Assessment of AVL for San Luis Obispo Transit

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Assessment of AVL for San Luis Obispo Transit

David Gillen*, Doug Johnson**, Nick Schrank** and Edward Sullivan***

* Wilfrid Laurier University and Institute for Transportation Studies, University of California-Berkeley

**Institute for Transportation Studies, University of California, Berkeley *** Department of Civil Engineering, Cal Poly, San Luis Obispo



This report describes the first steps in the evaluation of a methodology for Efficient Development of Advanced Public Transportation Systems (EDAPTS). The project is being undertaken by Cal Poly. The Cal Poly research project, funded by the NT&R Program, is intended to have two primary benefits: furthering the interests of public transportation in San Luis Obispo County, and furthering the state-of-the-art in advanced public transportation.

The NT&R Program wishes to further research efforts into the subset of transportation options referred to as Personalized Public Transportation. Personalized Public

Transportation acknowledges that an underutilized transportation asset are the already acquired -- empty transit seats in the transit fleets -- that must be utilized if any

meaningful progress is to be made with transportation mode shifts. The goal is to increase rider access to that asset and improve the real quality of service thereby enhancing transit’s attractiveness. A second goal is to allow transit agencies to better tailor their capacity to market demands. An interesting challenge in the case of SLO transit is they contract the provision of services to Laidlaw, a private transportation corporation. This feature of the ITS application provides a setting in which it will potentially be possible to compare implementation outcomes in a private versus a public setting.

This EDAPTS project which focuses on the evaluation of the Cal Poly hardware application to San Luis Obispo transit will be completed in two phases. The first phase implements the new technology only with the transit agency that serves the City of San Luis Obispo. Phase II will extend the system technology to agencies outside of the City. In Phase I the focus is on the transit agency and how EDAPTS influences its management and operation decisions. In other words how does AVL affect the management decisions of firms. The report also contains a full economic/financial and operational evaluation of SLO Transit in the context of its market will be completed. The purpose of the phase I work is to establish the baseline data sets and to form of the measures to be used in the evaluation.

The report is organized in the following way. Section 2 describes the range of ITS applications available for transit and describes in greater detail AVL, the variations and the expected impacts. In section 3 we provide a statistical profile of San Luis Obispo (SLO) transit. It includes economic data, operational data and some baseline measures completed in the May-June period. In section 4 we provide an assessment of how AVL can influence transit productivity. This analysis was carried using a methodology called Data Envelopment Analysis (DEA) and is aimed primarily as measuring productivity



The importance of technology to the future of public transportation cannot be overstated. As transit agencies are pushed to produce greater ridership and hours of services while keeping costs down, technological advancements will help agencies be as efficient as possible with their resources. The immense size of many agencies (both Seattle and Baltimore’s transit agencies have over 800 vehicles) presses the need for greater

automation to more closely monitor services and ridership. And the number of vehicles is not the only issue relating to agency size. Many agencies have enormous networks which may cover hundreds of miles throughout a region. In such a large system, precise information may have a large impact on agency operations.

The Federal Transit Authority (FTA) has cited technological advancement as a cornerstone of transit systems in the new century.

‘FTA will support deployment of technological innovation to improve personal mobility, minimize fuel consumption and air pollution, increase ridership, and enhance the quality of life of all communities.’1

As transit services strive for greater responsiveness in their services, the flexibility and information supplied by different technological advancements may make a huge difference in the years to come.

Many of the technologies implemented by transit agencies can be considered part of a larger system called Automated Vehicle Monitoring (AVM). These technologies improve emergency location of vehicles, vehicle performance monitoring and service control, data collection, passenger information communication including compliance with the Americans with Disabilities Act, fare collection and traffic signal priority2.

Implementation of AVM systems, either piecemeal or as a complete system, is a complicated process that can take years to operate smoothly.

Details of the value and contribution of AVL systems to productivity improvements, cost reductions, service delivery of fixed-route transit and ADA-type paratransit services, ridership and revenue are needed before investment decisions in AVL can be made. This includes decisions on: whether to invest in AVL; which type of AVL technology to select; what level of investment to choose and the timing of any such investments and, benefits/impacts of alternative control strategies utilizing AVL.


FTA website,


Transit Cooperative Research Program, “TCRP Synthesis 24, AVL Systems for Bus Transit”, Transportation Research Board National Research Council


There are a number of different ways in which AVL and other technologies can improve transit performance. In the "Advanced Public Transportation Systems: The State of the Art Update 1996", four ways in which technology can affect transit are cited.3 They are:

Fleet Management : Effective vehicle and fleet planning, scheduling, and operations.

Fleet Management focuses on the vehicle, improving the efficiency and effectiveness of the service provided (the "supply side"), and on passenger safety. By making transit more efficient and reliable, it should be more attractive to prospective riders, and the

municipalities they serve.

Traveler Information: Facilitate decision making before a passenger's trip and during

the trip (en-route). Information can be provided to trip makers at home, work,

transportation centers, wayside stops, and on-board vehicles. With links to automatic vehicle location, traveler information systems specifically for transit are beginning to provide real-time information, such as arrival times, departure times, and delays.

Electronic Fare Payment: A variety of benefits are anticipated from EFP. They are:

more sophisticated fare pricing systems, based on distance traveled, time of day, and user profile (e.g., school children, elderly, frequent users); elimination of cash and coin

handling to improve security, reduce dwell time and lower costs; automation of the accounting and financial settlement process, which will also reduce costs; creation of multimodal and multi-provider transportation networks that are seamless for the rider but operationally and organizationally sound for the multiple modes and providers.

Transportation Demand Management Technologies: The goal of these technologies is

to maximize the ability of the current transportation network - roads and transit - to serve the recent rapid increase in demand for transportation. This is accomplished through a combination of, among other things, coordination of transportation service providers, and enhanced incident response and monitoring.

2.1 AVL Technology: A Description and Experience

Advanced Vehicle Location (AVL) systems, in their most basic form, help track the whereabouts of vehicles on a network. AVL is considered part of the fleet management technologies, which concentrate on improving vehicle operations, future planning efforts and safety. By knowing the exact (though this varies across different AVL technologies) location of vehicles on a transit network, the agency has clear, objective information regarding those vehicles. Historically, dispatchers would need to verbally confirm the position of buses spread across a city. As AVL systems are implemented, vehicle information is automated and available for both current operations and planning purposes.


AVL systems have been in use for 30 years. Starting in 1969, transit agencies in the U.S. of all sizes and regions, experimented with various AVL systems4. There are four basic technologies employed for AVL systems5. In some cases, two technologies are used to create redundancy in the system. The most common technologies are:

• Signpost and odometer • Radio navigation/location • Dead-reckoning

• Global Positioning System (GPS Satellite Location)

The signpost/odometer system has been the most common until recently. In this system, a receiver is mounted on the bus, while transmitters are placed along the bus’ route. Utility poles and signposts are most commonly used as mounting locations for these transmitters. The bus picks up a low-powered signal from these transmitters as it passes by, and the mileage noted. When the bus reports its location, the distance from the last pole is used to locate the vehicle's position on a route. The system can be run in reverse, with the transmitter on the bus and multiple receivers mounted along the route. However, should the bus need to leave the route, there will be no information about the bus, so most agencies prefer to have a receiver on the bus. This older technology has some

drawbacks. Creation of new routes requires the placement of new transmitters, and the system is maintenance intensive due to the relatively high number of transmitters and receivers involved.

Radio-location systems use a low-frequency signal to cover the system, and the buses are located as they receive the signal. Loran-C (Long-Range Aid to Navigation) is the most common type of land based radio-location. Despite the simplicity of the system, it is subject to some major drawbacks. Overhead power lines or power substations can cause signal interference, and signal reception is typically very poor in canyons.

Dead reckoning is among the oldest navigation technologies. Dead reckoning sensors can measure distance and direction from a fixed point (under the most basic setup, an odometer and compass could be used to calculate position from a specific stop on a route). Typically, these systems act as a backup to another AVL system. This relatively inexpensive system is self-contained on the bus. This system has a number of drawbacks. Uneven surfaces and hills can compromise the positioning information. Should the vehicle leave a fixed route, its location will no longer be known since there will be no waypoints off the fixed route. Also, accuracy degrades with distance traveled, and regular recalibration is required (tire circumference changes with wear)6.

Due to the shortcomings of the other AVL technologies, GPS became the most popular system for new installations over the last few years. GPS utilizes the signals emitted from a network of 24 satellites, which are picked up by a receiver placed onboard the bus. The satellite system covers almost all of North America, eliminating the need to place


“AVL Systems for Bus Transit”


“Advanced Pubic Transportation Systems: The State of the Art Update 1996” pg. 6


transmitters/receivers along any route. The existence of the satellite system means that the main cost for the agencies result from purchase of the GPS receivers and equipment to transmit to dispatch. While the U.S. military, which oversees the satellite system, has limited the accuracy of the system in the past, it is now allowing more accurate readings. The accuracy and reasonable cost of GPS makes it the most appealing, though it too has some problems. Foliage, tall buildings, and tunnels can temporarily block the satellite signal, and at times satellite signals do not reach specific locations. Typically dead reckoning is used in conjunction with GPS to fill in such gaps.

The final issue regarding the technology concerns the relay of the buses’ positions to a central dispatcher. A central computer communicates with each bus and exchanges the information automatically over radio waves. There are two different methods for collecting this information, polling and exception reporting. Under polling, the central computer contacts each bus on the network in turn. When it has reached the ‘last’ bus, it will start over from the beginning. This process can take a few seconds or many minutes, depending on the number of buses and the capabilities of the computers. With exception reporting, the buses send in their signal only at a few specific locations or if they have fallen far off of their schedule. Under exception reporting, dispatch not only knows the position of the bus, but also the scheduled position of the bus. This extra amount of information makes exception reporting more useful, though somewhat more expensive7.

This information is then typically sent to a pair of personal computers (PCs) at the dispatch office. One computer serves as the communication machine, making contact with the buses. The second PC will usually map the vehicles’ location on the network. It is important to realize that proper use of mapping software like a geographic information system (G.I.S.) or computer aided design (CAD) is required in order to display this information effectively. This is a concern for many agencies with limited technology resources at their disposal. Many agencies have neither the money for additional equipment, nor for the trained personnel required. Training of employees is a key to maximizing the use of an AVL system. These PCs help anticipate and address bus failures, monitor schedule adherence and emergency response, and they can trigger location specific audio and visual announcements to comply with the Americans with disabilities act (ADA)8.


There are numerous anticipated benefits resulting from the use of AVL9. These benefits include:

• Increased dispatching and operating efficiency

• More reliable service (on-time performance, leading to increased ridership) • Quicker response to service disruptions

• Information to be used in passenger information systems

• Increased driver and passenger safety and security (silent alarms with precise location information)

• More effective response to mechanical failures, reducing maintenance costs • Inputs to traffic signals for signal preemption use

• Improved data (quantity and quality) automatically collected for agencies at a lower cost

So far, most agencies have not quantified the benefits resulting from AVL. The fact that AVL systems are able to make improvements throughout an agency (operations,

maintenance, administration) makes the use of common measures of effectiveness

(MOE) inadequate. Studies of just safety or revenue hours may not reflect all the benefits accruing to an agency. However, agencies have reported more ‘anecdotal’ benefits such as more flexible assignments, quicker emergency response, improved on-time

monitoring, and a better capability to handle grievances10. While these benefits may be hard to quantify for the agencies, they do reflect the general importance of an AVL system to almost any transit agency.

It is important to consider how transit agencies' performance is reviewed. Since there is so much variety among transit agencies (size, population, cost structures, etc), it is very difficult to compare agencies to one another. Agencies need a set of quantitative

indicators capable of tracking performance in sufficient detail in order to identify the real sources of gain (or loss) in overall productivity11.


In this section the SLO transit agency is profiled in two dimensions. First, we provide a financial and output description of the agency. This includes information on passengers, revenues, service hours, buses (capital), workers and costs. The current service levels are also profiled. Next we provide a description of the first data collected for field operations. These data were collected in the late spring of 2000 when Cal Poly was not in full

session. Because this institution is such a heavy user of transit services in San Luis Obispo a second wave of operational data will be collected in the fall of 2000 when Cal Poly students have returned for the fall term.


ibid, pg. 5


“TCRP Synthesis 24, AVL Systems for Bus Transit”, pg. 3


Hensher, David, "Editorial for Transportation Research Part B", Transportation Research Part B, vol. 26B, no. 6, 1992, pg. 433


3.1 Financial, Service and Passenger Information

The transit service supplied in the San Luis Obispo region is a combination of localized bus service and intercity/regional bus service. As noted, the EDAPTS program is expected to start at the city level, namely with San Luis Obispo (SLO) Transit, and to subsequently grow out to the surrounding bus services. Given the relatively large headways for the regional bus service, it is even more important to maintain schedule adherence and to provide the best possible connections. The use of AVL should allow the local transit agencies to accomplish that most easily.

A list of the other area transit agencies, which will eventually be a part of the EDAPTS project follow12.

CCAT Central Coast Area Transit: Countywide public bus system connecting individual

cities within the county.

SCAT South County Area Transit: Public bus system serving Shell Beach, Pismo Beach,

Grover Beach, Arroyo Grande and Oceano.

SMAT Santa Maria Area Transit

PRCATS Paso Robles City Area Transit Service Atascadero City Area Transit Service

SLO Transit operates six motorbus routes, an electric bus service and a trolley service. For our purposes, we will examine only the motorbus services at this time, as both the electric bus and trolley serve mainly weekend visitors. Also, these are not revenue services, such that payment by the city to the provider is based solely on the ability of the provider to supply the service.

All six of the bus routes that are a part of the study have two common stops called City Hall, basically located in the center of the city, and Cal Poly, which is on the California State campus. The central location of the first stop reflects the importance of good connectivity planning, especially in a low-density setting. The fact that the buses all go to campus reflects the importance of student ridership to SLO transit and the community (approximately 16,400 students attend Cal Poly September to June).

The need for good on time performance and for precise connections is made all the more important by the relatively large headways used by SLO Transit. Of the six routes, four operate buses hourly, while the remaining two operate two buses per hour. Riders who have to wait an hour if they miss a bus are not as likely to return for service again if they have the choice. This is strictly a quality of service issue given the existing quantity of service.


extends to 10:30 p.m. One of the daily routs ends service at 6:30 p.m., meaning three routes run into the late evening seven days a week. These routes provide good area coverage and are likely geared to service the college students in the area.

Given the size of the agency, the availability of data is limited. The most recent year with substantial Federal Transit Data is 199413. Since then, data has either been unavailable or piecemeal. We are still hoping to get a more extensive set of data for current and recent operations from SLO Transit. However, we also have limited cost and ridership information from March 1999. We can use this data to make some general remarks on the current status of the agency.

A summary of the most useful information available for 1994 can be found in Box 1 below.

Box 1: Operating and Ridership Information, SLO Transit, 1994

Vehicles Operated in Maximum Service Vehicles Available for Maximum Service Annual Scheduled Vehicle Revenue Miles Annual Vehicle Miles Annual Actual Vehicle Revenue Miles Annual Vehicle Hours Annual Vehicle Revenue Hours Annual Unlinked Passenger Trips Annual Passenger Miles 9 15 225,700 239,400 225,100 25,400 22,800 694,700 2,185,300

From this data, we can get a sense of ridership and the size of the fleet for 1994 for a preliminary comparison with our 1999 data. Using this data, we can calculate average monthly ridership to be 57,890. Also, we are able to estimate monthly vehicle miles to be 19,950. Monthly vehicle hours are 2117 per month.

It is also possible to view the total number of buses in service, as well as the size of the fleet for SLO Transit. These numbers will be more important when compared with the 1999 data.

A comparison of the data from 1994 with our sample month of March 1999 may be insightful. A table of values for the month is shown in Box 2 below.

Box 2: Operating and Ridership Information, SLO Transit, March 1999

Vehicles Operated in Maximum Service Vehicles Available for Maximum Service Monthly Vehicle Miles Monthly Vehicle Hours Monthly Unlinked Passenger Trips 8* 14* 28,775* 2674* 80,319*

*Does not included trolley service


In large part, these numbers reflect the growth of transit ridership in the region over the five-year period between observations. While one must be careful not to overemphasize the increased monthly ridership when school is in session, it would seem that the monthly ridership in excess of 80,000 would lead to an overall increase in total ridership for the 1999 year.

It is also important to notice that the monthly vehicle miles have increased greatly, approximately 45% over the five-year period. Also, vehicle hours of service increased 26% over the same period. It is important to keep in mind that all of this is achieved with essentially the same operating fleet as from five years before.

It is important to notice that March is a time when school is in session at Cal Poly, not to mention local elementary and high schools. Nevertheless, if one only considers ridership based on a nine-month year as it relates to the school year, ridership, vehicle miles and vehicle hours would still be greater than for the entire twelve-month period of 1994. For the data available, we can make some generalizations about the ridership for SLO transit. For the month of March 1999, roughly 60% of all riders were students (student information is not broken down by which school a student attends). This fact is very important for our future examination of SLO Transit, as the impact of the students on the system is pronounced. While the number of tourists visiting San Luis Obispo is higher in the summer, this group is not likely to utilize the transit system available to them, except for the free, local shuttle. As a result, data collected will be very sensitive to the time of year in which it was collected.

Other useful information obtained for March 1999 included ontime performance for SLO Transit. Schedule adherence checks were performed 293 times during the month,

including weekends. While we do not know the permissible lag time allowed by SLO Transit, it is likely between five and ten minutes. Only four buses reported late during the month on their check-ins, or 1.365% of the time. This would suggest that overall, the system is performing in a timely fashion. Given that these ontime records come from a month in which school was in session and ridership is high, it seems likely that it is a good reflection of SLO Transit’s overall ontime performance.

Cost information for the month was also included. Laidlaw billed the city $95,298 for the month's services supplied, which were offset by only $8907 in farebox revenue. A fixed fee of $37,163 constituted more than a third of the month's bill. Total miles driven for the month were 34,636 at a rate of $0.62355 per mile. Also, 2799.01 payroll hours for the 25 employees were charged at a rate of $12.64 per hour. There is some small inconsistency here between the final charges to the city and the billing information available. A further examination of the billing system and charges will be required.


Transit Authority. Therefore, all data for this project must be obtained directly from, and with the consent of, Laidlaw, SLO Transit’s contractual operator.

3.2 Route Operation Information

Initial baseline data collection was conducted in late spring, 2000, during the closing weeks of the Cal Poly academic year. Table I provides a list of the 33 runs which were observed. The observations followed a schedule designed to provide balanced across the six different routes, days of the week, and times of the day. An example of the data collection schedule for Route 1 appears in Figure 1. There are four weekly runs targeted for each time period, giving a total of 12 runs per route and, therefore, 72 targeted runs for the six-route system.14

We propose to conduct a second baseline data collection effort as soon after the beginning of the fall quarter as possible, to fill in the gaps remaining from the spring effort and provide some replication of the baseline data. Ridership conditions in the fall could be somewhat different than in the spring due to the presence of brand new students and the generally higher university enrollment in fall. Unfortunately, neither the spring nor fall baseline data collection efforts are able to observe the system operating under bad weather conditions, when buses are more congested and driving more difficult.

Notwithstanding the presence of the target schedule, student workers were allowed to make some adjustments in order to mitigate scheduling conflicts to the extent possible. For this initial data collection, this is acceptable. Subsequent data collection will be more constrained since the future work should duplicate the particular runs (days and times) observed during this initial fieldwork.

Data were collected by students supplied with forms customized to the routes they were riding. Student field workers were instructed to synchronize their watches with standard telephone time to ensure that arrival time measurements would be consistent. While riding the bus, students recorded the time the bus arrived at the each stop (when the door opened) and the number of passengers boarding and alighting. At certain stops,

specifically the beginning of runs and layover locations, the departure times were also recorded (when the bus started moving). No data were recorded In the event that a stop was skipped for whatever reason. Student workers were also instructed to make notes of any unusual circumstances, which might affect the bus operation.

The data were summarized to show total boardings per run (see Table I) and to develop schedule-keeping diagrams for each run (see examples in Figure 2 through Figure 7). Loading diagrams, showing "ons" and "offs" and the load on the bus as a function of location along the route can also be prepared, if needed. The schedule-keeping diagrams, which are shown as examples are among the more interesting cases of buses deviating


The spring 2000 data collection effort fell short of the target number of runs due to difficulties finding enough student workers as final exams approached, scheduling conflicts of the students who were

available, and the fact that the data collection was undertaken on short notice and against the deadline that all work be finished by the last day of school on June 9.


from the published schedules, from among the 33 runs, observed. These limited data show that schedule deviation is not a rare event for the SLO Transit operation. As yet, no effort has been made to search for systematic patterns in the deviations from the

schedule, which were observed.

These data act as a baseline for seasonal demand when Cal Poly is not in session. The additional data collected in the September/October period, when Cal Poly is in session, will complete the baseline operational information.

Monday Tuesday Wednesday Thursday Friday


7:30 Start 7:26 Start 7:26 8:00

8:30 End 8:26 End 8:26 Start 8:26 Start 8:26

9:00 9:30 End 9:26 End 9:26 10:00 10:30 Start 10:26 11:00 11:30 End 11:26 Start 11:26 12:00 12:30 End 12:26 Start 12:26 13:00 13:30 End 1:26 Start 1:26 14:00 14:30 End 2:26 15:00 15:30 Start 15:26 Start 15:26 16:00

16:30 Start 16:26 Start 16:26 End 16:26 End 16:26


17:30 End 17:26 End 17:26 18:00


Table I. Summary of Onboard Data Collection -- Spring 2000





Wed. 05/24 10:22 47 Thu. 05/25 10:21 37 Fri. 05/26 13:25 47 Wed. 05/31 10:26 50 Wed. 05/31 08:25 34 Thu. 06/01 08:27 40 Mon. 06/05 07:28 23 Wed. 06/07 07:27 22 Thu. 05/25 13:28 57 Fri. 05/26 11:26 54 Tue. 05/30 16:29 47 Tue. 05/30 11:32 33 Wed. 05/31 09:28 36 Wed. 05/31 16:28 33 Thu. 06/01 15:28 56 Fri. 06/02 15:31 48 Tue. 05/30 13:32 44 Wed. 05/31 11:37 18 Wed. 05/31 15:30 73 Thu. 06/01 13:31 43 Thu. 06/01 15:29 56 Fri. 06/02 15:30 50 Tue. 05/30 07:55 21 Wed. 05/31 07:55 22 Thu. 06/01 07:58 22 Tue. 05/30 13:56 85 Wed. 05/31 12:52 60 Thu. 06/01 11:25 38 Fri. 06/02 09:55 79 Tue. 05/30 12:41 60 Wed. 05/31 10:42 41 Thu. 06/01 11:42 74 Fri. 06/02 13:42 53








Figure 2. Sample Schedule Keeping Diagram for Route 1 Schedule Keeping - Route 1 - Mon. 6/5/00

6:43 6:57 7:12 7:26 7:40 7:55 8:09 8:24 8:38

Osos @ Palm Broad @Islay

Broad @

Johnson @ Laurel Ln. Augusta @ Johnson @

Osos @ Palm N. Chorro @

Cal Poly

Highland @ Patricia @ Foothill @ Foothill @

Osos @ Palm


Arrival Schedule

Figure 3. Sample Schedule Keeping Diagram for Route 2 Schedule Keeping - Route 2 - Tues. 5/30/00

4:04 4:19 4:33 4:48 5:02 5:16 5:31 5:45

Osos @ Palm (City Ha... Nipomo @ MarshPismo @ Acher

Higuera @ Bridge (C...Higuera @ Granada ...Higuera @ Tank Far...Los Osos Valley Rd. ... Higuera @ Prado Higuera (Chumash Tr...

Marsh @ ArcherMarsh @ Chorro Santa Rosa @ Higuera

Mill @ JohnsonMill @ Grand Grand @ McCollum

Cal Poly (UU) Cal Poly (Mustang St...

Foothill @ Ferinni Tassajara @ Ramona

Foothill @ Chorro Osos @ Palm (City Ha...


Arrival Schedule


Figure 4. Sample Schedule Keeping Diagram for Route 3 Schedule Keeping - Route 3 - Fri. 6/2/00

1:12 1:26 1:40 1:55 2:09 2:24 2:38 Osos @

Foothill @ Cal Poly Grand @

Mill @

Osos @

Johnson @ Augusta @ Augusta @ Laurel Lane Orcutt @ Tank Farm Tank Farm Broad @ Broad @ Broad @ Broad @ Chorro @ Chorro @

Osos @


Arrival Schedule

Figure 5. Sample Schedule Keeping Diagram for Route 4 Schedule Keeping - Route 4 - 6/1/00

7:48 7:55 8:02 8:09 8:16 8:24 8:31 8:38 8:45 8:52 9:00

Osos @ Palm (C...Santa Rosa @ ...Santa Barbar @... South @ King

Madonna @ US...Central Coast Ma..Madonna @ LO..LOVR @ Ocean...LOVR @ Descanso Valle Vista Foothill @ La En...Ramona @ Pal...

Foothill @ Casa Cal Poly (Mott ...

Grand @ WilsonMill @ California Osos @ Palm (Ci..

Time Arrival


Figure 6. Sample Schedule Keeping Diagram for Route 5 Schedule Keeping - Route 5 - 5/30/00

1:48 1:55 2:02 2:09 2:16 2:24 2:31 2:38 2:45 2:52 3:00

Osos @ Palm (Ci..Mill @ CaliforniaGrand @ Abbott Grand (Vista Gra...Cal Poly (Graphi...

Mustang VillageFoothill @ FerriniFoothill @ Rosita Valle Vista

Perfumo @ Del RioLOVR @ DescansoLOVR @ OceanaireMadonna @ Oce...Madonna @ US 1..South @ Beebee... South @ King Santa Barbara @...


Santa Rosa @ B...Osos @ Palm (Ci..


Arrival Schedule

Figure 7. Sample Schedule Keeping Diagram for Route 6 Schedule Keeping - Route 6 - Thurs. 6/1/00

11:31:12 11:38:24 11:45:36 11:52:48 12:00:00 12:07:12 12:14:24 12:21:36 12:28:48 12:36:00 12:43:12

Osos @ Palm...Foothill (Unive...Foothill @ Cu...Ramona @ P...Foothill @ CasaCal Poly (Mot...Grand @ WilsonMill @ CaliforniaOsos @ Palm...Higuera @ C...Higuera @ Su...Higuera @ P...Higuera (Ch...Marsh @ ArcherMarsh @ ChorroSanta Rosa ...

Time Arrival



approach if the processes are also of interest.15 A second approach is to collect

information on a number of transit agencies, a subset of which have adopted AVL, and assess statistically the impact of AVL on the costs and revenues of the firm. It is the second approach, which is selected in this research first because of time constraints in conducting interviews at selected transit agencies, and secondly, the case approach will be used in phase II.

4.1 Data Envelopment Analysis of Transit Efficiency with AVL

To assess the impact of AVL on transit efficiency there are a couple of methods that can be used. One approach is to construct productivity indexes using total factor productivity measures (TFP) while a second is to use DEA. We selected the latter for a number of reasons. First, DEA has been used on a few occasions to investigate transit efficiency but not to assess AVL. Second, DEA provides a natural method for measuring shifts in a technology frontier and AVL represents a technological shift. Third, DEA does not place any a priori assumptions on the behavior of the transit agency while TFP does. Therefore, it is possible to explore the impact of AVL on transit agencies which have different operational objectives such as maximizing passengers or service levels. Finally, DEA is flexible enough to allow for differences in the nature of the production function,

specifically allowing for both constant as well as variable returns to scale.


DEA is a mathematical programming model applied to observed data that provides a new way of obtaining empirical estimates of production functions and/or efficiency

production possibility surfaces (Charnes et al, 1978). More simply put, DEA is a

technique that enables researchers and managers to understand the efficiency of resource use in the production process relative to that of other similar and dissimilar operational units. Using DEA, they can assess how efficient units are overall, how efficient their use of any given factor is, and how units compare to each other.

DEA was pioneered in the late 1950’s, but was not widely used for several decades. However, the body of academic literature created using DEA is now quite large, and the method has proved effective in a number of fields. A significant factor in this growth is the fact the DEA does not require adherence to traditional economic assumptions that operational units are cost minimizers or revenue maximizers. This makes it a particularly effective tool for analysis of public sector and non-profit organizations, in which these rules rarely hold and might skew interpretation of important data, and drives our interest in using DEA here to analyze municipal transit agencies.

Coelli et al (1998) notes several key advantages of the DEA frontier approach, which make it a particularly good tool for our analysis of bus systems and Automatic Vehicle Location technology. First, the frontier method does not rely on pricing information to determine the efficiency of a unit. This is helpful for us because prices for bus rides generated are hard to define and revenue-maximizing is not a typical goal of public


The case study method will be used in this research in Phase II when we undertake an examination of SLO transit as well as two other agencies that have adopted AVL.


transit. Second, unlike classical economic techniques, DEA does not assume that firms are efficient, rational, and perfectly informed, and public transit authorities as a

population are not characterized by any of the three. Indeed, the goal of AVL and our study here is to address and deal with these issues – in solving a problem, recognizing that one exists is always a good starting point. Third, DEA analysis needs not assume any specific normative goal to work. As long as Constant Returns to Scale are taken as given, DEA results are descriptive and useful without being objective-specific. Finally, and perhaps most importantly, DEA discriminates between efficiency gains (or losses) caused by changes in technology and those which spring from better uses of technology. For our study, the ability to separate effects of technical change (AVL application) from effects of technical efficiency change (operational practice) is very important.


Graphically, DEA analysis constructs a Production Possibilities Frontier for productive units with those units found to be most efficient on the boundary itself. These units are defined as 100% efficient and all other units are scored based on their position within the frontier – those close to the origin are inefficient and get low scores; those close to the boundary are more efficient and get scores approaching 100%. This is easy to understand when there are only two inputs and a single output, or two outputs and a single input – it makes up a familiar two-dimensional graph. When more outputs and/or more inputs are considered, as is usually the case, DEA analysis operates in an n-dimensional space that can only be represented mathematically – there is no easy graphical analogue, but accurate results are obtainable and useful. Familiarity with the functional form of DEA methods helps us understand both where our efficiency “scores” come from and what they mean.

DEA provides a scalar measure of relative efficiency by comparing the efficiency achieved by a decision-making unit (DMU) with the efficiency obtained by similar DMUs. The method allows us to obtain a well-defined relation between outputs and inputs. In the case of a single output this relation corresponds to a production function in which the output is maximal for the indicated inputs. In the more general case of multiple outputs this relation can be defined as an efficient production possibility surface or frontier. As this production possibility surface or frontier is derived from empirical observations, it measures the relative efficiency of DMUs, which can be obtained with the existing technology or management strategy. Technological or managerial change can be evaluated by considering each set of values for different time periods for the same DMU as separate entities (each set of values as a different DMU). If there is a significant change in technology or management strategies this will be reflected in a change in the production possibility surface.

Figure below attempts to illustrate the basic notion behind DEA.16 First consider the five DMUs labeled P1 through P5. All DMUs produce one unit of output using different


This locus can be thought of as a unit isoquant. Since it is piece-wise linear, it implies that the marginal rate of technical substitution is fixed along each of the two linear segments. A point like P4 is inefficient compared to p1, since the former uses one more unit of x2 to produce the same output as the latter. By similar reasoning, p2 is an

inefficient point. The ‘base’ DEA model expresses inefficiency purely in terms of output slacks and excess inputs. The airport manager’s question is answered simply as – use one less unit of input 1. Thus both DMUs P2 and P4 can become efficient by eliminating excess inputs (as shown by the arrows). However, this is not the only way of getting to the frontier. DMU P4 can reach the frontier by a proportional reduction in the use of both factors of production. The ‘input-oriented’ DEA model allows the determination of the amount of proportional reduction required. Thus P4 can move a long the ray through the origin to P4 which lies in the cone formed by the two reference points p1 and P5. P4 is a weighted average of P5 and P1 and the weights are positive since it lies in the cone. Orientation in DEA models imposes restrictions on the production technology, as does piece-wise linearity. The usual smooth, twice differentiable, convex to the origin isoquants yield unique and interior solutions. Corners, kinks and holes typically lead to problems. In addition, moving down a ray from the origin implies that choice of technique (or relative input intensity) does not change. In a situation where there are various factors of production, whether a proportional reduction is possible depends upon the production technology. In DEA relative efficiency is quantified using the distance of a particular DMU from the frontier. The measure is a ratio of radial distances thus the efficiency score for P4 would be O P4/O P4. According to this measure, an efficient unit would be accorded a score of unity.

Figure 8

The efficient DMUs P3 and P5 form a cone, which is also sometimes referred to, as the base region. How is a DMU such as P6 that lies outside the base region evaluated? DMU P6 cannot be compared to a weighted average of two efficient DMUs, as was the case for P2 and P4. Clearly, compared to P3, P6 is inefficient as it uses an extra unit of


input x1. The efficient region is extended in parallel to the x-axis by joining the points P3 and P6, but the two cannot be given an efficiency score of 1. In order to get around this problem, the excess input at P6 is accorded a positive weight in the optimization problem. The weight is a small positive number referred to as the ‘non-Archimedean epsilon’. Thus in ‘base’ models projected input used equals actual input use less any excess inputs. In the ‘input oriented’ model there are two sources of inefficiency, one that can be eliminated by a proportional reduction in input use and a residual or excess input.17 Total inefficiency equals residual inefficiency at the point P6.18 In view of DMUs such as P6, a DMU is efficient if its efficiency score is unity and there are no output slacks or excess inputs.

At this point we have addressed only the first part of our questions. We have a measure of relative efficiency and we can rank transit firms by year and to each other. This is one of the significant benefits of the DEA measure in that it is useful for both inter-temporal as well as cross-sectional comparisons. However, it is also our purpose to explain the variation in performance measures both over time and across firms and to assess the contribution of AVL to firm efficiency. To do this we develop a set of variables for each transit firm that allows us to see the net contribution of AVL.

To obtain the true or net efficiency index we undertake a second stage of analysis. In this we utilize the efficiency index generated from the DEA methodology and use it as the dependent variable in a regression in order to identify the variables which affect efficiency. The DEA efficiency measure has an upper bound of 1 or, as we have done here, if one take natural logs of the efficiency measure, a lower bound of zero. Thus we use the censored regression or Tobit model. The standard Tobit model can be

represented as follows for observation i: y y y y y y i i i i i i i i * * * * = ′ + = ≤ = > β x ε if if 0 0 0 (1)

The estimated coefficients of the Tobit model do not provide the marginal effects. The marginal effect for variable j is derived as follows:19

β E yi j j i x = Φ (2) 17


In addition, conditional marginal effects are also reported. The conditional marginal effect for variable j is derived as:








E y y

i i j j i i i i i














In the above two equations, φi and Φi are the respective probability and cumulative density functions of a standard normal variable evaluated at β′xi/σ . Since a unique marginal (and marginal conditional) effect can be calculated for each observation, we report the mean of the marginal. effects.


This leads us to a discussion of the computer efficiency program used in our DEA study: Banxia Frontier Analyst is a computer efficiency program designed to make Data

Envelopment Analysis easy and equation free for the user. Taking a data set broken into organizational units, each with its own values for selected input and output variables, Frontier Analyst quickly and easily provides a comprehensive analysis of productive efficiency for each unit, ranks them, and identifies points of inefficient resource use. Coelli (1992) points out two major advantages to this method of analysis. One is that it is more closely related to ‘the theoretical definition of a production function, which relates to the maximum output attainable from a given set of inputs.’ Also, that the efficiency estimates may be obtained by ‘comparing the observed output with the predicted (or attainable) output. The program, he continues, will obtain the maximum likelihood estimates of the stochastic frontier production function.

Using DEA methods, Frontier Analyst gives efficiency scores in a 0-100% range, with the most efficient unit in the study set as the base-case at 100%. Other units are ranked with respect to this unit – or units – and Frontier Analyst identifies which inputs are inefficiently used, which outputs are inefficiently produced, and explains how much each contributes to the overall inefficiency of the unit. These figures allow a manager or researcher to know not only who produces efficiently, but also how and why.

The Frontier Analyst User’s Guide and T.J. Coelli (1998) identify several important steps to conducting an effective DEA analysis. First is deciding, defining and selecting the factors to be considered. Then, a time frame must be examined for the study, such that it covers an adequate length of time to capture the possible changes in efficiency for the firms under consideration. The data are broken down into inputs and outputs of the production process and constitute the substance of the analysis, the data panel from which all conclusions are drawn. Selecting relevant and sufficient data to consider is necessarily critical to a useful DEA study, and only factors that have some bearing on the production process should be included. Important qualitative or environmental factors such as

geographic location or skill level of labor can be added to the analysis if they are assigned numerical values or represented by an appropriate proxy variable such as distance from a


port or average years of schooling. Finally, care should be taken that any single factor in the analysis does not convey the same information as another, since this redundancy has the effect of amplifying the influence of the forces at work behind both inputs.

Once the relevant data for DEA have been selected, the correct model orientation must be chosen to best analyze them. A key decision is if the data should be analyzed with

Constant Returns to Scale (CRS) or Variable Returns to Scale (VRS) in mind. A CRS orientation assumes that factor productivity within units and between units does not vary with the scale of the production process; that is, insofar as technology and current

operating practices remain constant, so too will efficiency, regardless of the quantities involved. This has the consequence that conclusions drawn from DEA will remain applicable as the scale of a firm grows or shrinks.

On the other hand, a Variable Returns to Scale orientation assumes that as the scale of production of a unit changes (or as it is compared to a unit of different size), so too will its return to inputs. In this case, a doubling of inputs yields a higher or lower than double proportion of outputs. Under this assumption, units can only be compared to others of similar scale, and conclusions drawn from DEA may not be true as a unit’s output changes.

Choosing between CRS and VRS models is dependent on the industry or production process being considered. In most situations, a CRS analysis is preferred, since more flexible information is attained. In others, such as when coordination problems arise with a large organization, a VRS orientation is more appropriate. Due to the varying sizes of the agencies considered in our study, a CRS assumption appears to be the most logical. Our results will be examined to see if this is indeed the case.

Another critical decision is whether to orient DEA around Input Minimization or Output Maximization. An input-oriented analysis considers how much a firm’s inputs could potentially be reduced, while maintaining current output levels. In essence, it identifies which inputs are under-utilized, and by how much. The alternative is output-oriented DEA, which seeks the maximum possible output given current input levels.

Under CRS, efficiency results are identical for output and input-oriented analyses – since scale has no bearing on efficiency. However, under VRS, these conclusions will differ because returns to scale are assumed to be different at a hypothetical, higher output level than they are at the current one and this affects the basic foundation of the analysis. Consequently, under VRS, DEA and Frontier will give yield different figures and insights depending on the goal of the study – cost-reduction or output maximization.


transportation sectors. Gillen and Cooper have completed work on airport efficiency. Parker examined the role of the British Airports Authority’s privatization of airports using DEA as well. S. Sjogren used DEA to examine decision support at transport terminals.

Given this growth in the use of DEA, it is not surprising that we find a number of studies of U.S. public transit using DEA analysis. Fielding Brenner and Faust (1985) combined peer groups with service performance indicators to create the Irvine Performance

Evaluation Methodology (IPEM). These measures allow the performance of one agency to be examined over time in comparison to other, similar agencies. Yu (1988) added census data to reflect social and demographic characteristics. IPEM was used by Perry and Babitsky (1986) to examine the relative efficiency of differing ownership and management forms among transit agencies. However, the IPEM system is still made up of numerous indicators, and since these cannot be summed, they proved difficult to use in comparison between firms. Talley (1988) and Lee (1989) both proposed additional indicators for transit evaluation, but the need to simplify these measurements prompted additional work.

Chu, Fielding and Lamar (1990), in their article Measuring Transit Performance Using Data Envelopment Analysis, selected 42 public transit agencies to examine their

performance. In this work, they made an important distinction between efficiency and effectiveness. Efficiency here is referred to as the ‘relationship between inputs and outputs of what is referred to as “productive” or “technical” efficiency in the economics literature.’ Therefore, in this study, efficiency is examined using produced services (ex, vehicle revenue miles or hours) as a function of various inputs (ex, labor hours and total number of buses). On the other hand, effectiveness refers to the ‘use of outputs to achieve objectives, or service consumption.’ Since transit agencies do not act like

competitive firms, a firm’s effectiveness may be as important to its efficiency. After all, what good is a highly efficient agency with low ridership (effectiveness)? Effectiveness scores use services consumed (ex, passenger trips) as a function of the inputs to the system, which would now include service supplied. The examination of efficiency and effectiveness is very important, as they examined both the minimization of costs (efficiency) and the maximization of ridership (effectiveness). The authors cite Hatry (1980), who noted that while the desire to produce transit services as economically as possible, ‘it is only one of a broad set of social and economic goals’ that must be achieved. The paper’s goals included creating a system to help regulators assess

agencies’ operations. By creating two scales for evaluation, the authors are able to better review an agency’s operations. The authors also suggested the need to break down the agencies into peer groups based on regional population, which in turn affected fleet size and peak operations.

Since the work of Chu, Fielding and Lamar, the most notable DEA and transit work was published in 1997 by Obeng, Azam and Sakano. In their book Modeling Economic Inefficiency Caused by Public Transit Subsidies, the authors examine transit firms’ performance using DEA as it relates to government subsidies. This paper points out many of the reasons why one cannot evaluate transit performance based solely on profit


maximization like a ‘normal’ DMU. Since the government may actually require certain levels of service, either in miles provided or level of safety for example, each firm is limited in their set of choices. In this case, a DMU would be required to meet a given quantity of service (perhaps in combination with a level of service quality), at the lowest possible cost. Therefore, even if a firm is efficient given the requirements, it may find itself losing money. As such, these firms can be seen as cost-minimizers given a certain level of output required. When DMUs fail to minimize their costs, they are said to be technically inefficient. In this case, some input is being underutilized and keeping the firm from reaching its appropriate level of output. These inefficient firms would

normally improve or close with competition. However, given the local monopoly power that transit agencies hold, this will not happen. Instead, each agency will try and do the best it can with what it has available.

The report found that technical inefficiency for the average firm was 16.1% compared with 33.26% for the non-frontier method employed. The inefficiency was seen to drop with increased firm size. In this examination, both DEA and stochastic frontier methods produced similar results.


Firms were considered for selection based on their use of AVL, their comparability with an AVL agency and the availability of useful data for the agency. Our final list contained twenty-three agencies, with data for each agency covering the years 1988-1997. Data for each agency was collected from the Federal Transit Authority (FTA). The Section 15 reports later referred to as the National Transit Database, contain financial and

operational information for all transit agencies in the country, regardless of size or location. This information is available in print, and it is also available for download for years after 1992.20

Before we reached the final list of agencies, we examined a much larger list of possible agencies. Information regarding agency use of AVL was based on information from the Synthesis of Transit Practice 24: AVL Systems for Bus Transit, Advanced Public

Transportation Systems Deployment in the United States, and Advanced Public Transportation Systems: The State of the Art, Update 1998. We were able to find the agencies that had AVL systems up and running on a regular basis. This report focused on the use of AVL on fixed route motor bus services. The use of AVL in direct response services was not covered here to maintain consistency.


Table 2

Transit Agencies Included in Study

FTA Indentification Number State Agency Name AVL Carrier

0001 WA Seattle-Metro Yes

0011 ID Boise Urban Stages No

1001 RI Providence-RIPTA No 1066 VT Burlington-CT No 2113 NY Rochester-RTS No 3034 MD Baltimore-Maryland-MTA Yes 4027 FL St. Petersburg-PSTA No 4029 FL Ft. Lauderdale-Bct No 4032 FL Daytona Beach-VOTRAN No 4041 FL Tampa-Hartline Yes 4046 FL Sarasota-SCTA No 5012 OH Cincinnati-SORTA No 5016 OH Columbus-COTA No

6011 TX San Antonio-VIA Yes

7003 MO Springfield-CU No

7005 MO Kansas City-KCATA Yes

7006 MO St. Louis-Bi-State No

8001 UT Salt Lake City-UTA No

8006 CO Denver-RTD Yes

9008 CA LA-Santa Monica Yes

9013 CA San Jose-SCCTD No

9016 CA SF-Golden Gate No

Agencies Included in AVL Study

A review of these articles offered important information regarding the installation status and use of various AVL systems. For example, we eliminated San Francisco's MUNI from consideration when we discovered MUNI only uses its AVL systems for emergency response, not for planning or data collection. As well, we were able to find out the

number of years that a system was employed and on the number of buses. This

information helps us understand the stage of AVL implementation. In Baltimore, there were both a preliminary test of a Loran-C AVL system and the installation of a full GPS system at the end of 1996.

These articles provided the AVL technology employed, the year it was introduced and the number of buses equipped with AVL. With this information, we were able to create three columns of information. The column AVL dummy shows what years AVL was

employed, where a One shows that AVL was in place. The second column which technology was used, information used in secondary regressions to compare the effect of the different technologies implemented. The final column shows the amount of buses using AVL, such that partial deployments can be analyzed.


Agencies for consideration also need to be examined based on their comparability. Criteria included network size, regional population, geography, and size of agency. The FTA organizes reports for agencies by the size of their 'urbanized area' (UZA). As a result, a broad range of agencies was included in the final data set. The fact that Seattle and Santa Monica are both AVL carriers are reviewed (over 800 and approximately 125 buses respectively) requires that many agencies of varying sizes be included for


The main reason agencies were not included in the final data set came from shortcomings in the available data. Unfortunately, two firms using AVL had to be excluded from consideration due to inadequate labor input information. Other agencies to be included for comparison also were also removed due to data shortcomings. Chapter 2 of the National Transit Database warns, "analysis of expenses by function must be qualified by the degree to which transit agencies uniformly allocate expenses among the various functional categories". The fact that we encountered agencies with unacceptable vehicle-operations information is, therefore, not surprising.

Table 3

Agencies Excluded from Study of AVL Agency ID


State Agency Name Reason for Exclusion

2076 NY Westchester Co. DOT Incomplete vehicle-operation cost inputs, SECTION 15 TABLE 3.08.1. AVL user.

3023 PA Beaver County TA Incomplete vehicle-operation cost inputs, SECTION 15 TABLE 3.08.1. AVL user.

9015 CA San Francisco MUNI AVL system only used for emergency response

9016 ME Portland-METRO No fleet information, non AVL

2010 NY Poughkeepsie-LOOP No fleet information, incomplete labor information, non AVL

2080 NJ New Jersey Transit No fleet information, non AVL

2010 NY Clarkston Mini-Trans No fleet information, incomplete labor information, non AVL

3042 MD Hagerstown Commuter No fleet information, incomplete labor information, non AVL

6016 TX Beaumont-BMT No fleet information, incomplete labor information, non AVL

9030 CA San Diego-NCTD No fleet information, non AVL

The data for each transit agency was developed from FTA Section 15 reports. Information on employee work hours is broken down into four categories: vehicle operations, vehicle maintenance, non-vehicle maintenance, and general administration. While the precise allocation of work hours among these categories may vary among agencies, they do reflect the relative importance of the labor inputs. This information can


of tires and maintenance parts for the equipment. Since many agencies use both diesel and gasoline, fuel consumption was converted into BTUs (British thermal units). This table also offered information on additional expenses, such as utilities paid fringe benefits and insurance costs. Purchased transportation, also included, is an expense some agencies incur due to contracting services. In these cases, private firms are employed by agencies to help offer a greater quantity and quality of service.

Information regarding the capital stock of the agencies in question came from the "Transit Vehicle Data Book, 1998" published by the American Public Transit

Association. Model, make, year, length and seat information is available for nine of the years in question; however, we did not have the 1988 information, so a least squares regression was used to estimate these values. From the data supplied, a summation of total vehicles and total seats was created for each year of the study. This allows two different input measurements for consideration: total vehicles per year and total seats per year.

Lastly, the data for the output measurements (vehicle revenue miles, passenger trips and passenger miles) come from the 'Transit Service Supplied' table in the National Transit Data base.

Graphs of inputs and outputs for TFP calculations have been placed in an appendix at the end of the report. Included below are some of the trends observed in the data, as well as a description of the TFP indices for non-AVL agencies.

4.2 Estimates of Transit Firm Efficiency Using DEA

The first step in the analysis was to measure the efficiency of each transit firm using the DEA methodology. We examined both variable and constant returns to scale production processes but used the input minimizing approach. Recall this provides a measure of how much a transit firm could reduce their inputs to produce the same level of output, relative to the most efficient agencies. This fits most reasonably with the hypothesis we are exploring reading the contribution of AVL to firm efficiency.

Table 4 and Table 5 illustrate the values of the DEA index for all transit firms included in the sample. The most efficient firms will have an index of 1 or 100 percent efficient. The efficiency indexes are reported for three different output definitions; revenue vehicle miles, passenger miles and passenger trips. The indexes are generated using Hours of Vehicle Operation, Hours of Vehicle Maintenance, Hours of Non-Vehicle Maintenance, Hours of General Administration, BTU Mil, Quantity of materials used and Total Seats Available ( a measure of capital used) as the inputs in the DEA function.


Table 4

DEA Efficiency Measures

0 20 40 60 80 100 120

Seattle 1989 Seattle 1992 Seattle 1995 Boise 1989 Boise 1992 Boise 1995

Providence 1989 Providence 1992 Providence 1995 Burlington 1989 Burlington 1992 Burlington 1995 Rochester 1989 Rochester 1992 Rochester 1995 Baltimore 1989 Baltimore 1992 Baltimore 1995 Daytona 1989 Daytona 1992 Daytona 1995

Tampa 1989 Tampa 1992 Tampa 1995 Sarasota 1989 Sarasota 1992 Sarasota 1995


Table 5

DEA Efficiency Measures

0 20 40 60 80 100 120

Cincinnati 1989 Cincinnati 1992 Cincinnati 1995 Columbus 1989 Columbus 1992 Columbus 1995

San Antonio 1989 San Antonio 1992 San Antonio 1995 Springfield 1989 Springfield 1992 Springfield 1995 Kansas City 1989 Kansas City 1992 Kansas City 1995

St. Louis 1989 St. Louis 1992 St. Louis 1995

Salt Lake City 1989 Salt Lake City 1992 Salt Lake City 1995

Denver 1989 Denver 1992 Denver 1995

Santa Monica 1989 Santa Monica 1992 Santa Monica 1995

San Jose 1989 San Jose 1992 San Jose 1995

San Francisco 1989 San Francisco 1992 San Francisco 1995

Transit Firms and Year (1989-1997)

Value of Efficiency Measure


The first point to make is the significant variation among the transit firms despite their being relatively the same size in terms of passengers carried. Furthermore, the transit firms efficiency is different, considerably in some cases, when different outputs are used. Second, vehicle revenue miles always produce an efficiency measure, which is higher than the other two. This is a produced measure of output not a 'sold' measure of output. In other words, it tells us nothing about the effectiveness of the vehicle miles that are

produced. It appears to be very clear that transit firms are relatively efficient at putting transit vehicles on the street but not particularly efficient at ensuring people are in the buses. Third, when passenger trips or passenger miles are used as the output measure we see this decreasing in the later years. As the economy has improved, we find there is a drift away from public transit use. It might therefore be thought of as a Giffen good.21

These indices are gross measures in the sense they incorporate all the influences, which can lead to more or less efficiency in production of the output of the agency. Therefore, the DEA measures cannot be directly compared between transit firms because there may be some aspects of their operating environment that may cause the efficiency index to be higher or lower. The intent is to net out these influences to obtain a pure measure of efficiency. In the process we are able to introduce an AVL variable that provides an indication of the extent to which AVL contributes to productive efficiency of the transit agency.

4.3 Measuring the Impact of AVL

There are three measures of DEA used in this analysis. DEAVRM uses vehicle revenue miles as the output unit, DEAPM uses Passenger Miles and DEAPT uses passenger trips. The DEA regressions were estimated for each of these output measures. We found that using passenger trips provided no information regarding the level of productivity or efficiency, we therefore report our results for revenue passenger miles (Table 6) and revenue vehicle miles (Table 7) provide the empirical results from the estimation of a linear model of DEA on environmental, structural and managerial variables.

In both tables the R2 is relatively high indicating that approximately 80 percent of the variation in the dependant variable, DEA productivity index is accounted for by the set of variables included in the regression. The set of variables includes Vehicle Miles as a measure of output to take account of differences in both the size and utilization rate of capital. This variable is significant and has an elasticity value of 0.02.22 While the impact is small, it shows a 10 percent increase in passenger miles would increase TFP by

approximately .2 percent. Thus, the evidence seems to be that expanding passenger miles will not lead to large increases in cost efficiency. If we examine Table 7, the same variable has a quite similar value when the dependant variable is DEA index using Revenue Vehicle Miles as the measure of output. The variable Buses was used as a measure of the amount of capital stock. The negative value indicates the transit firms


unsurprising since transit firms invest in capacity for the peak hours and the buses are underutilized the remainder of the day.

The remaining variables reported in the tables are dummy variables. This means they are shift effects and depending on the sign of the variable they would increase or decrease the value of the regression equation. Transit specific dummies are listed for Baltimore

through St. Louis. The majority of the variables are statistically significant (have at value >1.6) and there is a mixture of positive and negative signs.

Table 6

TFP Regression Using Passenger Miles

Dependent Variable: DEAPAXM Sample(adjusted): 2 198

Variable Coefficient Std. Error t-Statistic

C 0.9495 0.0710 13.38 Buses -0.0019 0.0011 -1.68 AVL 0.0154 0.0088 1.75 Miles 0.0023 0.0011 2.12 BALTIMORE -0.6528 0.2533 -2.58 BOISE -0.2986 0.0930 -3.21 CHITT -0.4876 0.0905 -5.39 CINCINNATTI -0.5023 0.1284 -3.91 COLUMBUS -0.5823 0.1109 -5.25 DAYTONA -0.4145 0.0867 -4.78 DENVER -0.7255 0.2377 -3.05 FORTLAUD -0.3491 0.1079 -3.24 GOLDENGATE -0.1319 0.1045 -1.26 HILLS -0.6266 0.0908 -6.90 KANSASCITY -0.6639 0.1038 -6.40 PINELLAS -0.4601 0.0936 -4.91 PROVIDENCE -0.5135 0.1002 -5.13 ROCHESTER -0.6574 0.0927 -7.09 SANANTONIO -0.6903 0.1856 -3.72 SANJOSE -0.7264 0.1842 -3.94 SARASOTA -0.5711 0.0889 -6.42 SEATTLE -0.5151 0.3078 -1.67 SLCITY -0.6581 0.1622 -4.06 SPRINGFIELD -0.5871 0.0910 -6.45 STLOUIS -0.8098 0.2196 -3.69 AR(1) 0.6700 0.0643 10.42 Adjusted R-squared 0.813459 S.E. of regression 0.081102 Sum squared resid 1.124746 Log likelihood 229.2853


The variable of most interest is the AVL dummy. In both equations the variable is statistically significant and has a positive sign. This is a significant finding. It indicates that the introduction of AVL had a positive impact on transit firm productivity. We can also see that the positive productivity affect is larger when output is measured by Vehicle Miles than when it is measured by Passenger Miles, in fact the value of the coefficient is about 30 percent higher. One can speculate that in instances where the transit manager has an objective of maximizing service levels that AVL is of greater consequence since using extra resources is of more importance or concern. When the manager's objective function is to maximize passenger miles, it has to work through fare levels and service quality. Improving productivity and developing better service information can be obtained through the use of AVL.

The AR(1) and AR(2) variables reported in the tables are first and second order parameters from the auto regressive model used to control for serial correlation. The Durbin-Watson statistic should be around 2. If it is less than 2 it indicates positive serial correlation and a value >2 indicates negative serial correlation.

The type of AVL system put in place was also examined. Using a specification similar to that in Table 6 and Table 7, dummy variables identifying the different types of AVL technologies that different transit firms employed were used in the regression. In all cases for all measures of TFP there was no case in which a particular AVL technology was statistically significant. The regression contained both the AVL dummy variable

indicating that an AVL system was being used as well as the series of dummy variables identifying the particular technologies. The proposition was, 'does the use of AVL matter, and if so, does it matter which one that is used'? The answer to the first question we have seen is yes. We discovered the answer to the second question is no; at least with the data set employed here.

4.4 Identifying the Drivers of AVL on Cost Efficiency and Service

The TFP regressions provided the evidence that the AVL has a positive affect on productivity. This information would be used in a benefit cost study to account for the beneficial impact on productivity. However, from a management strategy perspective as well as public policy it is useful to identify 'how' the use of AVL may make a difference. We explored several cost and revenue relationships.


AVL, as noted at the outset, can provide information to transit managers that can lead to better customer information on when buses will arrive and how long a wait might be expected. This allows people to better plan the use of their time but also allays apprehension. It can provides a measure of service quality as well since reducing the variance in schedule adherence is a signal of higher commitment to customer service.