Operating Expense per Revenue Hour Vehicle Maint. Exp. per Operating Exp.Non-Veh. Maint. Exp. per Operating Exp.Gen./Admin. Exp. per Operating Exp.

Farebox RecoveryLocal Revenue per Operating Expense*

Peak Vehicles Total Vehicles Vehicle Miles per Peak Vehicle Vehicle Hours per Peak Vehicle Revenue Miles per Vehicle Mile Revenue Miles per Total VehiclesRevenue Hours per Total VehiclesRevenue Miles per Peak Vehicle Spare Ratio

*System total measure NOTE: Benchmarking results shown later in this report were derived from a composite of the measures shown in italics.

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Scoring Procedure All data used in this analysis were compiled from the FTIS (2001 NTD data) and imported into Microsoft Excel. For each peer group, the mean and standard deviation of the transit agencies’ values for each of the measures shown in Table 2 were computed. Each transit agency received a score for each measure based on the following relatively simple method:

2 points if the agency’s value was greater than 2 standard deviations above the mean. 1.5 points if the agency’s value was between 1 and 2 standard deviations above the

mean. 1 point of the agency’s value was within 1 standard deviation above the mean. 0.75 point if the agency’s value was within 1 standard deviation below the mean. 0.5 point is the agency’s value was between 1 and 2 standard deviations below the

mean. 0 points if the agency’s value was greater than 2 standard deviations below the mean.

By awarding 1 point to an agency whose value for a particular measure was within one standard deviation above the mean, and only 0.75 point if the agency’s value was within one standard deviation below the mean, a slight weight is given to being greater than the average of those in the group. No other weights were introduced to the procedure in this initial phase. Composite scores were calculated for each transit agency, and then the agencies in each group were ranked by their composite scores in descending order. Agencies with higher scores would be considered the better performers in their respective group. Because group means and, subsequently, scores, were calculated separately for each peer group, composite scores cannot be compared outside of a given peer group (that is, Miami-Dade Transit’s composite score should not be compared to Central Ohio Transit Authority’s score, or that for Tallahassee Transit). Composite scores can only be compared within each peer group analysis. Several composite scores were calculated in each peer group. For example, a composite score was calculated using all 37 measures from Table 2, and one was calculated using only the 23 measures in italics from the same table. Results based on the latter score are presented in the next section of this report. In addition, composite scores were computed for each of the six subcategories from Table 2 so that the transit agencies can be compared on the basis of, for instance, service consumption or cost efficiency alone. The composite scores from the subcategories can also be used to help explain a transit agency’s particularly high or low overall composite score. For example, an agency may have a low overall composite score in its peer group, but closer inspection of the subcategory results may show it performs very well in one or two areas, but has relatively worse performance in other areas, thus compressing its overall score. Using the 23 measures, composite scores could have a range between 0 and 46.

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Results of Initial Benchmarking This section of the report details the results of the initial benchmarking process. Results are shown for each of the peer groups developed in each geographic region (southeast, northeast, midwest, southwest, and northwest). In addition, results are shown for a peer group containing the largest transit systems in the U.S. Benchmark rankings are presented using the composite score of the 23 measures shown in italics in Table 2. For each group examined, a brief discussion of the results is included, although further analysis beyond what is revealed by the NTD data is not provided. It should be noted that all information is for the fixed-route motorbus mode. Directly-operated and purchased transportation data were combined for those measures that are reported for purchased transportation service; where noted, data represent directly-operated service only. Appendix B provides detailed data tables for all transit agencies reviewed in this report.

Southeast (SE) Region The Southeast (SE) Region includes a total of 95 transit agencies in the following states:

Alabama Mississippi Arkansas North Carolina Florida South Carolina Georgia Tennessee Kentucky Virginia Louisiana West Virginia

The transit agencies in this region were divided into four separate peer groups, using the methodology discussed previously. Table 3 shows the results for the SE Region Group A. Table 3: Initial Benchmarking Results: SE Region – Group A

Transit Agency Composite Score

Central Florida Regional Transportation Authority (Lynx) – FL 23.25

Regional Transit Authority of Orleans and Jefferson – LA 21.50

Broward County Mass Transit Division – FL 20.50

Miami-Dade Transit – FL 19.25

Metropolitan Atlanta Rapid Transit Authority – GA 19.25

Transportation District Commission of Hampton Roads – VA 15.50

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The composite scores in Table 3 exclude the measure of revenue miles between collisions, because there were no data available in FTIS for the Regional Transit Authority of Orleans and Jefferson for the number of collisions. The most noticeable result in Table 3 is the relatively low score for the Transportation District of Hampton Roads. Further examination of the scores for the subcategories of measures shows this agency to be comparably weak in most areas, but very strong in cost efficiency. The data also show that, based on the peer selection criteria for this study, this agency does belong in this particular peer group. Just for informational purposes, Transportation District of Hampton Roads was reviewed in the SE Region Group B, and it appeared third from the bottom out of 19 transit agencies. Table 4 lists the results of the next group in this region, Group B, which contains 18 agencies. Table 4: Initial Benchmarking Results: SE Region – Group B


Lee County Transit – FL 24.25

City of Kingsport – TN 24.00

Pinellas Suncoast Transit Authority – FL 23.00

Capital Transportation Corporation – LA 23.00

Palm Tran, Inc. – FL 22.50

Research Triangle Regional Public Transportation Authority – NC 22.25

Jacksonville Transportation Authority – FL 22.00

Greater Richmond Transit Company – VA 22.00

City of Alexandria, Alexandria Transit Company – VA 21.75

Charlotte Area Transit System – NC 20.75

Fairfax Connector Bus System – VA 20.50

Transit Authority of Northern Kentucky – KY 20.25

Petersburg Area Transit – VA 19.75

Memphis Area Transit Authority – TN 19.50

Transit Authority of River City – KY 19.25

Metropolitan Transit Authority – TN 19.25

Hillsborough Area Regional Transit Authority – FL 18.50

Birmingham-Jefferson County Transit Authority – AL 15.00

Lee County Transit and the City of Kingsport are the best performers in this peer group, despite the fact that these two transit agencies are fourth-last and second-last, respectively, in the subcategory of service consumption, and they are second-last and last, respectively, in the subcategory of operating ratios (which includes farebox recovery and local revenue as a percent of operating expense). However, Lee County and Kingsport perform well in every other subcategory, with Lee County being the best in terms of vehicle utilization, and third-best in

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cost efficiency, and Kingsport being the best in service supply and cost efficiency. The agencies with the lowest scores in Table 4 are Hillsborough Area Regional Transit Authority and Birmingham-Jefferson County Transit Authority. In all subcategories, Hillsborough ranks in the middle to lower part of the group. Birmingham-Jefferson is last in three subcategories (service supply, quality of service, and vehicle utilization). Closer inspection of the peer selection criteria used in this study confirms that Birmingham-Jefferson should be in this group. Table 5 shows results from the next group, SE Region Group C, which contains 27 agencies. Table 5: Initial Benchmarking Results: SE Region – Group C


Durham Area Transit Authority – NC 24.00

Albany Transit System – GA 23.25

Charleston Area Regional Transportation – SC 22.75

Shreveport Area Transit System – LA 22.75

South Carolina Electric & Gas Company-Columbia – SC 22.50

Chatham Area Transit Authority – GA 21.75

Central Arkansas Transit Authority – AR 21.50

Gainesville Regional Transit System – FL 21.25

Chapel Hill Transit – NC 21.00

Kanawha Valley Regional Transportation Authority – WV 21.00

Sarasota County Transportation Authority – FL 20.75

Transit Authority Lexington- Fayette Urban County Government – KY 20.75

Jefferson Parish Department of Transit Administration – LA 20.25

Winston-Salem Transit Authority - Trans-Aid of Forsyth County – NC 20.00

Greensboro Transit Authority – NC 19.50

Knoxville Transportation Authority – TN 19.50

Capital Area Transit – NC 19.25

Cobb Community Transit – GA 19.00

County of Volusia dba: VOTRAN – FL 19.00

City of Tallahassee-TALTRAN – FL 18.75

Metro Transit – AL 18.75

Augusta Richmond Co. Transit Dept – GA 18.50

Escambia County Area Transit – FL 18.50

Greater Roanoke Transit Company – VA 18.50

Chattanooga Area Regional Transportation Authority – TN 17.75

Potomac and Rappahannock Transportation Commission – VA 17.25

Space Coast Area Transit – FL 17.00

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The composite scores listed in Table 5 do not include the measure of revenue miles between vehicle system failures. Six of the transit agencies in this peer group operate purchased motorbus services only, and so failures are not reported in the NTD (these six agencies are: Charleston Area Regional Transportation, Cobb Community Transit, Greensboro Transit Authority, Jefferson Parish Department of Transit Administration, Potomac and Rappahannock Transportation Commission, and South Carolina Electric & Gas Company-Columbia). The two best performers in SE Region Group C are Durham Area Transit Authority and Albany Transit System. Despite ranking second-last in the subcategory of quality of service, Durham performs well in the other areas, ranking fourth-best in cost efficiency and best in vehicle utilization. Albany was strong in the subcategories of cost efficiency and the operating ratios, placing third-best in each, and was tied for best in the subcategory of vehicle utilization with Durham. Potomac and Rappahannock Transportation Commission and Space Coast Area Transit placed at the bottom of this peer group. Potomac and Rappahannock ranked high in the subcategories of quality of service and the operating ratios, but ranked low in the subcategories of service consumption and cost efficiency (placing last in the latter subcategory). Space Coast appeared in the middle of the group in most subcategories but placed last in service consumption and second-last in vehicle utilization. Space Coast does have a unique service area geography that may help explain its performance in this peer group. However, when Space Coast was placed in the next peer group, Group D, it ranked 40th out of 45 transit agencies. Table 6, on the following page, shows the results of the initial benchmarking within the SE Region Group D, the last group in this region. Group D contains 44 transit agencies. The best performers in this group are the City of Fairfax CUE Bus and Okaloosa County Transit. Those ranked near the bottom of this group are Polk County Transit Services, Lee-Russell Council of Governments, and Indian River County. It must be noted that the composite scores for this group do not include measures incorporating failures or collisions, due to a lack of data for several transit agencies in this group. The City of Fairfax performs well in most of the subcategories, and is best in service supply. However, Fairfax did rank near the bottom of the group for the subcategory of quality of service (which, in this group, only contains the measures for average speed and average headway). Okaloosa County performs best in the group for cost efficiency, and is ranked near the top for the operating ratios. However, Okaloosa County ranked in the middle of the group for most of the other subcategories, and was second-last in service consumption. Polk County, Lee-Russell, and Indian River County were the three transit agencies with the lowest composite scores in this group. Polk ranked near the bottom in most subcategories but was near the top in terms of cost efficiency. Lee-Russell’s strongest subcategories were quality

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of service and cost efficiency, where it ranked near the top of the group. Indian River was sixth-best in cost efficiency, but ranked near the bottom of the group in most other subcategories, placing last in terms of service supply, and second-last in quality of service. Table 6: Initial Benchmarking Results: SE Region – Group D

Transit Agency Composite

Score Transit Agency

Composite Score

City of Fairfax CUE Bus – VA 23.25 Greenville Transit Authority – SC 19.00

Okaloosa County – FL 22.00 Athens Transit System – GA 18.75

Clarksville Transit System – TN 21.75 City of Rome Transit Department – GA 18.75

Department of Transportation/METRA – GA

20.75 Loudoun County Commuter Bus Service – VA

18.75

Jackson Transit Authority – TN 20.75 High Point Transit – NC 18.50

Wilmington Transit Authority – NC 20.75 Johnson City Transit System – TN 18.50

Lafayette Transit System LTS – LA 20.50 City of Alexandria – LA 18.25

Lakeland Area Mass Transit District – FL 20.50 City of Danville Mass Transit System – VA

18.25

Pasco County Public Transportation – FL 20.50 Fayetteville Area System of Transit – NC

18.25

SunTran – FL 20.50 Mid-Ohio Valley Transit Authority – WV 18.25

Bay County Council On Aging - Bay Coordinated Transportation – FL

20.25 Tuscaloosa County Parking and Transit Authority – AL

18.25

City of Key West - Department of Transportation – FL

20.25 City of Jackson Transit System – MS 18.00

Manatee County Area Transit – FL 20.25 City of Monroe Transit System – LA 18.00

Mississippi Coast Transportation Authority – MS

20.25 Terrebonne Parish Consolidated Government – LA

18.00

City of Huntsville Department of Parking & Public Transit – AL

19.75 The Tri-State Transit Authority – WV 18.00

James City County Transit – VA 19.75 Pee Dee Regional Transportation Authority – SC

17.50

University of Arkansas, Fayetteville/Razorback Transit – AR

19.75 Santee Wateree Regional Transportation Authority – SC

17.50

Charlottesville Transit Service – VA 19.50 CRPTA - Waccamaw Regional Transportation Authority – SC

17.00

Greater Lynchburg Transit Company – VA 19.50 Montgomery Area Transit System – AL 17.00

Ohio Valley Regional Transportation Authority – WV

19.50 Polk County Transit Services Division – FL

16.75

Spartanburg Transit System – SC 19.50 Lee-Russell Council of Governments – AL

16.50

Asheville Transit Authority – NC 19.25 Indian River County Council on Aging, Inc. – FL

16.00

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Northeast (NE) Region In this analysis, the Northeast (NE) Region includes a total of 107 transit agencies in the following states:

Connecticut New Hampshire Delaware New Jersey District of Columbia New York Maine Pennsylvania Maryland Rhode Island Massachusetts Vermont

The transit agencies in this region were divided into four separate peer groups, using the methodology discussed previously. Table 7 shows the results for the NE Region Group A. Table 7: Initial Benchmarking Results: NE Region – Group A


New York City Transit – NY 25.25

Southeastern Pennsylvania Transportation Authority – PA 22.25

New Jersey Transit Corporation (Consolidated) – NJ 22.25

Massachusetts Bay Transportation Authority – MA 21.00

Port Authority of Allegheny County – PA 19.75

Washington Metropolitan Area Transit Authority – DC 19.50

GTJC – NY 19.50

Maryland Transit Administration – MD 18.75

Because New York City Transit is so much larger than the other systems in this peer group (as well as the entire country), this group was analyzed without it; results are shown in Table 8. Table 8: Initial Benchmarking Results: NE Region – Group A(1)




GTJC – NY 21.00




Maryland Transit Administration – MD 18.50

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It is clear from Tables 7 and 8 that the results are not significantly different when New York City Transit is excluded from the analysis. The best performer in the group is the Southeastern Pennsylvania Transportation Authority (SEPTA), and the agency with the lowest composite score is the Maryland Transit Administration (MTA). In terms of the measures used in this study, New York City Transit is strong in all subcategories except cost efficiency. SEPTA is strong in vehicle utilization, and MTA’s low rank in quality of service and vehicle utilization accounted for its relatively low composite score in both Tables 7 and 8. Table 9 presents the results of NE Region Group B, which consists of 14 transit agencies. Despite ranking last in the subcategory of service consumption, Suburban Transit Corporation is first overall in this group, largely due to ranking best in terms of cost efficiency and vehicle utilization. Delaware Transit Corporation and Queens Surface Transportation had relatively low scores in this group. Delaware Transit appeared in the middle of the group for most subcategories, but finished last in quality of service and the operating ratios. Queens Surface Transportation ranked second-best in terms of service consumption, but ranked last in the group in terms of service supply and cost efficiency. Table 9: Initial Benchmarking Results: NE Region – Group B


Suburban Transit Corporation – NJ 26.00

Academy Lines, Inc. – NJ 23.50

Ride-On Montgomery County Government – MD 23.50

Hudson Transit Lines, Inc. – NJ 23.25

Metropolitan Suburban Bus Authority dba MTA Long Island Bus – NY 22.00

Liberty Lines Transit, Inc. – NY 21.75

New York Bus Tours, Inc., dba New York Bus Service – NY 21.00

Connecticut Transit-Hartford Division – CT 21.00

Niagara Frontier Transit Metro System, Inc. – NY 20.75

Capital District Transportation Authority – NY 20.50

Regional Transit Service, Inc. and Lift Line, Inc. – NY 20.00

Rhode Island Public Transit Authority – RI 20.00

Delaware Transit Corporation – DE 18.00

Queens Surface Corporation – NY 17.75

On the following page, the results of NE Region Group C are shown in Table 10. This peer group contains a total of 25 transit agencies.

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Table 10: Initial Benchmarking Results: NE Region – Group C Transit Agency Composite Score

Olympia Trails Bus Co., Inc. c/o NJ Transit, Carrier Admin. – NJ 25.25

Trans-Bridge Lines, Inc. c/o NJ Transit, Carrier Admin. – NJ 22.50

Trans-Hudson Express c/o NJ Transit, Carrier Admin. – NJ 22.00

Private Transportation – NY 20.00

City of Long Beach – NY 19.75

Worcester Regional Transit Authority – MA 19.50

York County Transportation Authority – PA 19.25

Pioneer Valley Transit Authority – MA 19.25

Greater Bridgeport Transit Authority – CT 19.25

Rockland Coaches, Inc. – NJ 19.25

Connecticut Transit-New Haven Division – CT 19.00

CNY Centro, Inc. – NY 19.00

Lakeland Bus Lines, Inc. c/o NJ Transit, Carrier Admin. – NJ 19.00

Red Rose Transit Authority – PA 19.00

Transit Express – MA 18.75

New Jersey Transit Corporation – NJ 18.75

Transport of Rockland – NY 18.75

Metro-North Commuter Railroad Co. - -NY 18.75

Private One of New York LLC, d.b.a. New York Airport Service – NY 18.25

Liberty Lines Express, Inc. – NY 18.00

Southeastern Regional Transit Authority – MA 17.75

Lehigh and Northampton Transportation Authority – PA 17.50

DeCamp Bus Lines c/o NJ Transit, Carrier Admin. – NJ 17.50

Westchester County Department of Transportation – NY 16.75

Cumberland-Dauphin-Harrisburg Transit Authority – PA 16.50

Due to several transit agencies not having complete NTD data for vehicle failures and/or collisions, the composite scores in Table 10 do not include the measures of revenue miles between failures and revenue miles between collisions. Olympia Trails Bus Co., Trans-Bridge Lines, and Trans-Hudson Express, all part of NJ Transit, are the best performers in this peer group. Olympia Trails had the highest score, and is ranked best in terms of service supply and vehicle utilization, but is not as strong in terms of service consumption and cost efficiency. Trans-Bridge lines ranked best in quality of service, while Trans-Hudson ranked best in service consumption.

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Westchester County Department of Transportation and Cumberland-Dauphin-Harrisburg Transit Authority had the lowest scores in NE Region Group C. Further examination of the subcategory scores can provide insight into these results. Westchester ranked last in the group in terms of quality of service and cost efficiency, but ranked best in the operating ratios and was strong in terms of vehicle utilization. Cumberland-Dauphin-Harrisburg ranked in the middle to lower part of the group in each of the subcategories of measures. Table 11, on the following page, presents the initial benchmarking results of the last peer group in the NE Region, which is Group D. This peer group, one of the largest in this report, contains 60 mostly small and medium-sized transit agencies in the NE Region. Once again, because of a lack of data on failures and collisions for several systems in this group, composite scores listed in Table 11 do not include the measures of revenue miles between failures and revenue miles between collisions. The best three performers in this peer group are Orange-Newark-Elizabeth, Inc. and South Orange Avenue IBOA, both affiliated with NJ Transit, and Putnam County Transit. Both Orange-Newark-Elizabeth and South Orange Avenue IBOA ranked near the top of the group in all subcategories except cost efficiency, where the ranked near the bottom (South Orange Avenue actually ranked last). The three transit agencies with the lowest scores in Table 11 are Western Maine Transportation Services, Inc., G G & C Bus Company, Inc., and Cape Ann Transportation Authority. Western Maine ranked in the middle to lower part of the group for most all subcategories, ranking lowest in cost efficiency. Similarly, G G & C and Cape Ann both ranked in the lower part of the group in most subcategories. G G & C ranked last in quality of service and Cape Ann ranked last in vehicle utilization.

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Table 11: Initial Benchmarking Results: NE Region – Group D

Transit Agency Composite Score Transit Agency Composite

Score

Orange-Newark-Elizabeth, Inc. c/o NJ Transit, Carrier Admin. – NJ

26.50 City of Nashua Nashua Transit System – NH

18.75

Putnam County Transit – NY 24.50 New Britain Transp. Co. Inc. (1047)–CT 18.75

South Orange Avenue IBOA c/o NJ Transit, Carrier Admin. – NJ

24.00 Middletown Transit District – CT 18.75

Clarkstown Mini-Trans – NY 23.25 Berks Area Reading Transp. Auth. – PA 18.50

Howard Transit c/o Corridor Transp – MD 23.25 Village of Spring Valley Bus – NY 18.50

Community Transit, Inc. c/o NJ Transit, Carrier Admin. – NJ

22.25 Norwalk Transit District – CT 18.25

Greater Glens Falls Transit System – NY 22.00 Centro of Cayuga, Inc. – NY 18.25

Lafeyette-Greenville IBOA c/o NJ Transit, Carrier Admin. – NJ

22.00 Southeast Area Transit – CT 18.25

Connecticut Transit-Stamford Div. -- CT 21.25 TransIT Svcs of Frederick Count – MD 18.25

Centre Area Transp. Authority – PA 21.00 Merrimack Valley Reg Transit Auth–MA 18.00

Monsey New Square Trails Corp. – NY 21.00 T-Tran – NY 18.00

Dutchess Cty. Div. of Mass Transp – NY 20.75 Huntington Area Rapid Transit – NY 18.00

Greater Hartford Transit District – CT 20.50 Manchester Transit Authority – NH 18.00

Milford Transit District – CT 20.50 Beaver County Transit Authority – PA 18.00

Greater Attleboro-Taunton Regional Transit Authority – MA

20.25 Centro of Oswego, Inc. – NY 18.00

Chemung County Transit System – NY 20.25 Montachusett Reg Transit Auth – MA 18.00

Leisure Line c/o NJT, Carrier Admin. – NJ 20.25 Connecticut Dept. of Transp. – CT 17.75

Greater Portland Transit District – ME 20.25 Utica Transit Authority – NY 17.75

Tompkins Consolidated Area Transit – NY 20.00 Cty of Lackawanna Trans. System – PA 17.75

Harford County Transp. Services – MD 19.75 Mid Mon Valley Transit Authority 17.75

Broome Cty Dept. of Public Transp. – NY 19.50 Northeast Transp. Company, Inc. – CT 17.50

Chittenden County Transp. Auth. – VT 19.50 Washington County Transp Dept – MD 17.25

Luzerne County Transp. Auth. – PA 19.25 Cambria County Transit Authority – PA 17.25

Williamsport Bureau of Transp. – PA 19.25 Lowell Regional Transit Authority – MA 17.00

City of Bangor – ME 19.25 New Britain Transp. Co. Inc. (1052)–CT 17.00

Housatonic Area Regional Transit – CT 19.25 Altoona Metro Transit – PA 16.00

Erie Metropolitan Transit Authority – PA 19.00 Cape Cod Regional Transit Auth. – MA 16.00

Westmoreland County Transit Auth. – PA 19.00 Western Maine Transp Svcs, Inc. – ME 15.50

Brockton Area Transit Authority – MA 18.75 G G & C Bus Company, Inc. – PA 14.75

Annapolis Department of Transp. – MD 18.75 Cape Ann Transp. Auth – MA 14.75

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Midwest (MW) Region For this benchmarking analysis, the Midwest (MW) Region includes a total of 105 transit agencies in the following states:

Illinois Missouri Indiana Nebraska Iowa North Dakota Kansas Ohio Michigan South Dakota Minnesota Wisconsin

Following the peer selection methodology discussed previously, the transit agencies in this region were divided into four separate peer groups. Tables 12 and 13 show the results for the MW Region Group A, which contains 11 of the largest transit agencies in the region. Due to its size in comparison with the other transit agencies in this region, Chicago Transit Authority is included in Table 12, but is not included in the results in Table 13 (on the following page). Table 12: Initial Benchmarking Results: MW Region – Group A


Chicago Transit Authority – IL 27.75

Milwaukee County Transit System – WI 23.50

Pace, Suburban Bus Division – IL 23.25

Suburban Mobility Authority for Regional Transportation – MI 22.25

City of Detroit Department of Transportation – MI 21.50

The Greater Cleveland Regional Transit Authority – OH 20.00

Bi-State Development Agency – MO 19.75

Metro Transit – MN 19.50

Southwest Ohio Regional Transit Authority – OH 19.25

Kansas City Area Transportation Authority – MO 19.25

Central Ohio Transit Authority – OH 18.50

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Table 13: Initial Benchmarking Results: MW Region – Group A(1)


Milwaukee County Transit System – WI 27.50


City of Detroit Department of Transportation – MI 22.50

Suburban Mobility Authority for Regional Transportation – MI 21.75



Bi-State Development Agency – MO 20.00

Southwest Ohio Regional Transit Authority – OH 19.25

Kansas City Area Transportation Authority – MO 19.25

Central Ohio Transit Authority – OH 18.75

Table 12 shows Chicago Transit Authority to be the best performer in the group, which might be expected given its size relative to the other agencies in the group. Chicago performed well in all subcategories, with its lowest ranking in quality of service (still in the middle of the group). Milwaukee County Transit System is the best performer when Chicago Transit Authority is not considered. Upon closer examination of the subcategories of performance measures, Milwaukee ranked high in terms of service consumption, cost efficiency, and vehicle utilization, but ranked lower in terms of quality of service. Southwest Ohio Regional Transit Authority, Kansas City Area Transportation Authority, and Central Ohio Transit Authority had the lowest overall composite scores in this group. While Kansas City and Central Ohio are two of the smaller transit agencies in this group, it should be noted that Suburban Mobility Authority for Regional Transportation is similarly sized, but ranked much higher. Southwest Ohio was very strong in terms of cost efficiency, but ranked last in vehicle utilization. Kansas City ranked in the middle to lower part of the group for most subcategories but was very strong in terms of quality of service. Central Ohio ranked in the middle to lower part of the group in all subcategories, but did not have one or more areas in which it performed well enough to boost its score. Table 14, on the following page, presents the results of the next group in this region, MW Region Group B. This group consists of 10 transit agencies. It should be noted that the composite scores shown in Table 14 do not include the measure of revenue miles between failures, because the Metropolitan Council is a purchased-transportation service and failures are not reported in the NTD.

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Table 14: Initial Benchmarking Results: MW Region – Group B


Indianapolis Public Transportation Corporation – IN 22.50

Miami Valley Regional Transit Authority – OH 22.00

Capital Area Transportation Authority – MI 21.75

Madison Metro Transit – WI 20.25

Mass Transportation Authority – MI 20.25

Interurban Transit Partnership – MI 20.00

Transit Authority of Omaha – NE 19.50

Metropolitan Council – MN 19.50

Metro Regional Transit Authority – OH 19.00

Toledo Area Regional Transit Authority – OH 18.50

As shown in Table 14, Indianapolis Public Transportation Corporation is the best performer in this group, based on the analysis used in this report, while Toledo Area Regional Transit Authority has the lowest composite score. Interestingly, both Indianapolis and Toledo are in the middle of the group in terms of service consumption and cost efficiency. However, Indianapolis ranked second-best in terms of both service supply and vehicle utilization. Toledo ranked in the middle to lower part of the group in most subcategories of measures. The next group in this region, MW Region Group C, contains 30 transit agencies. Its results are listed in Table 15 on the following page. Due to a lack of data on vehicle system failures for several transit agencies in this group, the composite scores in Table 15 do not include the measure of revenue miles between failures. According to Table 15, University of Michigan Parking and Transportation Services and Stark Area Regional Transit Authority have the highest overall composite scores in this group. University of Michigan ranked best in the subcategories of service supply and service consumption, and was also strong in cost efficiency. Stark ranked best in terms of vehicle utilization, and ranked well in the other subcategories except service consumption, where it was one of four transit agencies with the lowest score in the group. Five Seasons Transportation and Springfield Mass Transit District have the lowest composite scores in this group, as shown in Table 15. Both transit agencies ranked in the middle to lowest parts of the group in all subcategories of measures.

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Table 15: Initial Benchmarking Results: MW Region – Group C Transit Agency Composite Score

University of Michigan Parking and Transportation Services – MI 26.25

Stark Area Regional Transit Authority – OH 24.00

Belle Urban System-Racine – WI 23.25

Southwest Missouri State University – MO 23.25

Lorain County Transit – OH 23.00

Champaign-Urbana Mass Transit District – IL 22.25

LaCrosse Municipal Transit Utility – WI 21.25

Madison County Transit District –IL 21.00

Butler County Regional Transit Authority – OH 21.00

Wichita Transit – KS 20.25

Campus Bus Service – OH 20.25

Pekin Municipal Bus Service – IL 20.25

South Bend Public Transportation Corporation – IN 20.00

Greater Peoria Mass Transit District – IL 19.75

Bloomington Public Transportation Corporation – IN 19.75

Kalamazoo Metro Transit System – MI 19.50

Ann Arbor Transportation Authority – MI 19.25

Waukesha County Transit System – WI 19.25

Gary Public Transportation Corporation – IN 19.25

Greater Lafayette Public Transportation Corporation – IN 19.00

Muncie Indiana Transit System – IN 19.00

Duluth Transit Authority – MN 18.75

Western Reserve Transit Authority – OH 18.75

Rock Island County Metropolitan Mass Transit District – IL 18.75

StarTran – NE 18.75

Kenosha Transit – WI 18.25

Des Moines Metropolitan Transit Authority – IA 18.00

Green Bay Metro – WI 17.75

Five Seasons Transportation – IA 17.25

Springfield Mass Transit District – IL 16.75

On the following page, Table 16 presents the results for the 54 transit agencies in MW Region Group D, the last group in this region. Group D represents most of the smaller transit agencies in this region. With several agencies lacking NTD data for vehicle failures and/or collisions, the composite scores in Table 16 do not include measures derived from these two variables.

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Table 16: Initial Benchmarking Results: MW Region – Group D


Score

Heart City Rider/Goshen Transit – IN 24.00 City of Holland Macatawa Area Express – MI

19.00

University of Iowa – IA 22.75 Ozaukee County Transit Services – WI 19.00

Brunswick Transit Alternative – OH 22.75 City of Appleton-Valley Transit – WI 18.75

City Utilities of Springfield Transit Services – MO

22.50 City of Newark Transit Operations – OH 18.75

Eau Claire Transit System – WI 21.75 Columbia Transit System – MO 18.75

City of Anderson Transportation System – IN

21.50 Sioux Falls Transit – SD 18.50

Rockford Mass Transit District – IL 21.00 Sheboygan Transit System – WI 18.50

Iowa City Transit – IA 21.00 Johnson County Transit – KS 18.00

Lawrence Transit System – KS 20.75 Richland County Transit – OH 18.00

Metropolitan Evansville Transit System – IN

20.50 Blue Water Area Transportation Commission – MI

18.00

Oshkosh Transit System – WI 20.50 Janesville Transit System – WI 18.00

Rapid Transit System – SD 20.50 LAKETRAN – OH 17.75

Fort Wayne Public Transportation Corporation – IN

20.25 Twin Cities Area Transportation Authority – MI

17.75

City of Rochester, Minnesota – MN 20.25 Hammond Transit System – IN 17.25

St. Cloud Metropolitan Transit Commission – MN

20.25 Decatur Public Transit System – IL 17.25

City of Jackson Transportation Authority – MI

20.00 Wausau Area Transit System – WI 17.25

Davenport Public Transit – IA 19.75 Springfield City Area Transit – OH 17.00

Fargo Metropolitan Area Transit – ND 19.75 Saginaw Transit Authority Regional – MI

16.75

Bloomington-Normal Public Transit System – IL

19.50 Portage Area Regional Transportation Authority – OH

16.75

Bettendorf Transit System – IA 19.50 East Chicago Transit – IN 16.75

Grand Forks City Bus – ND 19.50 Battle Creek Transit – MI 16.75

Sioux City Transit System – IA 19.50 City of Beloit Transit System – WI 16.75

Topeka Metropolitan Transit Authority – KS

19.25 City of Moorhead-Transit – MN 16.75

Waukesha Transit Commission – WI 19.25 Muskegon Area Transit System – MI 16.50

Metropolitan Transit Authority of Black Hawk County – IA

19.25 Terre Haute Transit Utility – IN 16.00

St. Joseph Transit – MO 19.25 Bay Metropolitan Transportation Authority – MI

15.75

Middletown Transit System – OH 19.00 City of Dubuque-KeyLine – IA 15.25

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Further examination of the subcategory scores can be used to help explain the results shown in Table 16. Heart City Rider/Goshen Transit, University of Iowa, and Brunswick Transit Alternative had the highest composite scores in this group. Heart City Rider performed best in terms of cost efficiency and vehicle utilization, but ranked low in terms of service consumption. University of Iowa ranked best in service supply and service consumption, but ranked lowest in terms of the operating ratios, mainly as a result of a low farebox recovery ratio, which can be expected for a university transit system that often has low or free fares (University of Iowa reported low passenger fare revenues). Brunswick Transit Alternative ranked low in terms of service supply and service consumption, but ranked very high in terms of quality of service, operating ratios, and vehicle utilization. Bay Metropolitan Transportation Authority and City of Dubuque—Key Line had the lowest composite scores in this group. Both transit agencies ranked in the middle to lower part of the group in all subcategories of measures, but ranked lowest in terms of service supply.

Southwest (SW) Region The Southwest (SW) Region includes a total of 110 transit agencies in the following states:

Arizona New Mexico California Oklahoma Colorado Texas Hawaii Utah Nevada

Using the peer selection methodology discussed previously, the transit agencies in this region were divided into five separate peer groups. Table 17, on the following page, shows the results for the SW Region Group A, which contains 9 of the largest transit agencies in the region, including Los Angeles County Metropolitan Transportation Authority (LACMTA). Due to its size in comparison with the other transit agencies in this group, LACMTA is included in Table 17, but is not included in the results in Table 18 (on the following page).

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Table 17: Initial Benchmarking Results: SW Region – Group A Transit Agency Composite Score

San Francisco Municipal Railway – CA 25.00

City and County of Honolulu Dept of Transportation Services – HI 23.50

Orange County Transportation Authority – CA 23.25

Metropolitan Transit Authority of Harris County – TX 21.50

Regional Transportation District – CO 21.50

Alameda-Contra Costa Transit District – CA 21.50

Los Angeles County Metropolitan Transportation Authority – CA 21.00

Santa Clara Valley Transportation Authority – CA 19.00

Dallas Area Rapid Transit Authority – TX 18.75

Table 18: Initial Benchmarking Results: SW Region – Group A(1)


San Francisco Municipal Railway – CA 25.50

Orange County Transportation Authority – CA 22.75

City and County of Honolulu Dept of Transportation Services – HI 22.75




Dallas Area Rapid Transit Authority – TX 19.50

Santa Clara Valley Transportation Authority – CA 18.50

Both Table 17 and Table 18 show San Francisco Municipal Railway (which directly-operates motorbus services), to be the best performer in this group. Both tables also indicate that Dallas Area Rapid Transit Authority and Santa Clara Valley Transportation Authority have the overall lowest composite scores in this peer group. From closer inspection of the results in Table 17, LACMTA ranked high in the subcategories of service supply and service consumption, as well as the operating ratios. However, LACMTA ranked lower in terms of quality of service. San Francisco Municipal Railway, upon examination of the subcategory measures, ranked the best in the group in terms of service supply and service consumption, and was strong in vehicle utilization. Dallas and Santa Clara ranked in the middle to lower part of the group for most of the subcategory measures; however, Dallas ranked the best in the group in terms of the

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operating ratios. Both Dallas and Santa Clara ranked solidly in the middle of the peer group in terms of vehicle utilization. Table 19 below presents the initial benchmarking results of the next group in this region, SW Region Group B. This group consists of 8 transit agencies from this region. Table 19: Initial Benchmarking Results: SW Region – Group B


RTC of Southern Nevada – NV 25.25

San Diego Transit Corporation – CA 21.25

First Transit, Inc. (6057) – TX 20.50

VIA Metropolitan Transit – TX 20.50

Capital Metropolitan Transportation Authority – TX 19.75

City of Los Angeles Department of Transportation – CA 19.25

City of Phoenix Public Transit Department – AZ 18.50

San Mateo County Transit District – CA 16.50

The scores in Table 19 show that RTC of Southern Nevada is the strongest performer in this group, while San Mateo County Transit District has the lowest overall score. Further examination of the subcategory scores shows that both RTC and San Mateo have the same scores for service supply, placing them in the middle of the group; however, RTC ranks near the top in terms of service consumption while San Mateo ranks near the bottom. Also, RTC’s high overall composite score was due, in part, to its very high score in the subcategory of vehicle utilization. RTC ranked best in the group in terms of vehicle utilization, while San Mateo ranked the lowest. The next group in this region is SW Region Group C. Results for this group are shown on the following page in Table 20. SW Region Group C includes 18 transit agencies. As shown in the table, there is more than one transit system operated by Laidlaw Transit Services, Inc., and First Transit, Inc. These transit systems are differentiated by their NTD identification numbers, which are the four-digit numbers in parentheses. In addition, the Los Angeles Metropolitan Transportation Authority in this peer group represents the purchased motorbus services operated for LACMTA under a separate NTD identification number. It should also be noted that the composite scores shown in Table 20 do not include the measure of revenue miles between failures, due to a lack of data on vehicle system failures for several transit agencies in this group.

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Laidlaw Transit Services, Inc. (9178), which operates in El Monte, California, ranked the best in this group. Laidlaw (9178) ranked the best in the subcategories of quality of service and the operating ratios, and was strong in the other subcategories. Another system operated by Laidlaw Transit Services, Inc., (NTD #9158) which operates in Santa Ana, California, had one of the lowest scores in this group. This system ranked in the middle to lower part of the group in most subcategories but ranked the best in the group in terms of cost efficiency. Finally, Golden Gate Bridge Highway and Transportation Authority and Fort Worth Transportation Authority had the two lowest composite scores in the group. Golden Gate did rank near the top in terms of service supply and quality of service. Fort Worth ranked in the lower part of the peer group in most of the subcategories of measures. Table 20: Initial Benchmarking Results: SW Region – Group C


Laidlaw Transit Services Inc. (9178) – CA 25.25

Long Beach Public Transportation Company – CA 22.50

Santa Monica's Big Blue Bus – CA 22.25

Mass Transit Department-City of El Paso – TX 22.00

North San Diego County Transit Development Board – CA 22.00

San Diego Metropolitan Transit Development Board – CA 21.75

OMNITRANS – CA 21.75

First Transit, Inc. (6087) – TX 21.25

Riverside Transit Agency – CA 20.50

City of Tucson – AZ 20.50

Sun Tran of Albuquerque – AZ 19.50

First Transit, Inc. (9179) – CA 19.25


Sacramento Regional Transit District – CA 18.25

Central Contra Costa Transit Authority – CA 18.00

Laidlaw Transit Services, Inc. (9158) – CA 17.50

Golden Gate Bridge, Highway and Transportation District – CA 17.25

Fort Worth Transportation Authority – TX 15.50

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Table 21 below summarizes the results of the next group, SW Region Group D. A total of 27 transit agencies form this peer group. Composite scores in this group exclude the measure of revenue miles between failures due to a lack of NTD data on vehicle system failures from several agencies. Table 21: Initial Benchmarking Results: SW Region – Group D


Regional Transportation Commission of Washoe County – NV 24.25

Culver City Municipal Bus Lines – CA 24.00

City of Vallejo: Vallejo Transit Vallejo-San Francisco Ferry – CA 24.00

Laredo Metro, Inc. – TX 23.25

UNITRANS University of California, Davis – CA 22.50

Fresno Area Express – CA 22.00

Norwalk Transit System – CA 21.75

Montebello Bus Lines – CA 21.50

City of Gardena Transportation Department – CA 21.50

Golden Empire Transit District – CA 21.50

Santa Barbara Metropolitan Transit District – CA 21.50

Santa Clarita Transit – CA 21.50

County of San Diego Transit System – CA 20.50

City of Tempe Transportation Planning & Transit Division – AZ 20.25

South Coast Area Transit – CA 20.00

Regional Public Transportation Authority – AZ 19.75

Colorado Springs Transit System – CO 19.00

Eastern Contra Costa Transit Authority – CA 18.75

Santa Cruz Metropolitan Transit District – CA 18.50

Monterey-Salinas Transit – CA 18.50

Sonoma County Transit – CA 18.50

Central Oklahoma Transit & Parking Authority – OK 17.75

City of Torrance Transit System – CA 17.75

Metropolitan Tulsa Transit Authority – OK 17.75

Corpus Christi Regional Transportation Authority – TX 17.25

SunLine Transit Agency – CA 17.25

San Joaquin Regional Transit District – CA 15.25

According to the results in Table 21 above, the Regional Transportation Commission of Washoe County has the highest overall composite score in this peer group, while San Joaquin Regional

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Transit District has the lowest. Closer examination of the subcategory scores provides some insight into these results. Washoe County ranks at the top of the group in terms of service supply, while San Joaquin ranks at the bottom. San Joaquin also ranks lowest in the group in terms of vehicle utilization, while Washoe County ranks best in this subcategory. Both transit agencies place in the middle of the group in terms of cost efficiency and the operating ratios. The final group in this region is SW Region Group E, which contains 48 of the smaller transit agencies in the southwest region. Table 22, on the following page, provides the initial benchmarking results of this group. It should be noted that, due to a lack of NTD data on vehicle system failures and collisions from several agencies in the group, the composite scores listed in Table 22 do not include the measures of revenue miles between failures and revenue miles between collisions. The City of Lodi, National City Transit, Beaumont Transit System, and The Logan Transit District have the best composite scores in this group. All four are strong in the subcategories of service supply and service consumption. The City of Lodi ranked best in the group in terms of vehicle utilization. National City was strong in most subcategories, but ranked low in terms of quality of service and cost efficiency. Beaumont also was strong in most subcategories, ranking third-best in vehicle utilization, but it ranked relatively low in the operating ratios. Finally, Logan’s strongest subcategory is service consumption, where it ranked second-best in the group. Santa Fe Trails, Waco Transit System, and the Lower Rio Grande Valley Development Council have the lowest composite score in this group. Santa Fe ranked in the middle to lower part of the group in each subcategory, and ranked last in terms of vehicle utilization. Waco ranked in the lower part of the group in each subcategory. Lower Rio Grande also ranked in the lower part of the group in most subcategories, including having the lowest score in quality of service; however, this transit agency was relatively strong in terms of cost efficiency and the operating ratios.

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Table 22: Initial Benchmarking Results: SW Region – Group E


Score

City of Lodi – CA 24.00 Livermore/Amador Valley Transit Authority – CA

19.00

National City Transit – CA 23.50 Chico Area Transit System City of Chico – CA

19.00

Beaumont Transit System – TX 23.25 City of Union City Transit Division – CA 19.00

The Logan Transit District – UT 23.00 Amarillo City Transit – TX 18.75

Island Transit – TX 22.25 Pueblo Transit – CO 18.50

City of Mesa – AZ 22.00 City of Greeley-Transit Services – CO 18.25

Western Contra Costa Transit Authority – CA

21.75 Santa Maria Area Transit – CA 18.25

Victor Valley Transit Authority – CA 21.50 City of Fairfield, Fairfield/Suisun Transit – CA

18.00

City Transit Management Company, Inc. – TX

21.25 City of San Angelo – TX 18.00

Ventura Intercity Service Transit Authority – CA

21.25 Yuba-Sutter Transit Authority – CA 18.00

Yolo County Transportation District – CA 21.00 City of Denton Public Transportation Department – TX

17.75

Merced County Transit – CA 21.00 Mesa County – CO 17.75

City of Santa Rosa – CA 20.75 Roseville Transit – CA 17.75

Antelope Valley Transit Authority – CA 20.50 Thousand Oaks Transit c/o City of Thousand Oaks – CA

17.75

Modesto Area Express – CA 20.25 City of Scottsdale - Scottsdale Connection – AZ

17.50

Simi Valley Transit – CA 20.25 Abilene Transit System – TX 17.50

City of Commerce Municipal Buslines – CA 20.00 Brazos Transit District – TX 17.50

Redding Area Bus Authority – CA 20.00 Hill County Transit District – TX 17.25

Transfort – CO 20.00 Port Arthur Transit – TX 17.25

City of Visalia-Visalia City Coach – CA 19.75 City of Lompoc-Lompoc Transit – CA 16.50

City of Napa Napa Valley Transit/ The V.I.N.E. – CA

19.75 City of Glendale – Transit – AZ 16.25

City of Brownsville Brownsville Urban System – TX

19.50 Santa Fe Trails - City of Santa Fe – NM 16.00

City of San Luis Obispo – CA 19.50 Waco Transit System, Inc. – TX 16.00

Las Cruces Area Transit-RoadRUNNER – NM

19.25 Lower Rio Grande Valley Development Council – TX

15.75

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Northwest (NW) Region The Northwest (NW) Region includes 24 transit agencies in the following states:

Alaska Idaho Montana Oregon Washington Wyoming

Transit agencies in this region were divided into four separate peer groups based on the peer selection process used throughout this study. The NW Region contains two transit agencies that are significantly larger in size than the other agencies in the region: King County Department of Transportation (Seattle Metro) and Tri-County Metropolitan Transportation District of Oregon. These two agencies are too large to compare with the other agencies in the region, and are too few to be considered one peer group. For informational purposes, Table 23 presents their composite scores when compared with the next two largest agencies in the region, Pierce County Public Benefit Authority and Snohomish County Transportation Benefit Area Corporation (NW Region Group A). King County and Tri-County are also included in the “Large” peer comparisons presented after this region’s results are discussed. The two large systems, Tri-County and King County ranked the best in terms of service supply, service consumption, and the operating ratios. However, Pierce County ranked very strong in terms of quality of service, cost efficiency, and vehicle utilization. Table 23: Initial Benchmarking Results: NW Region – Group A


Tri-County Metropolitan Transportation District of Oregon – OR 23.25

Pierce County Public Benefit Authority – WA 22.00

King County Department of Transportation - Metro Transit Division – WA 20.75

Snohomish County Transportation Benefit Area Corporation – WA 17.25

Table 24, on the following page, presents the results of NW Region Group B, which places Pierce County and Snohomish County with this next peer group. Group B has a total of 9 transit agencies.

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Table 24: Initial Benchmarking Results: NW Region – Group B


Pierce County Public Benefit Authority – WA 24.50

Clark County Public Transportation Benefit Area Authority – WA 22.75

Spokane Transit Authority – WA 22.00

Lane Transit District – OR 22.00

Municipality of Anchorage - Public Transportation Department – AK 21.75

Salem Area Mass Transit District – OR 21.25

Snohomish County Transportation Benefit Area Corporation – WA 21.00

Intercity Transit – WA 19.25

Kitsap Transit – WA 19.25

Table 24 shows that Pierce County ranks best in this group, while Snohomish County ranks lower in the group. The two lowest scores belong to Intercity Transit and Kitsap Transit. In this peer group, Pierce County ranked best in the subcategories of service supply and vehicle utilization, but ranked low in the operating ratios. Snohomish County ranked second-best in terms of quality of service, but ranked the lowest in cost efficiency. Intercity Transit ranked in the middle to lower part of the group for all subcategories except cost efficiency, where it ranked best in the group. Finally, Kitsap Transit ranked lowest in terms of service supply and vehicle utilization, but did rank best in the group in quality of service. Table 25 presents this peer group without the relatively large systems of Pierce County and Snohomish County. Now, NW Region Group B(1) contains 8 transit agencies, with the addition of Ben Franklin Transit (Ben Franklin was on the border between this group and Group C, and so was not included in Group B with the two larger agencies of Pierce and Snohomish). Table 25: Initial Benchmarking Results: NW Region – Group B(1)


Clark County Public Transportation Benefit Area Authority – WA 22.75

Spokane Transit Authority – WA 22.50

Lane Transit District – OR 22.25

Municipality of Anchorage - Public Transportation Department – AK 21.75

Ben Franklin Transit – WA 21.50

Kitsap Transit – WA 20.75

Salem Area Mass Transit District – OR 20.50

Intercity Transit – WA 19.00

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The results of NW Region Group B(1), shown in Table 25 on the previous page, indicate that Clark County Public Transportation Benefit Area Authority ranks the highest in this group. Intercity Transit has the lowest composite score in this peer group. Upon closer examination of the subcategory scores, it is evident that Clark County ranked near the top of the group in most of the subcategories. Intercity Transit mostly ranked near the bottom of the group, but ranked third-best in terms of cost efficiency. Both Clark County and Intercity Transit had the same score for the operating ratios, placing them in the middle of the peer group for that subcategory. The final group in the NW Region is Group C. This peer group contains 12 transit agencies, and the results are provided in Table 26. Targhee Regional Public Transit Authority operates only purchased motorbus services, and does not report failures in the NTD; as such, the composite scores in Table 26 do not include the measure of revenue miles between failures. Table 26: Initial Benchmarking Results: NW Region – Group C


Whatcom Transportation Authority – WA 24.75

Yakima Transit – WA 22.25

Community Urban Bus Service – WA 22.00

Boise Urban Stages/VIATrans – ID 21.00

Everett Transit – WA 20.25

City of Pocatello, Pocatello Regional Transit – ID 20.25

Targhee Regional Public Transit Authority – ID 20.25

Rogue Valley Transit District – OR 19.75

Missoula Urban Transportation District – MT 19.75

The City of Cheyenne Transit Program – WY 19.00

Great Falls Transit District – MT 18.50

Billings Metropolitan Transit – MT 18.25

Whatcom Transportation Authority has the highest composite score in this peer group. Whatcom ranked near the top of most subcategories, having the best score in service consumption (tied with another transit agency); however, it ranked lowest in terms of cost efficiency. Great Falls Transit District and Billings Metropolitan Transit received the lowest composite scores in this peer group. Both ranked in the middle to lower part of the group in each of the subcategories of measures.

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Largest Transit Systems in the U.S. There are several transit agencies from each of the geographic regions analyzed thus far in the report that are significantly larger than most of the agencies in their respective regions or groups. For informational purposes, the 20 largest transit agencies from all geographic regions were placed in a peer group called simply “Large.” Transit agencies were placed in this group using the arbitrary criterion of operating more than 500 vehicles in peak service. This group of 20 agencies was then further categorized into those agencies operating greater than 1,000 peak vehicles, and less than 1,000 peak vehicles. Table 27 shows the first of these comparisons, using all 20 of the transit agencies operating more than 500 vehicles in peak service. Table 27: Initial Benchmarking Results: Large Group (> 500 Vehs)






Utah Transit Authority – UT 22.75





GTJC – NY 20.50











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Table 27 indicates that Chicago Transit Authority and New York City Transit have the highest scores in this group, followed by Miami-Dade Transit. Port Authority of Allegheny County and Metro Transit have the lowest scores in this group. Further examination of the subcategory results shows that Chicago and New York are tied for the best score in terms of service supply, and are second-best and best, respectively, in service consumption. Both agencies rank at the top or in the upper part of the group for all the subcategories; except New York ranked lowest in terms of cost efficiency. Miami-Dade Transit ranked best in the group in terms of vehicle utilization. Port Authority and Metro Transit ranked in the middle to lower part of the group in all subcategories; Metro Transit ranked lowest in vehicle utilization. Table 28 shows the results of all transit systems that operate more than 1,000 vehicles in peak service. There are 7 transit agencies in this group. Table 28: Initial Benchmarking Results: Large Group (>1000 Vehs)









In this group of agencies that operate more than 1,000 vehicles in their peak, Chicago Transit Authority had the highest composite score, while Washington Metropolitan Area Transit Authority (WMATA) had the lowest score. Chicago tied for the best score in terms of service supply (with New York City Transit), had the highest score in vehicle utilization, and was second-best in terms of service consumption. WMATA, however, ranked mainly in the middle to lower part of the group in the subcategories, and ranked lowest in service supply. Lastly, Table 29, on the following page, illustrates the results of those transit systems in this larger group that operate less than 1,000 vehicles in the peak. A total of 13 transit agencies are placed in this group.

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Table 29: Initial Benchmarking Results: Large Group (<1000 Vehs) Transit Agency Composite Score



GTJC – NY 22.50



Utah Transit Authority – UT 21.00








The results in Table 29 above indicate that large systems from the southeast region perform the strongest in this peer group. Metropolitan Atlanta Rapid Transit Authority (MARTA) and Miami-Dade Transit tied for the best score in the group. Port Authority of Allegheny County had the lowest composite score in the group. Closer analysis of the subcategory scores shows that MARTA ranked in the middle to upper part of the group in all subcategories, ranking second-best in vehicle utilization. Miami-Dade ranked second-worst in quality of service, but had the third-best score in cost efficiency and the best score in vehicle utilization. Port Authority typically ranked in the middle to lower parts of the group, having the group’s lowest score in the operating ratios. However, Port Authority did rank second-best in terms of quality of service.

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Conclusions As the results and discussion on the previous pages illustrate, placing transit agencies into different peer groups produces varied results. It is clear that the most crucial element of a benchmarking analysis, such as the one undertaken in this study, is the categorization of transit agencies into appropriate peer groups. The intent of this study was to be a first approach to this issue using NTD data, and to address the limitations of previous efforts, as described in the review of literature. It should be noted that the peer groups presented in this report are reasonable given the NTD information used, and provide a valid analysis for 2001 data. However, the work presented in this report lays the foundation for a refinement of the peer selection process used herein. Additional research is required to incorporate other NTD and non-NTD data (such as U.S. Census or other available socio-economic data including transit-dependency, income levels, cost of living, and/or wage rates) into the process. Other factors that might be considered include the age of the transit agency, modes operated systemwide, and whether services are directly operated, contracted, or some combination of both. It may also be appropriate to form peer groups of only the smallest transit agencies in each region, such as those operating less than 10 peak vehicles, for example. This study developed benchmark rankings for fixed-route motorbus systems in the U.S. The method used in this study can also be applied to other modes such as rail and demand-response, creating additional peer groups. Future efforts may also refine the measures used in the generation of the benchmarking results. One issue that arose during the study involved the lack of NTD data on vehicle failures and collisions for some transit agencies. Without such information, some of the benchmarks do not include the quality of service measures dealing with safety (revenue miles between failures) and security (revenue miles between collisions). It should be noted that, beginning in 2002, NTD data are more complete with regard to incidents (collisions); however, vehicles system failures are still not required to be reported for purchased transportation services. This study used a relatively simple scoring method; other, more complex techniques might be explored, such as different indexes and the introduction of weighting schemes. Everything required for the benchmarking process has been prepared in Microsoft Excel. Modifications to peer groups, measures used, or the scoring method would be relatively simple to accomplish using what was developed in this study as a starting point (or template). Future benchmarking studies might also examine performance indexes based on three to five years of trend data for each transit agency, to capture major changes that can occur from year to year. The initial benchmarking results presented in this report are not intended to indicate that one particular transit agency is “better” than another. Rather, the development of peer groups and

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a composite scoring system based on an array of standardized NTD information allows transit agencies to see where they fit into a group of similar agencies. An inspection of the tables listing benchmarking results in this report show that many transit agencies have identical composite scores within their groups; this is not unintentional, as it shows that such transit agencies are performing at about the same level, as opposed to explicitly ranking them from “best” to “worst.” These benchmarking results show how transit agencies in the U.S. perform within the peer groups as defined in this study, using measures and a scoring system developed for this project. Brief discussions of results are included, but the analysis does not go beyond what is revealed by the NTD data used in the study. While this benchmarking process is intended to be comprehensive, there is no doubt that future benchmarking efforts will benefit from additional input from the industry. Finally, it must be noted that the results of this initial benchmarking study, or any other single ranking effort, do not conclusively define the overall performance of any transit agency. As discussed earlier in this report, there are countless other factors, both exogenous and endogenous to a transit agency’s operations, that influence performance. One of the most important factors is the agency goals. Goals such as geographic coverage, service to specific areas or groups, low fares, etc., often dictate a level of performance that can be acceptable to transit agencies and their governing boards, even when that performance appears “low” by the definitions used in a study such as this one. Benchmarking studies such as this never reveal the whole picture regarding transit performance, and should always be used as only one of many tools to measure performance and target areas for improvements.

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References American Public Transportation Association (APTA), Measuring Transit Fixed Route Performance

r f

t f r

– The T ansit Per ormance Index, October 4, 1998. Chu, Xuehao, On Ranking Transit Operators (Internal CUTR Report), December 1996. Chu, Xuehao, Gordon J. Fielding, Bruce W. Lamar, Measuring Transit Performance Using Data Envelopment Analysis, 1992. CUTR, 2000 Performance Evaluation of Florida’s Transit Systems, December 2000. Hartgen, David T. and Mark W. Horner, Compara ive Performance o Major US Bus T ansit Systems: 1988-199, Transportation Publication Report Number 163, University of North Carolina at Charlotte, May 1997.

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Appendix A – List of Definitions Average Headway - Average headway, in minutes, for the system as a whole that is computed utilizing the following performance indicators: directional route miles, revenue miles, revenue hours, and the number of vehicles operated in maximum service. The equation used to determine this measure first doubles the directional route mileage to produce an estimate of system size in terms of total (non-directional) route miles. The resulting mileage figure is then divided by the system’s calculated average speed (revenue miles per revenue hour) to produce an estimate of the time it would take, in hours, to traverse all of the system’s total route miles. Finally, this time figure is divided by the system’s number of peak vehicles (then multiplied by 60 to convert time in hours to minutes) to determine the number of minutes it takes for a vehicle to complete its portion of the total route miles one time. Average Speed - Average speed of vehicles in revenue service operation (i.e., not including travel to and from the garage or any other deadhead) calculated by dividing total revenue miles by total revenue hours.

Average Weekday Span of Service – Time, in hours, from the start of service to the end of service, for an average weekday. Directly-Generated Non-Fare Revenue - All revenues, except passenger fares, generated through the operation of the transit agency. Includes special transit fares, school bus service revenues, freight tariffs, charter service revenues, auxiliary transportation revenues, subsidy from other sectors of operations, and non-transportation revenues. Farebox Recovery - Ratio of passenger fare revenues to total operating expenses; an indicator of the share of total operating costs that is covered by the passengers’ fares. This measure is expressed as a percentage.

General/Administrative Expense - General administration is all the activities associated with supporting the provision of transit service. Usually, these activities benefit all modes and types of service and are reported using a cost allocation plan. General/Administrative Expense Per Operating Expense - Calculated by dividing general/administrative expense by operating expense; expressed as a percent of total operating expense. Local Contribution - Any financial assistance obtained from a local government or agency (below the State level) to assist in paying the operating costs of providing transit service. Local Revenue - All revenues originating at the local level (excluding state and federal assistance). This represents the sum of local contribution, directly-generated non-fare revenue, and passenger fare revenue.

A-1

Local Revenue Per Operating Expense - Ratio of total local commitment with respect to total operating expense; expressed as a percent of total operating expense. Non-Vehicle Maintenance Expense - Non-vehicle maintenance are all the activities associated with ensuring buildings, grounds and equipment (garages, passenger stations and shelters, administration buildings); fare collection equipment; and communications systems, track, structures, tunnels and power systems are operable. Non-Vehicle Maintenance Expense Per Operating Expense - Calculated by dividing non-vehicle maintenance expense by operating expense; expressed as a percent of total operating expense. Number of Collisions - Total number of unforeseen occurrences resulting in collisions (excluding suicides) with property damage in excess of $1,000 as reported in the Safety and Security NTD forms. Number of Vehicle System Failures - Total number of failures that occur during revenue service, deadheading, and layovers. A failure is classified as the breakdown of either a major or minor element of the revenue vehicle’s mechanical system. Failures are tabulated regardless of whether they result in a vehicle completing or not completing its trip. Operating Expense Per Capita - Annual operating budget divided by the service area population; a measure of the resource commitment to transit by the community. Operating Expense Per Revenue Hour - Operating expense divided by revenue hours of operation; a key comparative measure which differs from operating expense per vehicle mile in that the vehicle speed is factored out. This is often important since vehicle speed is strongly influenced by local traffic conditions. Passenger Fare Revenue - Revenue generated annually from carrying passengers in regularly scheduled service. Passenger Miles - Number of annual passenger trips multiplied by the system's average trip length (in miles). This number provides a measure of the total number of passenger miles of transportation service consumed. Passenger Trips - Annual number of unlinked passenger boardings on the transit vehicles. A trip is counted each time a passenger boards a transit vehicle. Thus, if a passenger has to transfer between buses to reach a destination, he/she is counted as making two passenger trips.

Passenger Trips Per Capita - Average number of transit boardings per person per year. This number is larger in areas where public transportation is emphasized and in areas where there are more transit dependents, and is a measure of the extent to which the public utilizes transit in a given service area.

A-2

Passenger Trips Per Revenue Hour - The ratio of passenger trips to revenue hours of operation; reports on the effectiveness of the service since hours are a better representation of the resources consumed in providing service Passenger Trips Per Revenue Mile - The ratio of passenger trips to revenue miles of service; a key indicator of service effectiveness that is influenced by the levels of demand and the supply of service provided. Peak Vehicles - Number of revenue vehicles operated to meet the annual maximum service requirement, i.e., the revenue vehicle count during the peak hours of the peak days/weeks of the peak season (typically the rush period). Vehicles operated in maximum service exclude atypical days or one-time special events. Revenue Hours - Total hours of operation by revenue service vehicles in active revenue service. Revenue Hours Per Total Vehicles - Total revenue hours of service that are provided, on average, by each vehicle available for maximum service. Revenue Miles - Number of annual miles of vehicle operation while in active service (available to pick up revenue passengers). This number is smaller than vehicle miles because of the exclusion of deadhead miles such as vehicle miles from the garage to the start of service, vehicle miles from the end of service to the garage, driver training, and other miscellaneous miles that are not considered to be in direct revenue service. Revenue Miles Between Collisions - Number of revenue miles divided by the number of collisions (excluding suicides); reports the average interval, in miles, between collisions. Revenue Miles Between Failures - Number of revenue miles divided by the number of vehicle system failures; an indicator of the average frequency of delays because of a problem with the equipment. Revenue Miles Per Peak Vehicle - Total revenue miles of service that are provided, on average, by each vehicle operated in maximum service. Revenue Miles Per Route Mile - Number of revenue miles divided by the number of directional route miles of service; an indicator of the availability of transit service. Revenue Miles Per Total Vehicles - Total revenue miles of service that are provided, on average, by each vehicle available for maximum service. Revenue Miles Per Vehicle Mile - Reflects how much of the total vehicle operation is in passenger service. Higher ratios are favorable, but garage location, training needs, and other considerations may influence the ratio.

A-3

Route Miles - Number of directional route miles as reported in NTD data; defined as the mileage that service operates in each direction over routes traveled by public transportation vehicles in revenue service. Service Area Population - This measure provides a suitable approximation of overall market size for comparison of relative spending and service levels. According to NTD definitions, it should be measured as a ¾-mile buffer around a fixed-route network; however, agencies that serve a larger area with another mode such as demand-response will report the larger figure.

Service Area Population Density – Measured as service area population per square mile of service area. Service area size, in square miles, is reported in the NTD.

Spare Ratio - Vehicles operated in maximum service subtracted from vehicles available for maximum service divided by vehicles operated in maximum service. This measure is an indicator of the number of spare vehicles available for service. A spare ratio of approximately 20 percent is considered appropriate in the industry. However, this varies depending on the size and age of fleet as well as the condition of equipment. Total Operating Expense - Reported total spending on operations, including administration, maintenance, and operation of service vehicles. Total Vehicles - Number of vehicles available for use by the transit agency to meet the annual maximum service requirement. Vehicles available for maximum service include spares, out-of-service vehicles, and vehicles in or awaiting maintenance, but exclude vehicles awaiting sale and emergency contingency vehicles.

Vehicle Hours - Total hours of operation by revenue service vehicles including hours consumed in passenger service and deadhead travel. Vehicle Hours Per Peak Vehicle - Substitutes vehicle hours for vehicle miles and again reflects how intensively equipment is utilized. Vehicle Maintenance Expense - Vehicle maintenance are all the activities associated with ensuring revenue vehicles and service vehicles are operable, cleaned, fueled, inspected and repaired. Vehicle Maintenance Expense Per Operating Expense - Calculated by dividing vehicle maintenance expense by operating expense; expressed as a percent of total operating expense. Vehicle Miles - Total distance traveled annually by revenue service vehicles, including both revenue miles and deadhead miles.

A-4

Vehicle Miles Per Peak Vehicle - Vehicle miles divided by the number of vehicles operated in maximum service. It is an indicator of how intensively the equipment is used and is influenced by the bus travel speeds as well as by the levels of service in the off-peak time periods. A more uniform demand for service over the day would result in a higher number.

A-5

Appendix B – Detailed Data Tables

B-1

B-2

Initi

al B

ench

mar

king

Res

ults

: S

E R

egio

n --

Gro

up A

Tran

sit A

genc

yR

even

ue M

iles

Per R

oute

Mile

Alt S

core

Tran

sit A

genc

yAv

erag

e W

eekd

ay S

pan

of

Serv

ice

Alt S

core

Tran

sit A

genc

yPa

ssen

ger T

rips

Per C

apita

Alt S

core

Tran

sit A

genc

yPa

ssen

ger T

rips

Per R

even

ue M

ileAl

t Sco

re

Mia

mi-D

ade

Tran

sit

14,6

78.9

31

Mia

mi-D

ade

Tran

sit

24.0

01

Mia

mi-D

ade

Tran

sit

34.4

30.

75M

iam

i-Dad

e Tr

ansi

t2.

601

Met

ropo

litan

Atla

nta

Rap

id T

rans

it A

utho

rity

13,5

88.0

91

Met

ropo

litan

Atla

nta

Rap

id T

rans

it Au

thor

ity22

.00

1M

etro

polit

an A

tlant

a R

apid

Tra

nsit

Auth

ority

60.1

51

Met

ropo

litan

Atla

nta

Rap

id T

rans

it Au

thor

ity2.

991

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n10

,019

.19

0.75

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n21

.50

1Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

19.4

20.

75Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

2.38

0.75

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffers

19,8

04.6

51.

5R

egio

nal T

rans

it Au

thor

ity o

f Orle

ans

and

Jeffe

rs14

.00

0.75

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffers

102.

151.

5R

egio

nal T

rans

it Au

thor

ity o

f Orle

ans

and

Jeffe

rs4.

181.

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

9,11

7.96

0.5

Tran

spor

tatio

n D

istri

ct C

omm

issi

on o

f Ham

pton

9.

500.

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

12.9

30.

75Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

1.50

0.5

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

11,7

47.2

60.

75C

entra

l Flo

rida

Reg

iona

l Tra

nspo

rtatio

n Au

thor

it22

.83

1C

entra

l Flo

rida

Reg

iona

l Tra

nspo

rtatio

n Au

thor

it14

.85

0.75

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

1.84

0.75

Tran

sit A

genc

yPa

ssen

ger T

rips

Per R

even

ue

Hou

rAl

t Sco

reTr

ansi

t Age

ncy

Aver

age

Spee

dAl

t Sco

reTr

ansi

t Age

ncy

Rev

enue

Mile

s B

etw

een

Failu

res

Alt S

core

Tran

sit A

genc

yAv

erag

e H

eadw

ay

(min

utes

)Al

t Sco

re

Mia

mi-D

ade

Tran

sit

33.2

30.

75M

iam

i-Dad

e Tr

ansi

t12

.79

0.75

Mia

mi-D

ade

Tran

sit

1,54

30.

75M

iam

i-Dad

e Tr

ansi

t14

.71

1M

etro

polit

an A

tlant

a R

apid

Tra

nsit

Aut

horit

y37

.34

1M

etro

polit

an A

tlant

a R

apid

Tra

nsit

Auth

ority

12.4

90.

5M

etro

polit

an A

tlant

a R

apid

Tra

nsit

Auth

ority

2,24

70.

75M

etro

polit

an A

tlant

a R

apid

Tra

nsit

Auth

ority

15.9

81

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n32

.65

0.75

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n13

.72

1Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

28,3

021.

5Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

22.6

70.

5R

egio

nal T

rans

it Au

thor

ity o

f Orle

ans

and

Jeffe

rs57

.75

1.5

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffers

13.8

31

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffers

1,69

40.

75R

egio

nal T

rans

it Au

thor

ity o

f Orle

ans

and

Jeffe

rs8.

551.

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

19.0

50.

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

12.6

60.

75Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

4,89

70.

75Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

19.3

10.

75C

entra

l Flo

rida

Reg

iona

l Tra

nspo

rtatio

n Au

thor

it25

.71

0.75

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

13.9

81.

5C

entra

l Flo

rida

Reg

iona

l Tra

nspo

rtatio

n Au

thor

it16

,851

1C

entra

l Flo

rida

Reg

iona

l Tra

nspo

rtatio

n Au

thor

it22

.26

0.75

Tran

sit A

genc

yO

pera

ting

Expe

nse

Per

Cap

itaAl

t Sco

reTr

ansi

t Age

ncy

Ope

ratin

g Ex

pens

e Pe

r R

even

ue H

our

Alt S

core

Tran

sit A

genc

y

Veh.

Mai

nt.

Expe

nse

Per

Ope

ratin

g Ex

pens

e

Alt S

core

Tran

sit A

genc

y

Non

-Veh

. Mai

nt.

Expe

nse

Per

Ope

ratin

g Ex

pens

e

Alt S

core

Mia

mi-D

ade

Tran

sit

$85.

220.

75M

iam

i-Dad

e Tr

ansi

t$8

2.25

0.75

Mia

mi-D

ade

Tran

sit

21.5

8%0.

75M

iam

i-Dad

e Tr

ansi

t4.

90%

0.5

Met

ropo

litan

Atla

nta

Rap

id T

rans

it A

utho

rity

$130

.50

0.75

Met

ropo

litan

Atla

nta

Rap

id T

rans

it Au

thor

ity$8

1.01

0.75

Met

ropo

litan

Atla

nta

Rap

id T

rans

it Au

thor

ity27

.99%

0.5

Met

ropo

litan

Atla

nta

Rap

id T

rans

it Au

thor

ity2.

86%

1Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

$38.

111

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n$6

4.07

1Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

18.8

1%1

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n2.

89%

1R

egio

nal T

rans

it Au

thor

ity o

f Orle

ans

and

Jeffe

rs$1

74.9

60.

5R

egio

nal T

rans

it Au

thor

ity o

f Orle

ans

and

Jeffe

rs$9

8.92

0.5

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffers

19.5

1%1

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffers

3.39

%1

Tran

spor

tatio

n D

istri

ct C

omm

issi

on o

f Ham

pton

$3

5.45

1Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

$52.

241.

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

17.7

1%1

Tran

spor

tatio

n D

istri

ct C

omm

issi

on o

f Ham

pton

3.

71%

0.75

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

$35.

151

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

$60.

871

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

18.0

6%1

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

3.43

%1

Tran

sit A

genc

y

Gen

. Adm

in.

Expe

nse

Per

Ope

ratin

g Ex

pens

e

Alt S

core

Tran

sit A

genc

yFa

rebo

x R

ecov

ery

Alt S

core

Tran

sit A

genc

y

Loca

l Rev

enue

Pe

r Ope

ratin

g Ex

pens

e (S

yste

m

Tota

l)

Alt S

core

Tran

sit A

genc

yVe

hicl

e M

iles

Per

Peak

Veh

icle

Alt S

core

Mia

mi-D

ade

Tran

sit

11.0

4%1.

5M

iam

i-Dad

e Tr

ansi

t26

.82%

0.75

Mia

mi-D

ade

Tran

sit

52.1

5%1

Mia

mi-D

ade

Tran

sit

53,6

851

Met

ropo

litan

Atla

nta

Rap

id T

rans

it A

utho

rity

15.5

0%1

Met

ropo

litan

Atla

nta

Rap

id T

rans

it Au

thor

ity30

.26%

1M

etro

polit

an A

tlant

a R

apid

Tra

nsit

Auth

ority

62.0

1%1

Met

ropo

litan

Atla

nta

Rap

id T

rans

it Au

thor

ity53

,137

1Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

12.2

0%1

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n22

.56%

0.5

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n56

.89%

1Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

56,3

011

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffer s

26.0

3%0.

5R

egio

nal T

rans

it Au

thor

ity o

f Orle

ans

and

Jeffe

rs37

.43%

1.5

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffers

54.8

2%1

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffers

41,1

080.

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

23.8

9%0.

75Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

28.2

9%0.

75Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

19.5

2%0.

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

41,6

770.

5C

entra

l Flo

rida

Reg

iona

l Tra

nspo

rtatio

n Au

thor

it21

.48%

0.75

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

33.9

0%1

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

40.4

3%0.

75C

entra

l Flo

rida

Reg

iona

l Tra

nspo

rtatio

n Au

thor

it66

,294

1.5

Tran

sit A

genc

yVe

hicl

e H

ours

Pe

r Pea

k Ve

hicl

eAl

t Sco

reTr

ansi

t Age

ncy

Rev

enue

Mile

s Pe

r Veh

icle

Mile

Alt S

core

Tran

sit A

genc

yR

even

ue M

iles

Per T

otal

Ve

hicl

esAl

t Sco

reTr

ansi

t Age

ncy

Rev

enue

Hou

rs

Per T

otal

Ve

hicl

esAl

t Sco

re

Mia

mi-D

ade

Tran

sit

3,94

51

Mia

mi-D

ade

Tran

sit

0.86

0.5

Mia

mi-D

ade

Tran

sit

34,3

930.

75M

iam

i-Dad

e Tr

ansi

t2,

690

0.75

Met

ropo

litan

Atla

nta

Rap

id T

rans

it A

utho

rity

3,98

41

Met

ropo

litan

Atla

nta

Rap

id T

rans

it Au

thor

ity0.

850.

5M

etro

polit

an A

tlant

a R

apid

Tra

nsit

Auth

ority

38,2

891

Met

ropo

litan

Atla

nta

Rap

id T

rans

it Au

thor

ity3,

065

1Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

3,94

61

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n0.

921

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n40

,382

1Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

2,94

31

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffer s

3,33

80.

75R

egio

nal T

rans

it Au

thor

ity o

f Orle

ans

and

Jeffe

rs0.

951.

5R

egio

nal T

rans

it Au

thor

ity o

f Orle

ans

and

Jeffe

rs30

,911

0.5

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffers

2,23

50.

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

3,09

60.

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

0.89

0.75

Tran

spor

tatio

n D

istri

ct C

omm

issi

on o

f Ham

pton

35

,141

0.75

Tran

spor

tatio

n D

istri

ct C

omm

issi

on o

f Ham

pton

2,

775

0.75

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

4,65

41.

5C

entra

l Flo

rida

Reg

iona

l Tra

nspo

rtatio

n Au

thor

it0.

921

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

49,4

741.

5C

entra

l Flo

rida

Reg

iona

l Tra

nspo

rtatio

n Au

thor

it3,

539

1.5

Tran

sit A

genc

yR

even

ue M

iles

Per P

eak

Vehi

cle

Alt S

core

Tran

sit A

genc

ySp

are

Vehi

cle

Rat

ioAl

t Sco

reTr

ansi

t Age

ncy

Com

posi

te S

core

(M

easu

res

Onl

y)

Mia

mi-D

ade

Tran

sit

46,0

250.

75M

iam

i-Dad

e Tr

ansi

t33

.82%

1.5

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

23.2

5M

etro

polit

an A

tlant

a R

apid

Tra

nsit

Aut

horit

y45

,210

0.75

Met

ropo

litan

Atla

nta

Rap

id T

rans

it Au

thor

ity18

.08%

0.75

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffer s

21.5

0Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

51,9

431

Brow

ard

Cou

nty

Mas

s Tr

ansi

t Div

isio

n28

.63%

1Br

owar

d C

ount

y M

ass

Tran

sit D

ivis

ion

20.5

0R

egio

nal T

rans

it Au

thor

ity o

f Orle

ans

and

Jeffe

rs39

,024

0.75

Reg

iona

l Tra

nsit

Auth

ority

of O

rlean

s an

d Je

ffers

26.2

5%1

Mia

mi-D

ade

Tran

sit

19.2

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

37,1

490.

5Tr

ansp

orta

tion

Dis

trict

Com

mis

sion

of H

ampt

on

5.71

%0.

5M

etro

polit

an A

tlant

a R

apid

Tra

nsit

Auth

ority

19.2

5C

entra

l Flo

rida

Reg

iona

l Tra

nspo

rtatio

n Au

thor

it60

,931

1.5

Cen

tral F

lorid

a R

egio

nal T

rans

porta

tion

Auth

orit

23.1

6%1

Tran

spor

tatio

n D

istri

ct C

omm

issi

on o

f Ham

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pel H

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sit

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iona

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tham

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iona

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ttano

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tion

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mun

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ount

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sit A

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19.8

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9,14

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22.2

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3.91

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Six

th A

ve18

.50

Documents

Benchmark Rankings for Transit Systems in the United States