Analysis and Evaluation of Automatic Vehicle Location (AVL) for Maryland Transit Administration (MTA): Short-Term And Long-Term Strategies

Transcription

1 Analysis and Evaluation of Automatic Vehicle Location (AVL) for Maryland Transit Administration (MTA): Short-Term And Long-Term Strategies Young-Jae Lee, Ph.D. Assistant Professor School of Engineering Morgan State University Baltimore, MD National Transportation Center Montebello D-206 Morgan State University Baltimore, MD December

2 DISCLAIMER The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation, University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. 2

3 Technical Report Document Page 1. Report No. 2. Government Accession No. 3. Recipient s Catalog No. 4. Title and Subtitle Analysis and Evaluation of Automatic Vehicle Location (AVL) for Maryland Transit Administration (MTA): Short-Term And Long-Term Strategies 7. Authors: Young-Jae Lee, Asst. Professor, Engineering, Morgan State University 9. Performing Organization Name and Address National Transportation Center Morgan State University 1700 E. Cold Spring Lane Baltimore, MD Sponsoring Organization Name and Address Maryland Transit Administration 6 St. Paul St. Baltimore, MD Report Date 6. Performing Organization Code 8. Performing Organization Report No. 10. Work Unit No. (TRAIS) 11. Contract or Grant No. 14. Sponsoring Agency Code 15. Supplementary Notes 16. Abstract The AVL system that the Maryland Transit Administration (MTA) provides has many potential advantages for both operators and passengers on a short-term and a long-term basis. AVL systems can contribute to the optimization of vehicle operation, scheduling, and run-cutting for operators and reliable service and information for passengers. This research suggests the proper data to be collected from the system and shows the impact of the AVL system on scheduling adherence as short-term improvement. It suggests proper running analysis based on collected data and includes recommendations for scheduling changes for future operation. 17. Key Words 18. Distribution Statement No restrictions. This document is available to the public from the: National Transportation Center Morgan State University 1700 E. Cold Spring Lane Baltimore, MD Security Classification (of this report) 20. Security Classification (of this page) 21. No. of Pages 22. Price 3

4 TABLE OF CONTENTS Introduction 9 Introduction to AVL System 1.2 AVL System in ITS Application 1.3 Benefits from AVL System 1.4 AVL History at MTA 1.5 Current Reporting System for AVL 1.6 Objectives of the Project 1.7 Scope of the Project Methodology Data Collection 2.2 Schedule Adherence 2.3 Changes in Drivers Behavior with AVL Support 2.4 Running- Analysis 2.5 Trade-off between Link Travel Time and Schedule Adherence Analysis of Results by AVL Intervention Schedule Adherence and Drivers Operational Behavior --Aggregated Data Analysis 3.2 Schedule Adherence and Drivers Operational Behavior Disaggregated Data Analysis or the Peak Time and Off-peak Time 3.3 Travel Time Distribution and Running Time Analysis 3.4 A Scheduling Example Conclusion Data Collection 4.2 Schedule Adherence 4.3 Running Time Analysis ACKNOWLEGEMENTS REFERENCE 4

5 LIST OF TABLES Chapter 1 Table 1.1 Results of the survey from 40 Operators at MTA regarding their operational behaviors Chapter 3 Table 3.1 Schedule Adherence and Link Travel Times before and after AVL Interventio Table 3.2 Averages and Variances of the Arrival Times at the Main Timepoints and Next Timepoints, and Link Travel Times Table 3.3 Tests of Statistical Hypotheses for the Various Cases Table 3.4 Schedule Adherence and Link Travel Times before and after AVL Intervention during Peak Hours Table 3.5 Schedule Adherence and Link Travel Times before and after AVL Intervention during Off-peak Hours Table 3.6 Bus number and number of links having overnight as base variable for regressions Table 3.7 Summary of results from regression analysis Table 3.8 Summary of Regression Analysis on links where some variables are not present Table 3.9 Summary of Results from regressions categorized by routes having all variables with overnight as base Table 3.10 Parameters for GED distributions and number of rows per link for all Routes Table 3.11 Bus routes information Table 3.12 Starting and ending points of links for all routes TABLE 3.13 Travel Times at different Percentages for On- performance for Route 2 Table 3.14 Travel Times at different Percentages for On- performance for Route 3 Table 3.15 Travel Times at different Percentages for On- performance for Route 13 Table 3.16 Travel Times at different Percentages for On- performance for Route 22 Table 3.17 Percentages of buses arriving late at different definitions of lateness Table 3.18 Travel for superlink Saratoga Lexington Market Fred Ingl using different ways for calculation Table 3.19 Comparison of on- performance results using different methods of estimation for different definitions of late for superlink Saratoga Lexington-Fred Ingl 5

6 LIST OF FIGURES Chapter 1 Figure 1.1 Percentage of Agencies surveyed that have deployed or plan to deploy ITS technology Figure 1.2 Schematic map with the testing routes in MTA bus system. Chapter 3 Figure 3.1 Arrival distribution at the main Timepoints Figure 3.2 Arrival distribution at the next Timepoints after early arrival at the main Timepoints Figure 3.3 Link distribution after early arrival at the main Timepoints Figure 3.4 Density functions for Link 23 Figure 3.5 Density functions for Link 115 Figure 3.6 Density functions for Link 66 Figure 3.7 Density functions for Link 139 Figure 3.8 Density functions for Link 165 Figure 3.9 Density functions for Link 117 Figure 3.10 Travel s at different percentiles Figure 3.11 Travel s at different percentiles Figure 3.12 Travel s at different Percentiles Figure 3.13 Travel s at different Percentiles Figure 3.14 Travel Times at different Percentiles Figure 3.15 Travel s at different Percentiles Figure 3.16 Travel s at different Percentiles Figure 3.17 Travel s at different Percentiles Figure 3.18 Route versus on- performance (Route 2 direction 0) Figure 3.19 Route versus on- performance (Route 2 direction 1) 6

7 Figure 3.20 Route versus on- performance (Route 3 direction 0) Figure 3.21 Route versus on- performance (Route 3 direction 1) Figure 3.22 Route versus on- performance (Route 13 direction 0) Figure 3.23 Route versus on- performance (Route 13 direction 1) Figure 3.24 Route versus on- performance (Route 22 direction 0) Figure 3.25 Route versus on- performance (Route 22 direction 1) 7

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9 Analysis And Evaluation of Automatic Vehicle Location (AVL) for Maryland Transit Administration (MTA): Short-Term And Long-Term Strategies Introduction 1.1 Introduction to AVL System An AVL system is a computer-based vehicle tracking system capable of determining a vehicle s location in real. It allows a dispatcher from a control center to track vehicle movement and to communicate with the vehicle s operator. The expected benefits from an AVL system are improved schedule adherence, better emergency response, real information and data availability potentially used for planning and operation purposes [1]. At present, more than 60 transit agencies throughout the nation are at various stages of considering or installing AVL systems on their buses to improve fleet management and transit services. Although some benefits of the AVL system seem evident, there are very few studies that show the analytical results and benefits. The goal of this research is to gather the data from MTA and to show the impacts and benefits from the AVL system. 1.2 AVL System in ITS Application In addition to above benefits expected from the AVL system, there are other potential benefits. AVL is a key application of Intelligent Transportation System (ITS) to public transportation. According to the survey in 1994/1995, among ITS technology for transit including AVL, smart card, Automatic Passenger Counting (APC), passenger information system and adaptive signal control, AVL system has the most support from transit agencies: about 80 percent of agencies answered that they have deployed or plan to deploy AVL system as shown in Figure 1.1 [2]. 9

10 PERCENT OF SURVEYED AGENCIES AVL 25 SMART CARDS 33 APC 42 AUTO ANNUN 64 PASS INFO 25 SIGNAL PREMP ITS TECHNOLOGIE S TECHNOLOGIES Figure 1.1 Percentage of Agencies surveyed that have deployed or plan to deploy ITS technology. 1.3 Benefits from AVL System As mentioned in previous section, there are numerous impacts and benefits expected from AVL system. In this section, they are briefly categorized: Spatial Real- application Off-line application Type Quantitative Qualitative Object Passenger Agency 10

11 Temporal Short-term Long-term The Automatic Vehicle Location (AVL) system that Maryland Transit Administration (MTA) provides has many potential advantages for the operator and passengers for both short- and the long-term. These systems can contribute to the optimization of vehicle operation, scheduling and runcutting for the operator as well as reliable service and information for passengers. 1.4 AVL History at MTA Background [5, 6, 7] During 1986, MTA decided to enter the Bus Communications and Control Program. The program was to help MTA meet its goal of providing the public with more efficient and reliable service. It was decided that the program was to be implemented in phases: Phase I: Phase II: Phase III: Phase IV: Phase V: Improve radio communication between dispatcher and bus operator. Introduce LORAN-C based AVL on 50 buses and four supervisory jeeps with: Route-Schedule Adherence; historical data; map playback; 20-second vehicle polling. This phase was used to verify the AVL concept. Start fleetwide implementation of the AVL systems w/automatic Passenger Counting; improve radio coverage and efficiency; AVL on Light Rail. Complete radio system transition to 490mhz trunking; acquire additional frequencies (if available); acquire or upgrade necessary portable and mobile radios. Complete fleetwide implementation of AVL; integrate AVL with other systems. At the conclusion of Phase II of the program, an evaluation was conducted to determine whether the program was meeting its goals. The AVL On-Time Performance Report, published in April 1993, clearly illustrated that the objectives were met by the Phase II system. This system, however, had its limitations. These limitations are addressed in Phase III. The Phase III AVL system would include the following: Upgrade from LORAN-C to GPS; add it to 200 buses (including the upgrade of the Phase II buses) and all Light Rail Vehicles; upgrade the radio system from conventional to trunked; acquire a schedule and run-cutting system; and add Automatic Passenger Counting to 25 buses. The objectives of the Phase III system were essentially the same as those in Phase II. However, in Phase III, MTA 11

12 expected to achieve a solution applicable for the next 20 years of operation. The MTA expects to: A. Improve service reliability. B. Increase driver and passenger safety. C. Reduce operational expenses Other AVL-Related Projects There are other major projects in progress right now that are directly related to AVL or have a profound effect upon the operation of AVL. These relationships are described below. Transit Information Center Upgrade (TICU) Phase II TICU II provides an automated transit information system and Muldia Kiosks that require the integration of existing MTA systems including the scheduling and runcutting, AVL/CAD Phase III, and computer-telephony integration. This integration provides the capability of providing the public with current and real- information via the ATIS and the Kiosks. Computer-based training modules will be provided to improve the training process and to provide a refresher course for the Transit Information Center Agents and Bus Supervision. Automatic Vehicle Location Phase IV The AVL Phase IV Program expands the trunked radio communications system from five to eight channels and installs additional desktop radio consoles at the Bus Communications Control Center and the Light Rail Control Center. Specifically, this includes the design, installation, optimization, and placing into service three new base stations at each of the two radio sites (Westview and Mays Chapel), along with the addition of site controller, central electronics bank and voting comparator equipment. The trunking enhancements to the existing MTA Radio communications Systems will increase the efficiency of all bus, maintenance, and dispatcher operation by decreasing the average that users must wait before a frequency is clear for talking. The objective of Phase IV is to provide the radio communication requirements necessary for complete outfitting of all MTA bus and maintenance vehicles with AVL/CAD Systems. Scheduling and Runcutting System The scheduling and runcutting system was replaced as a part of the AVL Phase III project. This provided a map-based scheduling system to ease data transfer to the AVL system. Along with this, PLAN, a supportive data collection system is also being installed to interface with both scheduling software and the map. Just as vehicle schedules and route map activity supports the AVL system through PLAN, they also support the manual 12

13 collection of ridership data and will allow the use of an Automatic Passenger Counting (APC) System as well as hand-held data collectors. The data collected and summarized by the PLAN system provides the information required to update the schedules and runs. This in turn makes the use of the scheduling system practical. Automatic Passenger Counting System (APC) The MTA became the first transit property to require the linking of a real GPS system to an APC system. Previously, such systems used signposts along the scheduled route and dead reckoning to identify stop locations. This new system uses the schedules and stop location information, transferred to the AVL, to actively count passengers boarding or alighting transit vehicles. This data is then transferred, through AVL, to the scheduling system s PLAN software, where it will be used to update the schedules, to answer patron complaints, and respond to service requests. The intent is to automate the data collection, transfer, uploading and summarization process. This linking of GPS and APC has now become standard in the industry wherever both systems exist. 1.5 Current Reporting System for AVL [8] The SmartTrack CAD/AVL system, developed and provided to MTA by Orbital/Transportation Management Systems (TMS) in Orbital for the data collection and monitoring operation, is a fleet management system providing voice and data communications between dispatchers and vehicle operators, incident management, vehicle location tracking, route/schedule adherence tracking, data collection, report generation and status monitoring. Schedule Adherence tracking is performed by the system as follows. When a driver logs onto the route and block, and he/she is about to run, the SmartTrack server at the dispatch center downloads the schedule for the specified route and block to the Intelligent Vehicle Logic Unit (IVLU) on the vehicle. When the vehicle goes off-schedule (early or late) by more than a specified amount at a point, which is a pre-set location for checking the schedule adherence by the system, a data message (incident) is sent to the SmartTrack dispatcher workstation and the offschedule incident is also stored in the SmartTrack database. The Lead Dispatcher can adjust the amount of used to determine when a vehicle is considered early or late. The Lead Dispatcher can also generate the Schedule Adherence Report, which lists all early/late incidents received during a given frame. 1.6 Objectives of the Project Although some benefits of AVL, including qualitative impacts, are evident, many benefits from off-line applications, including operations analysis and planning, have been discussed [1, 3, 4], but there is little research to show the analytical results and benefits. Although operators have the written schedule for their operation, and are expected to try to keep the schedule, efforts by the operators to meet the schedule have been hardly 13

14 appreciated. Table 1.1 is the result of a survey of 40 Operators, which was conducted after the "without AVL intervention" test period. This project will suggest the proper data to be collected from the AVL system. Also, this project will evaluate the effects of the short-term improvement through AVL (real- intervention), and will suggest the long-term planning and operation plan as well as predict the long-term effects of those systems (link adjustments for future scheduling). Table 1.1 Results of the Survey from 40 Operators at MTA Regarding Their Operational Behaviors Always (5) Almost (4) Somes (3) Seldom (2) Never (1) Average I check my schedule at each point If I am running ahead of schedule, I try to drag the line to get back on schedule. If I am running late, I try to get back on schedule by safely adjusting speed. If I am operating off the schedule, I call radio supervision to explain why As a short-term benefit of the AVL system, short-term operational adjustment can be expected. This short-term adjustment will allow the predictable operation and runcutting. Also, better on- performance can be expected. These expectations can be examined with two sets of one-month experimental data with and without short-term adjustment. As a long-term benefit of the AVL system, the appropriate scheduling through properly estimated link will be suggested for the better on- performance from the collected data with short-term adjustment. This results in more reliable and cost-efficient scheduling. These potential improvements through the AVL system will be beneficial not only for the transit agency but also for transit users. Higher reliability will increase ridership, which will increase revenue of the system and will be beneficial for the agency, eventually. 14

15 1.7 Scope of the Project This study collected the data from four bus routes of MTA, numbers 2, 3, 13, and 22 shown in Figure 1.2. These lines were chosen with several factors in mind: 1. Utilization of all four of the bus divisions in MTA. 2. Sufficient AVL equipped vehicles to handle the routes simultaneously. 3. Good ridership data. 4. Somewhat difficult to manage the schedule adherence. The data was collected for two sets of four-week periods. During the first four weeks, the AVL system was used only as a tool for the radio communications and the data collection. It was not used to intervene schedule abnormalities that occur on the street. This phase is identified by the phrase without AVL intervention. On the other hand, operators had to be watched carefully to guarantee proper logons at pullout, layovers, for relief, et.al. This policing function was carried out by the dispatcher. 15

16 Towson Sheppard Prat hosp Joppa Road University Parkway City of Baltimore Coppin State College Lafayette Northern Pkwy Morgan State Univ U.S Rt 40 Penn Station St Paul Street Fayette Street East Ave Lombard Street Baltimore Street Gay Street Eaton Camden Yards Lombard Conway Boston Route 02 Route 03 Route 13 Route 22 Spruce Figure 1.2 Schematic map with the testing routes in MTA bus system The following four-week period was identified by the phrase with AVL intervention. The AVL system was used vigorously to intervene or intercept problems found along the four evaluation routes. Dispatchers were expected to not only make sure logons have occurred but must also contact vehicle with regard to route and schedule adherence, mechanical problem indications, and service adjustments. 16

17 This study analyzes the data in aggregated way for the scheduling adherence to test overall performance by AVL intervention. However, link estimation will be examined for the each route. METHODOLOGY In this section, methodologies for major issues such as data collection, scheduling adherence, drivers behavior analysis, running analysis and the relationship between scheduling adherence and estimated link s will be discussed. 2.1 Data Collection MTA and TMS generate incident files and three kinds of reference files. The incident files contain date, schedule, vehicle ID, operator ID, route number, block number, arrival status (early, late or on-), thresholds, point (TP) ID, and TP name. This file gives the idea how early or late a bus arrives at points. The minimum threshold that can be set to show the earliness and lateness is one minute. If the vehicle is more than a minute early or late, then the system shows the earliness and lateness in half a minute. This threshold can be set in two ways: one for the record and the other for the alert. Although the threshold for the record for this research was set to a minute to provide the arrival data close to the real arrival, the threshold for the alert that was informed and displayed to operators and dispatchers was set to one minute for the earliness and three minutes for the lateness. Original incident files contain only off-schedule data, which are in early or late status, but for this research, additional data for the on- arrivals were artificially generated according to the schedule. The reference files are log-on file, log-off file and error file, and they provide the additional information in terms of the correctness of the incident file. Although incident files are supposed to have correct data, some of them are not correct due to the system malfunction and mistakes by the operators. Only 86.9% of the collected data was used for the analysis. 2.2 Schedule Adherence This study follows a before-and-after study to compare the results by with and without AVL intervention to show the impacts on schedule adherence for transit systems by the AVL system. The collected data is categorized by the arrival status at points: 1) early (more than one minute ahead of schedule), 2) late (more than three minutes behind schedule), and 3) on- (less than one minute early and less than three minutes late). In order to show the impacts, frequencies of occurrences for each arrival status, average arrival s compared to the schedule, and variances of the arrival s for both with and without AVL intervention, will be analyzed and tested for the statistical hypotheses to show the improvement by AVL. The result will be analyzed, aggregated as well as disaggregated, for each route, period and each point. 2.3 Changes in Drivers Behavior with AVL Support Although schedule adherence itself is important, drivers behavior and their responses to AVL system may be more valuable, because in many cases, especially when the operation is behind schedule, to get back on schedule is difficult and somes out of the 17

18 operator s control. However, the AVL may encourage the driver to meet the schedule. This impact can be shown in the link s after the incident happens. In order to show the drivers behavior, the data is categorized by the arrival status at points: 1) early, 2) late and 3) on-. For each case, arrival status at the next points and link s after those particular stops are examined to see whether there is a relationship between arrival status and link, and whether AVL encourages the relationship to go in a positive direction. Usually, when the vehicle is ahead of schedule, it creates a problem, because once passengers miss the bus, they need to wait the entire headway, which is usually longer than the waiting caused by a vehicle that is behind schedule. However, the problem of a vehicle being ahead of schedule is easier to resolve. In this analysis, the results of both aggregated and disaggregated ways will be discussed. 2.4 Running- Analysis Running analysis is when data is collected and analyzed with AVL intervention to find proper link s for efficient scheduling. There can be two approaches for running- analysis in terms of the formation of data. The first uses discrete data, which is a similar format with those collected from the AVL system. The arrival s and the link s are separated by the given gap by AVL system, which is 30 seconds in this study. The second approach uses continuous density function, which can be estimated from the collected discrete data. When the discreteness of the data is severe and the amount of the collected data is minimal, using the continuous data function can be more effective, because it can reduce distortions and size of the collected data, although it requires higher skill to estimate the functions from the collected data. In this study, estimation of the density function is briefly introduced, and the estimation of link is also introduced [9]. First, a standard ordinary least squares (OLS) regression was performed with link as the dependent variable. The right-hand side variables were: weekday (vs. weekend), a vector of of day, lag status early and lag status late. Note that every variable is a dummy variable. While other variables may affect the average, these are the variables that seem reasonable to include in a scheduling process. Seasonal variables could be of interest as well, but the short duration of the data collection process precludes inclusion of these variables in this analysis. Lag status early and lag status late are used to pickup an important part of that is not totally dependent on the of day. It was shown that when a bus is behind schedule at a certain point, the driver will make an effort to speed up and return to schedule. When a bus is ahead of schedule, the driver will make an effort to slow down 18

19 [9]. Both of these actions by the driver are more pronounced, and more successful, with AVL intervention. Since the s are significantly affected by these actions, we include them in the regression analysis. The form of the linear regression used is = a + β + where x 1 = early morning and late evening periods, x 2 = mid-day periods, x 3 = morning rush hours, x 4 = afternoon rush hours, x 5 = lag status early, x 6 = lag status late, and x 7 = weekdays. 1x1 + β 2 x2 + β 3x3 + β 4 x4 + β 5x5 + β 6 x6 β 7 x7 The regression is estimated by OLS techniques in GAUSS, for each of the links separately. The periods selected for the analysis were contradictory to the period for morning rush hours used by the MTA. According to MTA, morning rush hours were from 6:00 a.m. to 9:00 a.m. and evening rush hours were from 3:00 p.m. to 6:00 p.m. Following the regression analysis, the s were fit to density functions. Many density functions were tried and tested against the data. The best density function for describing the data is kept for each link and used in the analysis. This allows examination of not only the changes in the mean of the across links (as a regression allows), but also how various other benchmarks perform. For example, we can examine the 85 th percentile or any other. A large group of density functions were fit to the data. Graphical results were presented from two of the functions, the Poisson distribution and the Generalized Error Distribution (GED). The Poisson is a discrete distribution function that is often used for arrival data similar to that of the s in this study. The GED is a general form of continuous symmetric distribution. The distribution function for the Poisson is λ x e λ f ( x) =, x! x = 0, 1, 2 where λ ( λ 0 )is the parameter of the distribution and x represents the. The mean of a Poisson distribution is λ. The density function for the GED distribution is 19

20 x µ p 1 p pσ g( x) = e 1 p 1 2 p Γ 1 + σ p where µ, σ, and p are the parameters of the distribution and x represents the and p 1. µ = Mean of the distribution, σ 2 = variance of the distribution and p = the steepness of the distribution. When p=2, the GED is the normal distribution; when p < 2, the distribution is more steep than normal distribution; and, when p > 2 the distribution is more flat than the normal distribution. For each link, the s are used to estimate the parameters of the distribution. The estimations are carried out in GAUSS using a maximum likelihood routine. The parameters that are returned maximize the likelihood that the data was drawn from a distribution of that type. There are no true tests between likelihood functions, this performance of the various distribution functions is judged by examining the graphical output, and from its performance in the χ 2 test. Further research is being carried out to perform the tests. The scheduling procedure outlined in this research is quite simple once the density functions are estimated. The goal is to create a scheduling procedure where the manager of the transit system can trade on- performance for frequency in full knowledge of the consequences. The examples presented in this paper are either for a sub-set of a single route or for the whole route in some cases and does not employ advanced estimation techniques. Because some links were not selected for the analysis, the for whole route could not be obtained in some cases. Hence, a sub-set of the route (where the links were connected directionally) was used for those cases. It is merely meant to be illustrative of how the methodology presented can be used to schedule. For each of the links selected, the 50, 75, 85, 90, 95, 99, and 99.9 percent values are obtained. These give us the scheduled s needed for an on- performance of that level. The sum of the scheduled s gives us the total needed to cover the whole route or a sub set of the route. In the section containing the examples, further extensions and refinements are presented. 2.5 Trade-off between Link Travel Time and Schedule Adherence It is not unreasonable to expect that there is a relationship between schedule adherence and running for scheduling. To estimate the relationship, a couple of assumptions are necessary. When buses arrive early at the stations, if drivers are willing to wait or slow down to keep the schedule, then longer estimated run will increase schedule adherence. However, if drivers do not slow down, longer estimated run will cause poor schedule adherence due to the early departure. For this reason, a proper assumption is necessary. 20

21 Because of AVL intervention, it is assumed that there is no early departure which means longer estimated running would increase schedule adherence. The other issue which should be discussed is whether schedule adherence at minor Time Points should be kept or not. It is obvious that the effort to keep the schedule adherence at the all Time Points will increase estimated link. If the schedule adherence of the selected major Time Points is considered, then running would not be increased as much because of expected compensations of running s between major Time Points. Although keeping the schedule adherence at the major Time Points is more realistic, in this paper, both examples will be briefly presented. ANALYSIS OF THE RESULTS BY AVL INTERVENTION 3.1 Schedule Adherence and Drivers Operational Behavior Aggregated Data Analysis As mentioned, the results of the analysis are presented in an aggregated manner. Although the general results are important, the impact by the AVL intervention when the operation is ahead of schedule is paid special attention because the particular case can be easily improved by the drivers awareness of the schedule and willingness to meet the schedule Frequency Analysis on the Arrival Status and Link Travel Time The data collected before AVL intervention and after AVL intervention is classified in Table 3.1. The results show that regardless of the AVL intervention, the drivers try to maintain their schedule in most cases. Generally, when the arrival status is late at the main Time Point, it is expected that the traffic condition is not good, and there is more possibility in longer link after the main Time Point, if drivers do not consider the schedule adherence. On the other hand, when the arrival status is early, it is expected that the traffic condition is good, and the vehicle can be operated with the shorter, if the schedule adherence is not considered. In both cases, with and without AVL interventions, the opposite results to the normal expectations mentioned above were found. This result indicates that drivers are well aware of their schedule and most of drivers tried to adjust their operation speed to keep the schedule. 21

22 Table 3.1 Schedule Adherence and Link Travel Times before and after AVL Intervention Main Timepoint Next Timepoint Link Travel Time Arrival status Frequency (%) Arrival status Frequency (%) Travel status Frequency (%) Before Early 5378 Early 3438 (63.9) Early 1512 (28.1) AVL (4.4) Late 396 (7.4) Late 3106 (57.8) After AVL Late 6269 (5.2) On (90.4) Total (100.0) Early 1808 (1.8) Late 3747 (3.7) On (94.6) Total (100.0) On (28.7) On- 760 (14.1) Early 64 (1.0) Early 2871 (45.8) Late 5215 (83.2) Late 2483 (39.6) On- 990 (15.8) On- 915 (14.6) Early 1421 (1.3) Early 3384 (3.1) Late 3112 (2.8) Late 3779 (3.5) On (95.9) On (93.4) Total (100.0) Total (100.0) Early 1137 (62.9) Early 502 (27.8) Late 189 (10.5) Late 1031 (57.0) On- 482 (26.7) On- 275 (15.2) Early 46 (1.2) Early 1612 (43.0) Late 3259 (87.0) Late 1548 (41.3) On- 442 (11.8) On- 587 (15.7) Early 527 (0.6) Early 1395 (1.4) Late 1297 (1.3) Late 1614 (1.7) On (98.1) On (96.9) Total (100.0) Total (100.0) In terms of schedule adherence, the first concern that can be discussed from the test is the impact on the schedule adherence by the AVL application. The result in the aggregated manner is shown at the third column in Table 3.1. After the AVL intervention, the on performance (less than one minute early and less than three minute late) is improved by 4.2 percent from 90.4 percent to 94.6 percent. Consequently, early arrival and late arrival are also reduced by 2.6 percent and 1.5 percent respectively. As shown in the Table 1.1, because the drivers try to meet the schedule and they are well aware of the schedule, even without AVL intervention, the schedule adherence was not really low. However, with the alert system of the AVL system, the drivers could be aware of their operational status more effectively, and as results, schedule adherence was improved. The second concern that can be discussed is the breakdown of schedule adherence at the next Time Points to the main Time Points. This result can show the drivers behavior and willingness to keep the schedule. The fifth column in Table 3.1 shows that the overall schedule adherence (on- performance), after the abnormal arrival status at the main Time Points, does not really improve. However, after the early arrivals, the probability of the early arrivals at the next points is slightly improved by one percent from 63.9 percent to 62.9 percent. This indicates that even though drivers are aware of their 22

23 operational status from the AVL system, they were either not able to or not willing to keep the schedule at the next Time Points. For detailed analysis, link s were categorized in the seventh column of Table 3.1. This data shows the drivers behavior in the best way. In many cases, despite drivers who are willing to adjust their operating speeds in a safe manner to meet the schedule, since the traffic condition may not allow them to adjust their speeds, it is hard for them to meet the schedule at the very next stop after the abnormality in the previous stops. However, if they are willing to adjust, their willingness can be easily shown at the link after the main Time Points. When the operation is ahead of schedule, the results show that AVL did not affect much to change drivers' willingness to slow down, since there is slight reduction (0.3 percent, from 28.1 percent to 27.8 percent) in shorter link, which shows increase of the operating speed. These results conclude that AVL helps drivers to be aware of schedules and to adjust their speed to meet the schedule. However, it was also shown that some drivers, regardless of AVL intervention, did not and/or could not try to adjust their operation. One main reason can be the design of the bus stop. If there is no pocket for the bus at the stop, it is not possible to stay at the stop to keep the schedule. Also, because of given traffic condition, slowing down the operation may not be easy in many cases. Other improvement can be suggested with the active intervention by the dispatchers with the AVL intervention to the drivers who do not pay attention to their operation in terms of schedule adherence. For this case, education of the drivers will make the AVL system more effective Arrival Time and Link Travel Time Distributions Although Table 2 showed the frequency of incidents, and that table itself showed some improvements by AVL, in order to show detailed impact, the averages and variances for the cases introduced in Table 3.1 were computed in Table 3.2. Table 3.2 Averages and Variances of the Arrival Times at the Main Timepoints and Next Timepoints, and Link Travel Times Main Timepoint Next Timepoint Link Travel Time Arrival Average Arrival Average Travel Average Before AVL status Early (4.82) (Variance) Late (24.00) On (0.00) status (Variance) status (Variance) Early (5.10) Early (1.07) Late (5.22) Late (7.11) On (0.00) On (0.00) Average (8.52) Average (7.17) Early (2.66) Early (5.05) Late (30.07) Late (3.84) On (0.00) On (0.00) Average (30.67) Average (5.96) Early (1.50) Early (1.58) Late (16.35) Late (16.35) On (0.00) On (0.00) Average (0.74) Average (0.73) 23

24 After AVL Total (4.89) Total (5.64) Total (1.52) Early Early (6.12) Early (1.56) (4.92) Late (19.28) Late (8.88) On (0.00) On (0.00) Late (32.36) Average (10.21) Average (8.38) Early (3.77) Early (5.49) Late (28.78) Late (3.87) On (0.00) On (0.00) Average (30.89) Average (6.26) On Early (1.46) Early (1.35) (0.00) Late (16.13) Late (16.13) On (0.00) On (0.00) Average (0.32) Average (0.31) Total (3.86) Total (3.86) Total (0.82) In most cases, with AVL intervention, the numbers were consistently improved compared to those without AVL intervention. Average arrival for the main points were reduced from 0.31 minutes late to 0.28 minutes late. Figure 3.1 shows the distributions of the arrival statuses with and without AVL interventions. Although data is recorded by 0.5-minute intervals, because the minimum amount of initial earliness and lateness recorded by AVL is one minute, all the data between one minute early and one minute late is recorded as on. Since this system exaggerates on- performance (0 minute in terms of arrival status), frequency recorded as on was smoothly allocated to 0.5 minute early, 0.5 minute late and on- artificially for realistic results. In spite of this effort, the figure shows extremely concentrated frequencies around on- arrival. Because of this concentrated result, from Figure 3.1, it is not easy to distinguish two curves by with and without AVL. However, with a closer look, it is shown that the curve with AVL is more concentrated than the one without AVL, which shows the better schedule adherence with AVL. However, as discussed before, the case that we need to focus on for this paper is when the operation is ahead of schedule. The Table 3.2 shows that when the operation is ahead of schedule, the average earliness with AVL reduced by 0.32 minutes from 3.00 minutes early to 2.68 minutes early. In addition to that, at the next Time Points, average earliness was reduced by 0.34 minutes and average lateness was increased by 0.61 minutes from 2.46 minutes late to 3.17 minutes late. Consequently, the average arrival at the next Time Points after the early arrival at the main Time Points was increased by 0.12 minutes from 1.69 minutes early to 1.57 minutes early. Figure 3.2 shows the arrival distribution curves with and without AVL at the next Time Points when the arrival status was early at the main Time Points. It is clearly shown that with AVL, the curve moves a little to the right side of the curve without AVL. That shows that when the early arrival occurs at the main Time Points, the arrival status with AVL is closer to on- as compared to that without AVL. 24

25 45.00% 40.00% 35.00% NUMBER OF INCIDENTS 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% DEVIATION FROM THE ACTUAL TIME WITHOUT AVL WITH AVL Figure 3.1 Arrival distribution at the main Time Points 25

26 16.00% 14.00% % NUMBER OF INCIDENTS 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% DEVIATION FROM THE SCHEDULED TIME WITHOUT AVL WITH AVL Figure 3.2 Arrival distribution at the next Time Points after early arrival at the main Time Points At the same, average link after the early arrivals at the main Time Points was increased by 0.11 minutes from 1.00 minute late to 1.11 minute late. The numbers referred here may look small and not significant, however, that is because earliness and lateness canceled each other in most cases. Also, since most scheduled link s are between two minutes to five minutes, one-minute change in link can be considered as a big change. Figure 3.3 shows the distribution curves of the link s with and without AVL after early arrival occurs at the main Time Points. Unlike other distribution curves, although the curves are concentrated at the center, they have two peaks regardless of AVL implementation. This result can be explained that when the operation is ahead of schedule, there are mainly two groups of drivers: one who enjoys congestion-free driving conditions and the other who tries to keep the schedule. In other words, it is interpreted that AVL has not been very effective to some drivers at MTA, who do not pay attention to schedule adherence. 26

27 16.00% 14.00% % NUMBER OF INCIDENTS 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% WITHOUT AVL WITH AVL DEVIATION FROM THE SCHEDULED TIME Figure 3.3 Link distribution after early arrival at the main Timepoints Variances of the arrival status and link s are also shown in Table 3, and it shows consistent results. The variances for the main Time Points and the next Time Points are reduced after AVL intervention because of the better schedule adherence at the stops. On the other hand, the variance of the link s with AVL intervention was increased, and this can be interpreted as some drivers were affected more by the AVL operation, while others were not affected much, so the link s are more spread with AVL intervention Tests of the Statistical Hypotheses In order to verify the impact of the AVL intervention, the significance tests for the selected cases were pursued and the results are shown in Table 3.3. Among many cases introduced in Table 3.1 and Table 3.2, the arrival status at the main Time Points, and arrival status at the next Time Points and link after early arrival status at the main Time Points were selected for significance tests due to their expected direct impact of the AVL intervention. From the mean values and variances in the Table 3.2 and sample sizes in Table 3.1, onetailed z-test to compare two means was applied due to the big sample size. For each case, statistical hypotheses were applied. As shown in Table 3.3, all null hypotheses were rejected, which means that averages of arrival statuses and link s without AVL intervention, than with intervention, are not the same at the 90 percent 99 percent confident level. 27

28 Table 3.3 Tests of Statistical Hypotheses for the Various Cases Early at main TPs Main TP Next TP Link Without µ A = 0.31 µ A = µ A = 1.00 AVL σ 2 A = 4.89 σ 2 A = 8.52 σ 2 A = 7.17 (Case A) n = n = 5378 n = 5378 With AVL (Case B) µ B = 0.28 σ 2 B = 3.86 n = µ B = σ 2 B = n = 1808 µ B = 1.11 σ 2 B = 8.38 n = 1808 Statistical hypotheses H 0 : µ A = µ B H 1 : µ A > µ B H 0 : µ A = µ B H 1 : µ A < µ B H 0 : µ A = µ B H 1 : µ A < µ B Z cal Z table (90%) (95%) (99%) P-value Result Reject H 0 at 99% confident level Reject H 0 at 90% confident level Reject H 0 at 90% confident level µ: mean value (minute) σ 2 : variance (minute 2 ) n: population size The results in Table 3.3 once made sure that with AVL intervention, average operation was improved and when the early arrival happens at the certain Time Point, link becomes longer to keep the schedule on. Consequently, the earliness at the next Time Point was reduced. While schedule adherence and link after the early arrival were improved by the AVL, it is not clear that those after the early arrival were either improved or deteriorated. These results can be induced from the circumstance of the operation behind schedule. In most cases, operation behind the schedule is caused by the traffic congestion, and in most cases, there is nothing that drivers can do to improve schedule adherence. 3.2 Schedule Adherence and Drivers Operational Behavior Disaggregated Data Analysis for the Peak Time and Off-peak Time Tables 3.4 and 3.5 show the frequency analysis for peak and off-peak just like Table 3.1 for aggregated analysis. On performances are improved by AVL implementation during both peak and off-peak from percent to percent and from percent to percent, respectively. Although the results show that AVL intervention improved the schedule adherence for both peak and off-peak significantly, schedule adherences between peak and off-peak were not significantly different before AVL intervention (86.93 percent and percent, respectively) and after AVL intervention (92.54 percent and percent, respectively). 28

29 Although the results do not show the significant difference in on- performance between off-peak and peak for the both with and without AVL intervention, the drivers behavior shows the different reaction to the AVL intervention between peak and off-peak hours. Without AVL intervention, compared to the off-peak hours, peak hours have a higher late ratio (8.97 percent to 8.35 percent) but also have a lower early ratio (4.10 percent to 4.66 percent). These results satisfy the common expectation, which is higher late ratio in peak hour due to potential congestion, and higher early ratio in off-peak hour due to the better traffic condition. Table 3.4 Schedule Adherence and Link Travel Times before and after AVL Intervention during Peak Hours Main Time Point Next Time Point Link Travel Time Arrival Frequency Arrival Frequency (%) Travel Frequency (%) Before AVL After AVL status (%) Early 1914 (4.1) Late 2646 (5.7) On (90.2) Total (100.0) Early 684 (1.8) Late 1435 (3.7) On (94.5) Total (100.0) status status Early 1171 (61.2) Early 526 (27.5) Late 144 (7.5) Late 1144 (59.8) On- 599 (31.3) On- 244 (12.7) Early 27 (1.0) Early 1208 (45.7) Late 2192 (82.9) Late 1064 (40.2) On- 427 (16.1) On- 374 (14.1) Early 573 (1.4) Early 1369 (3.3) Late 1374 (3.3) Late 1584 (3.7) On (95.3) On (93.0) Total Total (100.0) (100.0) Early 410 (59.9) Early 189 (27.6) Late 78 (11.4) Late 409 (59.8) On- 196 (28.7) On- 86 (12.6) Early 11 (0.8) Early 612 (42.7) Late 1262 (87.9) Late 593 (41.3) On- 162 (11.3) On- 230 (16.0) Early 223 (0.6) Early 598 (1.6) Late 521 (1.4) Late 646 (1.8) On (98.0) On (96.6) Total (100.0) Total (100.0) 29

30 Table 3.5 Schedule Adherence and Link Travel Times before and after AVL Intervention during Off-peak Hours Main Time Point Next Time Point Link Travel Time Arrival Frequency Arrival Frequency % Travel Frequency % Before AVL After AVL status (%) Early 3461 (4.7) Late 3623 (4.9) On (90.4) status status Early 2265 (65.4) Early 987 (28.5) Late 251 (7.3) Late 1959 (56.6) On- 945 (27.3) On- 515 (14.9) Early 37 (1.0) Early 1663 (45.9) Late 3017 (82.3) Late 1419 (39.2) On- 567 (15.7) On- 541 (14.9) Early 848 (1.3) Early 2018 (3.0) Late 1742 (2.6) Late 2199 (3.3) On (96.1) On (93.7) Total Total Total (100.0) (100.0) (100.0) Early 1122 (1.8) Early 727 (64.8) Early 313 (27.9) Late 110 (9.8) Late 620 (55.3) On- 285 (25.4) On- 189 (16.8) Late 2311 (3.6) On (94.6) Total (100.0) Early 35 (1.5) Early 999 (43.2) Late 1995 (86.3) Late 955 (41.3) On- 281 (12.2) On- 357 (15.5) Early 304 (0.5) Early 797 (1.3) Late 774 (1.3) Late 973 (1.6) On (98.2) On (97.1) Total (100.0) Total (100.0) With AVL intervention, reduced early incidents are the major improvement for both peak (4.10 percent to 1.76 percent) and off-peak (4.66 percent to 1.76 percent) s. Also, late incidents are also reduced for both peak (8.97 percent to 5.69 percent) and off-peak (8.35 percent to 5.5 percent) s. Overall, it is shown that on- performance during off-peak was improved more than that peak, because of higher early incident in off-peak. This is due to the better traffic condition which could be improved with AVL intervention at the higher rate. 3.3 Travel Time Distribution and Running Time Analysis Regression Analysis The regression analysis was performed separately for each of the 188 links studied. While each of these links was studied, results are discussed for only 164 links in this paper. These 164 links satisfied the following: 1. Observations on both weekdays and weekends 2. Observations from each of the s of day 30

31 3. Buses that were early, on-, and late at the point For these links, the base for the regression analysis was overnight and on a weekend with the bus being on- at the last point. Summary statistics for the regressions for all the routes are shown in Table 3.6. Table 3.7 shows the 164 links in which all the variables are present with overnight as base and are categorized according to the four routes under study for this paper. Table 3.6 Bus number and number of links having overnight as base variable for regressions Bus # Number of Links Table 3.7 Summary of results from regression analysis Variable Number of links with significant positive coefficients Number of links with insignificant coefficients (zero) Number of links with significant negative coefficients Average value of coefficients as percentage of weekend overnight Morning and evening off peak Day Morning rush hour Evening rush hour Lag Status (Early) Lag Status (Late) Weekday Goodness of Fit R 2 Statistics Minimum 0 Mean Maximum 0.65 The results for the remaining 24 links are not discussed in detail in the research as these links had different bases for regression and lack of substantial number of observations for the links to arrive at any realistic conclusions. The summary statistics of the regressions for the 24 links can be found in the Table 3.8. Table 3.8 Summary of Regression Analysis on links where some variables are not present Route 2 Weekend not present Overnight as base Variable Positive No Effect Negative Percentage Morning and Evening off peak Day Morning rush hour