Using AVL Data to Measure the Impact of Traffic Congestion on Bus

Using AVL Data to Measure the Impact of Traffic

Congestion on Bus Passenger and Operating Cost

A Thesis Presented By

Ahmed Talat M. Halawani

The Department of Civil and Environmental Engineering

in partial fulfillment of the requirements for the degree of

Master of Science

Civil Engineering

in the field of

Transportation Engineering

Northeastern University Boston, Massachusetts

December, 2014

ABSTRACT

Letting buses operate in mixed traffic is the least costly way to accommodate transit, but that exposes transit to traffic congestion which causes delay and service unreliability. Understanding the real cost that traffic congestion imposes on both passengers and operating agencies is critical for the efficient and equitable management of road space. This study aims to develop a systematic methodology to estimate those costs using Automated Vehicle Location data. Traffic congestion increases cost to both transit operators and passengers. For transit operators, congestion results in longer running times and increased recovery time. To passengers, traffic congestion increases riding time and, because of how congestion increases unreliability, waiting time. Using data from a low-traffic period as a baseline, incremental running time in each period can be calculated. However, some of this incremental running time is due to the greater passenger volumes that typically accompany higher traffic periods. Passenger counts and a regression model for dwell time, estimated from detailed ride check data, are used to estimate the passenger volume effect on running time so that incremental delay due to congestion can be identified. Cost impacts for operators and passengers follow directly. Observed running time variability is a combination of variability due to greater demand, variability in the schedule, inherent variability in running time, variability due to imperfect operating control, and variability due to traffic congestion. Methods are developed to estimate the first four components so that incremental variability due to traffic congestion can be identified for each period, again using a low traffic period as a baseline. From this incremental variability, we can estimate the additional recovery time needed as well as increases in passenger waiting time and potential travel time, which the difference between budgeted travel time and actual travel time.

iii

The methodology was tested on nine different bus routes including both high and low frequency routes. Overall, the average impact on operating cost is $20.4 per vehicle- hour, and the average impact to passengers is $1.30 per passenger; naturally, these impacts are far greater during peak periods.

ACKNOWLEDGMENTS

First and foremost I would like to express my special appreciation and thanks to my advisor, Prof. Peter G. Furth, who offered his continuous advice and encouragement through the past two years. I have been extremely lucky to have a supervisor who has a great personality, wisdom and knowledge. I would also like to thank my committee member, Prof. Haris N. Koutsopoulos, for his advice. I am grateful to Dr. Daniel Dulaski for his instruction during my study at Northeastern University. This paper would not have been completed without the willingness and support of Melissa Dullea, Samuel Hickey, and David Schmeer at MBTA who provided us with all the needed data for this thesis. I also thank the MIT transit research group for providing us with a sample of the AVL data that we used as a first step in exploring the data. I would also like to thank my parents and brothers who were always supporting me and encouraging me with their best wishes. Last but not least, I would like to thank my wife and best friend, Alyaa Alharbi, for her love, patience, and understanding. Finally, I would like to thank my daughter, Basema, who has been such a great inspiration to me.

TABLE OF CONTENTS

ABSTRACT ...... ii TABLE OF CONTENTS ...... v LIST OF TABLES ...... vii LIST OF FIGURES ...... ix Chapter 1. Introduction ...... 1 1.1. Overview ...... 1 1.2. Research Objective ...... 2 1.3. Thesis Organization ...... 3 Chapter 2. Literature review ...... 5 2.1. Measuring Congestion ...... 5 2.2. AVL systems ...... 6 2.3. Reliability ...... 7 2.4. Conclusion ...... 8 Chapter 3. Data Sources ...... 9 3.1. Automated Vehicle Location system (AVL) ...... 9 3.1.1. Heartbeat Data ...... 9 3.1.2. Time-point Data ...... 10 3.1.3. Announcement Record Data ...... 12 3.2. Automated Passenger Counting Data (APC) ...... 14 Chapter 4. AVL Data Analysis Methodology ...... 15 4.1. Announcement record data processing ...... 15 4.2. Time-point data processing ...... 16 4.3. Evaluation & Suggestion for the Reviewed AVL Archived Data ...... 17 Chapter 5. Methodology ...... 19 5.1. Stop Time Model ...... 19 5.1.1. Dwell Time Model ...... 19 5.1.2. Lost time ...... 21 5.2. Grouping trips ...... 23 5.3. Average lower speed impact ...... 24 5.4. Variability in Running Time impact ...... 26

5.4.1. Variability at the trip level ...... 26 5.4.1.1. Variations from the scheduled running time VFSch(RT)...... 27 5.4.1.2. Adjust running time variation for greater demand ...... 29 5.4.1.3. Impact on Operating Cost ...... 30 5.4.2. Variability at stop level ...... 32 5.4.2.1. Impact on waiting time “with high frequency service” ...... 32 5.4.2.2. Impact on waiting time “with low frequency Service” ...... 34 5.4.2.3. Impact on potential (Budgeted) Travel Time...... 35 5.5. Value of time...... 36 5.6. Summary ...... 37 5.7. AVL-Free Methodology ...... 38 5.7.1. The Number of Stops Made Model...... 38 5.7.2. Summary ...... 39 5.8. Application to MBTA Route 1 ...... 40 5.8.1. Annual Impact ...... 44 5.8.2. Analyzing route 1 using scheduled RT...... 46 Chapter 6. Results ...... 48 Chapter 7. Summary and Conclusions ...... 52 7.1. Conclusion ...... 52 7.2. Future Research ...... 52 REFERENCES ...... 53 APPENDIX A ...... 55 APPENDIX B ...... 56  Route 23 ...... 56  Route 66 (66_6) ...... 61  Route 77 ...... 65  Route 28 ...... 69  Route 39 (39_3) ...... 73  Route 99 (99_7) ...... 77  Route 9 ...... 81  Route 89 ...... 85

vii

LIST OF TABLES

Table 1 Description of Heartbeat Data ...... 10 Table 2 Description of Time-point Data ...... 12 Table 3 the Components of Announcement record Data ...... 13 Table 4 lost time components and their values...... 22 Table 5 data grouping periods...... 23 Table 6 Annual cost component summery for a period p ...... 37 Table 7 trips throughout a year for Route 1 , MBTA ...... 41 Table 8 the data sources and its outputs ...... 42 Table 9 Route 1 variables and corresponding adjustments (using AVL data) ...... 43 Table 10 Traffic Congestion Impact on Route 1, MBTA, Boston...... 45 Table 11 Route 1 variables and corresponding adjustments (using scheduled RT) ...... 46 Table 12 Traffic Congestion Impact on Route 1(using scheduled running time data) ..... 47 Table 13 list of the chosen routes ...... 48 Table 14 Summary Annual Impact of Traffic Congestion on the chosen routes ...... 49 Table 15 Traffic congestion impact per Veh-hr ...... 51 Table 16 Abbreviations ...... 55 Table 17 Route 23 variables and corresponding adjustments (using AVL data) ...... 57 Table 18 Traffic Congestion Impact on Route 23 (Using AVL Data) ...... 58 Table 19 Route 23 variables and corresponding adjustments ( using scheduled running time) ...... 59 Table 20 Annual Impact of The Traffic Congestion Impact on Route 23 (Using Scheduled Running Time Data) ...... 60 Table 21 Route 66 variables and corresponding adjustments (using AVL data) ...... 61 Table 22 Traffic Congestion Impact on Route 66 (Using AVL Data)...... 62 Table 23 Route 23 variables and corresponding adjustments ( using scheduled running time) ...... 63 Table 24 Traffic Congestion Impact on Route 66(using scheduled running time data) ... 64 Table 25 Route 77 variables and corresponding adjustments (using AVL data) ...... 65 Table 26 Traffic Congestion Impact on Route 77 (Using AVL Data)...... 66 Table 27 Route 77 variables and corresponding adjustments ( using scheduled running time) ...... 67 Table 28 Traffic Congestion Impact on Route 77(using scheduled running time data) ... 68 Table 29 Route 28 variables and corresponding adjustments (using AVL data) ...... 69 Table 30 Traffic Congestion Impact on Route 28 (Using AVL Data)...... 70 Table 31 Route 28 variables and corresponding adjustments ( using scheduled running time) ...... 71 Table 32 Traffic Congestion Impact on Route 28(using scheduled running time data) ... 72 Table 33 Route 39 variables and corresponding adjustments (using AVL data) ...... 73 Table 34 Traffic Congestion Impact on Route 39 (Using AVL Data)...... 74 Table 35 Route 39 variables and corresponding adjustments ( using scheduled running time) ...... 75

viii

Table 36 Traffic Congestion Impact on Route 39(using scheduled running time data) ... 76 Table 37 Route 99 variables and corresponding adjustments (using AVL data) ...... 77 Table 38 Traffic Congestion Impact on Route 99 (Using AVL Data)...... 78 Table 39 Route 99 variables and corresponding adjustments ( using scheduled running time) ...... 79 Table 40 Traffic Congestion Impact on Route 99(using scheduled running time data) ... 80 Table 41 Route 9 variables and corresponding adjustments (using AVL data) ...... 81 Table 42 Traffic Congestion Impact on Route 9, MBTA, Boston ...... 82 Table 43 Route 9 variables and corresponding adjustments ( using scheduled running time) ...... 83 Table 44 Traffic Congestion Impact on Route 9(using scheduled running time data) ..... 84 Table 45 Route 89_ variables and corresponding adjustments (using AVL data) ...... 85 Table 46 Traffic Congestion Impact on Route 89_, MBTA, Boston ...... 86 Table 47 Route 89 variables and corresponding adjustments ( using scheduled running time) ...... 87 Table 48 Traffic Congestion Impact on Route 89(using scheduled running time data) ... 88 Table 49 Route 89_2 variables and corresponding adjustments (using AVL data) ...... 89 Table 50 Traffic Congestion Impact on Route 89_2, MBTA, Boston ...... 90 Table 51 Route 89.2 variables and corresponding adjustments ( using scheduled running time) ...... 91 Table 52 Traffic Congestion Impact on Route 89.2 (using scheduled running time data) 92

LIST OF FIGURES

Figure 1 the multiple regression model result ...... 20 Figure 2 Demonstration to Poisson passenger arrival with 30 stops for a route ...... 39 Figure 3 route 1 [18] ...... 40 Figure 4 the estimated impact cost of traffic congestion on passenger ...... 50 Figure 5 Travel congestion impact per Veh-hr ...... 50 Figure 6 Comparing Measuring travel congestion impact on buses due to lower average speed using AVL data & Scheduled running time data ...... 51 Figure 7 Route 23, MBTA, Boston [18] ...... 56

Chapter 1. Introduction

1.1. Overview

Travel time of surface transit that shares right-of-way (ROW) with general traffic is affected by traffic congestion, leading to increased delay and unreliability. Since travel time and its reliability are the two most important determinants of travel mode choice, when transit suffers from traffic congestion, it makes transit less attractive, creating a vicious cycle with more cars on the road causing even more traffic congestion, making transit less and less attractive.

Even though traffic congestion affects both public transit and private vehicles, the effect on transit is greater because transit vehicles can’t change routes to avoid congestion. Thus, there is something unbalanced, even unfair, about private transportation imposing large delays on public transportation that share the same street.

Ideally, cities should manage street vehicles so that transit is protected from congestion, offering the public a high quality service as an alternative to driving. Some cities such as Zurich and Brussels do this well [1]. American cities, for the most part, do not. An important first step in connecting this imbalance is developing a method to measure the harm that traffic congestion imposes on surface transit. As Daniel Moynihan said, “We never do anything much about a problem until we learn to measure it” [2].

Quantifying the costs that traffic congestion imposes on transit is important for measuring the benefits of implementing transit priority including physical measures (e.g., bus lanes) and signal priority. Also, knowing how much traffic negatively impacts transit can also be used to justify using fuel taxes from general traffic to subsidize transit.

At this moment, the transit industry is in the era of “Big Data”. Automated data collection systems for transit have almost become part of every large agency. It increases the flexibility, ease, and accuracy of analyzing operations. Automated vehicle location

(AVL) systems and automated passenger counting (APC) systems have been used to measure transit performance in many respects. The goal of this research is to see how

AVL and APC can also be used to develop a method to measure how well (or poorly) the road network serves transit by measuring the impact that traffic congestion has on transit operations and users.

1.2. Research Objective

Transit systems routinely report performance measure such as total ridership, fraction of missed trips, fraction of trips that were on time, and number of safety incidents [3]. These measures report to the public how well transit is serving the customers. In the same manner, a measure is needed to report how well the traffic system serves public transportation. Thus, the objective is to develop a method that can be applied routinely as part of an annual “report card” to report how well the traffic system serves public transportation.

The goal of this thesis is to develop a method for measuring incremental delay and unreliability due to traffic congestion and how its cost can be quantified using routinely available datasets, namely, schedule data, automatic vehicle location (AVL) data and automatic passenger count (APC) data. Quantifying the costs that traffic congestion imposes on transit can be used:

a) As a tool to evaluate a city’s accommodation for public transportation.

b) As a tool to present the need for and the expected benefits of transit priority,

including both physical measures (e.g., bus lanes) and signal priority.

c) As justification for spending more of the fuel tax on transit as a way of mitigating

the harm that traffic does to transit.

The general approach this study follows is to compare actual running time for periods with congestion against running time during a period with very little traffic such as the period between 10:00 P.M. and 6:30 A.M. With AVL data, we can see how much greater are mean running times and running variability during periods with more traffic congestion. The complication is to exclude the effect of the greater passenger demand that typically occurs during the more congested periods. For this purpose, methods were developed to estimate the effect that passenger demand has on both mean running time and running time variability, so that the incremental impacts of traffic congestion can be identified.

1.3.Thesis Organization

This thesis is divided into seven chapters. Chapter 1 provides an introduction to the thesis objective and its approach. Chapter 2 reviews the related previous work on determining traffic congestion and its impact on the reliability of transit service with focus on using AVL data. Then, chapter 3 reviews all the data sources that were available for this research. Chapter 4 discusses the followed methodology of analyzing the AVL data in depth. Also, it discusses the found weaknesses of AVL data and finally it provides some suggestions to improve the automated vehicle location system at MBTA. Chapter 5 presents the research methodology and its application to 8 example routes. Chapter 6

4 presents the results of implementing the methodology on the sampled routes and a summary of the finding. Chapter 7 concludes the study and provides suggestions for future research.

Chapter 2. Literature review

The topic of this thesis is to develop a methodology for estimating transit delay due to traffic congestion using routinely collected data (AVL). This chapter will first provide a review of prior research focused on measuring congestion, reliability and using

AVL data.

Section 2.1 presents the pervious proposed methodology of measuring traffic congestion. Section 2.2 discusses using AVL data to study a transit system. Section 2.3 discusses the reliability and its impact on users and operators. Finally, section 2.4 discusses the rationale for conducting this research by discussing what has already been examined and how the previous research sets up this research.

2.1. Measuring Congestion

Quantifying transit delay due to traffic congestion has been explored by many researchers using different tactics to study travel time. The traditional method to study travel time is the “floating car method.” It involves collecting records while riding the bus or by from a car following a bus. The test vehicle technique is too costly to apply systematically because it is labor-intensive.

Thus, some researches have worked to develop models for buses travel time as a fraction of cars “General traffic” travel time (Levinson, 1983 [4]; McKnight, Levinson,

Ozbay, Kamga, & Paaswell, 2004 [5]). For example, McKnight et al conducted a study to determine the congestion impact on bus travel time by developing regression models that estimate bus travel time as a fraction of car travel time [5]. Then, the model was used to

6 estimate the proportion of bus travel time due to the increase in traffic time over free- flow conditions for car.

Some Dutch transit agencies use the bus speedometer to measure traffic delay directly as part of an AVL system [6]. The Dutch system is programmed to write a record when the speed drops below a certain level (e.g. 5 km/h) and when it rises above that threshold, while excluding time while serving a stop [6]. To our knowledge, there is no

U.S. AVL system that makes speedometer records in this way, making it impossible to measure delay directly.

2.2. AVL systems

Automatic Vehicle Location (AVL) systems are computer-based vehicle tracking systems that rely on Global positioning system (GPS). In last years, AVL system became widely part of every large transit system around the world .The general concept of the

AVL system is the same. However, the quality and level of the information that the AVL system can provide vary from an agency to other.

Many researchers have explored the usage of AVL data for improving transit system; one of the fundamental papers is TCRP Report 113 (Furth et al. 2006) [7]. Furth et al. provides a comprehensive guidance for how AVL archived data system can be collected and used to improve the performance and management of the transit operations [7].

The MBTA’s automated data collection system has been explored in some studies.

For example, Cham (2006) used the MBTA AVL/APC data to develop a practical framework to understand bus reliability. He studied variability in the running time using time point data for Silver Line, Washington Street service. Even though the Silver Line is supposed to function as Bus Rapid Transit route, Cham found that variability of running

7 time is high. Other researchers used the real time AVL data for off-line analysis such as

Gerstle (2009). He used AVL heartbeat data, which is free and open to the public from the MBTA, to explore buses position traces in real time. He used the real-time AVL published data to understand bus travel time variation which is the same as the archived heartbeat data, where the location of the bus is known every 60 seconds. However, there is a difficulty in determining the departure time for first stop and arrival time to last stop.

Therefore, this kind of data can be used to explore the variability in running time for a route but the start and end time has to be estimated.

2.3. Reliability

The reliability of transit service is critical to both operating agency and users.

Abkowitz et al. defines service reliability as “the invariability of service attribute which influence the decisions of the travelers and transportation providers.”[10].

Reliability affects the travel mode choice and departure time for travelers [10]. In more details, reliability attributes of concern to transit users include waiting time, in-vehicle time, transfer time (missed connection) and seat availability [11].

Passenger waiting time is sensitive to the reliability of the service. Muller and

Furth (2006) call the extra waiting time passengers suffer due to unreliability “hidden waiting time” [12]. They discussed how the short headway service is sensitive headway variability reliability, while long headway service is more related to high and low extremes of the schedule deviation distribution.

Transit agencies incur greater costs when reliability is poor. To illustrate, with less reliability transit agencies tend to increase the allowed time in order to limit the probability that a trip will start late because of the lateness of the pervious trip [7].

2.4. Conclusion

There are numbers of studies that have been conducted lately that are related to assessing the benefits of AVL data to study travel time or reliability of transit service.

However, none of them specifically included measuring delay due to the traffic congestion and its impact at the route or system level.

Chapter 3. Data Sources

Data used for this study was obtained from Massachusetts Bay Transportation

Authority (MBTA). No special data collections were attempted. All the used data are the data that MBTA collects routinely.

In this chapter, short description for the data sources that has been used in this research will be provided. The data sources are the automated vehicle location system and the automated passenger counting system.

3.1. Automated Vehicle Location system (AVL)

Every MBTA bus is part of the MBTA automated vehicle location system, which is based on Global Positioning System. The AVL data is used in real time for operation control, incident management and delivering information to users. Ideally, automated vehicle location data is also archived for later analysis such as evaluating performance and revising running time schedules. The types of automated vehicle location data that were available to this research are heartbeat, time-point, and announcement records data.

Each kind of AVL records will be described below.

3.1.1. Heartbeat Data

Heartbeat data shows bus location every 60 second. The data also has the time when a location message stamp. The main use of heartbeat data is to provide the real time information for operators and users. Table 1 shows what information each record of heartbeat data includes.

Heartbeat is “location-at-time” data which, as Furth et al. (2006) show, is not well suited to archived data analysis. It will not be used to measure the running time for bus routes in this study for different reasons.

First, departing first stop and arriving to last stop cannot be distinguished, and in the best-case scenario estimated running time could have two minutes error. Second, with heartbeat data the number of served stops per trip is hard to be obtained because it is difficult to distinguish between a stop that was for dwelling or for any other reason (such as traffic congestion).

Table 1 Description of Heartbeat Data

Data_Label (columns) Description logged_id Unique Number for each record (every 60 s) Calendar Date Message_type Null Latitude latitude of the bus location Longitude longitude of the bus location Adherence Adherence from schedule Odometer Odometer Validity Related to GPS signal Message_timestamp The Actual time for the record. Source_class N/A source_host Vehicle ID destination_class N/A destination_host N/A

3.1.2. Time-point Data

Time-point record is the most clear and straightforward type of AVL data. It is

“time at location” data; time at location data shows when a bus passes a specific location

(Furth et al. 2006). Time-points data shows “arrival” & “departure” time at key stops

(time-points) along a route, as determined by the bus computer using Global Positioning

System.

Also, it shows how long the bus was delayed from its schedule at each time-point.

Table 2 lists and describes each column in a Time-point record.

In order to inspect the quality of the data, we used the data to measure travel time for two MBTA routes (route 1 and 28). Unfortunately, the running time precision was high (unacceptable) at the first and last segment in both routes. My explanation for that is because of the complicated bus movements at terminals, stopping often at multiple locations, and also often going under a roof made the possibility of GPS error high. So, in some cases we got signal indicates that a bus departed the terminal but in actuality, the bus just was moving within the terminal.

However, time-point data is an accurate tool to capture headway deviation and departure deviation at each time-point (except the first and last one) because in each row of the data there is scheduled and actual arrival time to a time-point.

More details about what the time-points data contains are shown in the table below.

Table 2 Description of Time-point Data

Column Name Description Crossing_ID Unique ID for each record of data Service_Date Service Date. District District (goes with run.) Run The four-digit run number Block The block of the piece of work. Operator Badge number of the driver. Vehicle vehicle identifier Half_Trip_Id Unique Number identifies that Half_Trip. Route Rout name Direction Direction (“Inbound” or “Outbound”) Variation Route Variation such as (“39-3,” “39-_,” ) Stop Stop Number Time-point Time point abbreviation (5-letter code.) Order of time-point within HalfTrip (1st time-point of HalfTrip would be Time-point Order 1, etc.) Scheduled Scheduled time Arrival Actual Arrival time to the Time-point Departure Actual departure time Deviation from departure time (s), in which positive is early and Earliness negative is late. Scheduled Scheduled leading headway (seconds) Headway Headway Actual leading headway (seconds) PointType Startpoint, Midpoint, Endpoint. StandardType Schedule, Headway, Express. Standard N/A Include N/A

3.1.3. Announcement Record Data

Announcement record data has every announcement made along a route with the location and the timestamp. The records show whether the announcement was made internally or externally and whether it was made visually or audibly and also whether the door was open or not. However, there is no clear identification of trips on the data and no matching to schedule. More details about what the announcement record data contains is shown in the Table 3.

Table 3 the Components of Announcement record Data

Column Name Column Description ROUTE_ABBR Route abbreviation ROUTE_DIRECTION_NAME Inbound or outbound ANNOUNCE_DESC announcement description (e.g a name of a terminal) LOGGED_MESSAGE_BS1_ID Unique identifier for each record. CALENDAR_ID Date MESSAGE_TYPE_ID Null MESSAGE_TIMESTAMP Actual time when the record made (GMT time) LOCAL_TIMESTAMP Actual time when the record made (local time) SOURCE_HOST Vehicle ID LATITUDE LATITUDE LONGITUDE LONGITUDE ADHERENCE Schedule Adherence ODOMETER Odometer VALIDITY Associated With GPS signal MDT_BLOCK_ID Associate with the scheduled block MDT_RUN_ID Associate with the scheduled block EFFECTVE_SERVICE N/A It is a bus’s relative location in an ordered list of all stops for the STOP_OFFSET given run or block. CURRENT_DRIVER Driver ID ROUTE_OFFSET Related to the given MDT_RUN DIRECTION Direction (“Inbound” or “Outbound”) LONG_FIELD_1 Identifier for each announcement. LONG_FIELD_2 N/A Identifier to how the announcement was played. [e.g 545 indicates the announcement gets played externally (this occurs when the front doors are first opened and every 30 seconds after that if they remain open; the announcement gives the route and destination); 257 indicates the announcement gets LONG_FIELD_3 played internally both audibly and visually (this occurs as the bus approaches a stop); 273 indicates the announcement gets played internally visually only (this occurs when the front doors are first opened and every 30 seconds after that if they remain open)]. LONG_FIELD_4 Null LONG_FIELD_5 Null BYTE_FIELD_1 to 5 Null Announcement records data will be used in this research in order to obtain travel time between the first stop and the last stop. It is possible to capture last announcement made at the first terminal, when the bus door was open, by tracking the records in the

“LONG_FIELD_3” column. Also, announcement records data can be used to provide the number of stops that were serviced per trip, as it will be described in depth in the next chapter.

3.2. Automated Passenger Counting Data (APC)

Automated passenger counting data that will be used in this research are the MBTA

APC summary report and ride check data for individual trips. The fraction of buses that have APC is around 10% of the buses.

The APC summary report contains the average of passengers boarding and alighting for every scheduled trip, obtained from many observations.

Ride check data has the number of passengers getting on and off at each stop along with opening and closing door times for individual trips. The sample size for ride check data is not large compared to APC summary report. Ride check data is used to calculate the percentage of passengers boarding at first stop and alighting at last stop, since those passengers’ movements don’t affect the running time as measured from the announcement data (It will be described in depth in chapter 4). Ride check data will also be used to estimate a stop time model, as described in chapter 5.

Chapter 4. AVL Data Analysis Methodology As described in chapter 3, the AVL data that were used are announcement records and time-point data. In this chapter, the methodology of analyzing these data will be described.

4.1. Announcement record data processing

Announcement records will be used to determine the travel time for a bus between departing first stop and arriving to last stop. Also the data will be used to determine the number of stops that were served during each trip. The size of the data is large, for example, more than 2 million records were archived for Route 1 in three months only.

The announcement record data also includes records that are not useful, including “stop requested” announcements, “out of service” announcements and some repeated records.

For that, we wrote a code using Python to identify complete trips and determine each trip’s running time and the number of stops made during each trip.

The announcement data doesn’t include a trip ID. So, there is no field that gathers the data by trip. However, each announcement record has a vehicle_ID , a driver_ID, a unique ID and the direction of the bus whether it was running inbound or outbound.

Thus, the code’s main mechanism is to track each vehicle with its direction. Starting when the bus was at first stop and ending when it reached last stop, which helps distinguishing each trip records.

Basically, the code tracks each bus to get the last announcement played in the bus when the door was open at the first stop; that moment is considered as the departure time at the first stop. Then, we track the bus until we find the first announcement made when bus’s door was open at last stop, and consider it as the arrival time to the last stop.

The maximum possible time error for this methodology is 30 seconds because the announcements we tracked are played when the front door is first open and every 30 seconds after that if they remain open.

A bus with an open door was considered to be “at” a stop if it was with 50 m of the standard stop location. The results were viewed using ArcGIS in order to confirm that the stop locations were within the acceptable range.

Some criteria were added to the logic to confirm a complete trip and exclude odd data records.

1. If a trip announcement records are interrupted with “out of service”

announcement, the trip was eliminated.

2. If the driver’s ID changed in the middle of a trip, the trip was eliminated.

4.2. Time-point data processing

Time-point data is used to find the effect of the running time variability on passengers as it will be described in the methodology chapter.

Time-point data analysis process (code mechanism)

o For each individual trip, assign departure time from first stop as the trip time.

o Classify the trip date into either (weekday, Saturday or Sunday schedule)

[Taking in consideration holidays’ schedules].

o Calculate ideal and observed headways at each time-point.

o Grouped trips according to their trip time.

o Find arrival time deviation from scheduled time for each trip at each time-

point. (Also, departure deviation )

o Classified each period to high /low frequency service according to its average

actual headway at the second time-point.

The analysis code was written using Python by using data analysis libraries (Pandas and

Numpy). We ran the code to analyze four months data records; the data cover all the trips for MBTA from Aug 31 to Dec 27/2013 for all the chosen routes.

4.3. Evaluation & Suggestion for the Reviewed AVL Archived Data

The information that was obtained in this research from the AVL data is very useful but not all what we ideally would expect. The system might have been designed in a way that all that an analyst would need for off line analysis is time-points records. This section will suggest some changes in order to make the AVL system data more useful for off line analysis.

The found weaknesses and the proposed improvements are

 The announcement record data can be used to show where and when the door is

open but the name of the stop is not given unless the stop was terminal or time-

point. Thus, creating unique announcements for each single stop is suggested, or

at least noting the stop ID as a part of the announcement record.

 We have noticed in the data that some drivers turn the external announcement to

“out of service” before reaching the last terminal in order to show passengers who

are waiting at the last terminal that they can’t get into the bus at that time (because

there is shift change or any other reason). This is a problem because it is hard to

distinguish between if the “out of service” announcement was used to show

passengers that they couldn’t get into the bus or if the bus was really taken “out of

service”. Thus, creating new external announcement that the driver can use in

specific situations other than “out of service” like “no boarding allows at this

time” is suggested.

 It is hard to match announcement record data with time-point and heartbeat data

for a trip. Thus, creating a half_trip ID for each trip in all the datasets

(Announcement record and heartbeat data) is suggested.

 Although leaving first stop and arriving to the terminal are critical events, current

time-point data doesn’t capture the well. Thus, we suggest creating a unique

announcement for first terminal departure (its coordination should be set on a

point where the bus cannot stop and where leaving the stop is guaranteed) and

also creating an announcement for the arrival to last stop.

 During the research, I attempted to analyze Route 111. There was a difficulty

analyzing the trips that run (inbound) because there are two variations with

different starting points for the route and they have the same external

announcement even though the trips don’t start from the same places. Thus, the

announcement record needs a field to distinguish each route variation (time-point

data already has this feature).

Chapter 5. Methodology

The primary analysis will be based on the AVL data (plus some APC data). This chapter describes the stop time model in section 5.1 and the data organizing/grouping methodology in section 5.2. Then, traffic congestion impact will be discussed at two different levels; mean running time and variability in running time impact in sections 5.3

&5.4. Value of time for passengers and operating cost is discussed in section 5.5. Then, section 5.7 measures traffic congestion using a simple method that uses schedule data instead of AVL data. Finally, the methodology will be applied and demonstrated in detail for one route, Route 1 in section 5.8.

5.1. Stop Time Model

Stop time consists of acceleration delay, deceleration delay, dwell time, opening/closing door time, and return to traffic time. Dwell time, the longest component, is most directly related to demand. However, the other components are hard to measure using available automated data. Reasonable assumptions will be made where we try to model those components at a low traffic period. However, from AVL data a dwell time model was estimated.

5.1.1. Dwell Time Model

Since ride check data has the opening and closing door time as well as number of passengers getting ons and offs by stop, a multiple regression model can be performed to estimate the dwell time, defined as the time between doors open and doors close, as a function of the numbers of boarding and alighting passengers.

Figure (1) shows the multiple regression model results. Estimates are measured in seconds. 14,289 observations were used in the regression. The coefficient of determination or R2 is 0.64, which means the model explains 64% of the variability .The model shows as 9.79 s standard error against a mean dwell time of 29.42 s . Looking to the t-test and p-value, all variables are statistically significant. The F-value shows that the model overall is statically significant.

SUMMARY OUTPUT

Regression Statistics Multiple R 0.801711227 R Square 0.642740892 Adjusted R Square 0.642690876 Standard Error 9.795040477 Observations 14289

ANOVA df SS MS F Significance F Regression 2 2465902 1232951 12850.89191 0 Residual 14286 1370639 95.94282 Total 14288 3836541

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 1.634 0.089 18.445 0.000 1.460 1.807 ON 4.350 0.035 123.09 0.000 4.281 4.419 OFF 1.866 0.035 53.121 0.000 1.797 1.935

Figure 1 the multiple regression model result Using these results, dwell time will be estimated using equation (5.1)

Dwell Time = 1.63 + Tons * 4.35+ Toffs *1.87 (5.1)

At, the trip level, the part of dwell time dependent on passenger activity can be called passenger service time (Pax ST) ,given by

Pax ST = 4.35 Tons +1.87 Toffs (5.2)

Where

Tons = total passengers who boarded at trip, excluding the first stop.

Toffs = total passengers who alighted at trip, excluding the last stop.

Passenger activity at the first and last stop is excluded because as running time is measured from announcement data; dwell time at the first and last stop is not captured.

5.1.2. Lost time

Increased passengers activity most directly affects running time by increasing passengers’ service time. In addition, it leads to buses stopping more often. Therefore, it is necessary to estimate the parts of stopping time that are independent of passenger activity which we call lost time.

Lost time has several components, listed in Table 4.

- Lost time when door open, from regression model, is 1.6 s. It’s extra time for

the first passengers to board or alight, and for the drives to close the door after

the last passenger.

- Other lost time is assumed to be 2 second. That includes time between wheels

stooping and door opening, between door closing and wheels rolling, and

waiting to return to traffic.

- Acceleration and deceleration delay. Because this study focuses on finding the

effect of traffic congestion, time for a bus to accelerate, decelerate and return

to traffic at the low traffic period will be estimated to equal to the needed time

and any extra time that might occur at the other periods (e.g. peak periods)

will be considered as delay due to traffic congestion. The model that will be

followed on this study, assumes uniform acceleration (equation 5.3).

V V V 퐷푎푐푐 = 푡푎푐푐 − 푡푢푛푎푐푐 = – = (5.3) 푎푎푐푐 2 푎푎푐푐 2 푎푎푐푐

Where

Dacc = the acceleration delay tacc = time needed to accelerate tunacc = time needed to pass over the acceleration distance without stopping V = speed of non-stopping vehicle

aacc = the average acceleration rate

We assume that the applied acceleration rate is 1.5 mph/s and the non-

stopping bus runs at speed of 21 mph. Then the acceleration delay is 7 seconds.

The speed of 21 mph is mix of the buses that would otherwise pass at full speed

(30 mph) versus at a low speed due to signals and queuing. The deceleration rate is

assumed to be twice the acceleration rate. Thus, the deceleration delay equals to

half of the acceleration delay (3.5 second). o Together, then, lost time per stop is assumed to be 14.1 second (see Table 4).

Table 4 lost time components and their values.

Description Value Deceleration Delay 3.50 Lost Time “With door open" 1.60 Acceleration Delay 7 Other 2 Total Lost time 14.10

For trip as a whole, total lost time is

Lost_T Trip= 14.1 *( N_stop -1) (5.3)

Where

Lost_T Trip = lost time due to making stops for a trip. N_stop = number of served stop at a trip.

In our case study, I used (N_STOP -1) because captured running time for each trip starts from closing the door at first stop and ending when the door is open at last stop.

5.2. Grouping trips

Trips will be grouped into different periods of the week with relatively homogeneous running traffic conditions and demand. For the case of MBTA, we developed the set of periods show in Table 5.

Table 5 data grouping periods.

Day & Period Interval Interval Weekday Saturday Sunday

10:00 PM – 6:29 AM 0Low_Traffic 10:00 PM – 7:59 AM 6:30 AM – 6:59 AM 5Shoulder/Eve.

7:00 AM – 8:59 AM 1AM Peak 6Sat_Morning 7SUN_Morning 8:00 AM – 11:59 AM 9:00 AM – 1:29 PM 2Midday Base

1:30 PM – 3:59 PM 3Midday School 8Afternoon_WKEND 12:00 PM- 5:59 PM

4:00 PM – 6:29 PM 4PM Peak

6:30 AM – 9:59 PM 5Shoulder/Eve. 9Evening _WKEND 6:00 PM – 9:59 PM

5.3. Average lower speed impact

Using announcement record data, running time (RT)p and the number of stops served

(N_stops) for each trip are obtained as discussed in section (4.1). Then, mean adjusted running time for a period after accounting for demand, which means excluding serving passengers and serving stop effect out of observed running time, is

Adj(RT)p = RTp – 14.1 ( N_stop -1) - 4.35 Tonsp - 1.87 Toffsp (5.5)

Where ;

Adj(RT)p = Adjusted mean running time for period p.

RTp = mean running time for period p. N_stop = average number of served stop at a trip.

Tonsp = Average total passengers who boarded at trip, excluding the first stop (for period p).

Toffsp=Average total passengers who alighted at trip, excluding the last stop (for period p).

Thus, if running time of buses that run in mixed traffic is combination of a) Needed travel time b) Stop Time c) Delay due to traffic congestion d) Inherent randomness e) Operational control Then,

RTp includes all five components a,b,c,d and e. Adj(RT)p eliminate component b .

And Adj(RT)o (for low traffic period 0) also eliminates component c. Thus, the estimated incremental mean running time Del(RT)p for a period p due to traffic congestion is the difference between its Adj(RT)p and adj(RT)o for the low traffic period (see equation

5.6).

The reason why we use the adjusted running time at low traffic period as a base is that it gives the needed travel time during non-congestion time plus an estimation of all the inherent factors that affect net travel time for a bus such as road condition, drivers’ behavior and other factors that affect net travel time.

Del(RT)p = Adj(RT)p – Adj(RT)o (5.6)

Negative value means bus consumed more time at the low traffic period than the other periods, which indicates poor operating management for a route.

Del(RT)p reflect impact to operating agency .On the other hand, the impact on a passenger depends on the extra running time for the segment of route that his/her trip covers. Ideally, load per segment can be obtained using on-off data. Then, weighted segment level running time can be calculated in order to get total pax-min. Thus, the ideal method demands data collection are:-

A. Running time by stop (treat each stop as time-point, plus record door opening / closing)

B. Load by stop (requires balancing ons & offs)

Unfortunately, the ideal method can’t be implemented due to lack of data.

However, the method that will be followed in this research is to assume that passenger trip covers a certain fraction of the route. Then, the estimated increase on a passenger mean travel time due to traffic congestion (Del(TT)p ) will be equaled to the percentage of route length a passenger trip cover (% RT_L ) multiplied by the estimated incremental mean running time ( Del(RT)p)at period p:-

Del(TT)p = % RT_L * Del(RT)p (5.7)

For our case study, following Furth (1998), a passenger trip on average covers 40 % of the route length [16].

5.4. Variability in Running Time impact

Travel time variability leads to service unreliability, which imposes costs on both transit users and transit agencies. In one hand, variability in the travel time affects operating cost because it leads to an increase in trip’s allowed time* . On other hand, service unreliability increases passengers waiting time and their potential travel time [12].

Variability in running time “for the entire trip” uses to determine the variability impact on operating cost. However, passenger trip cost is more associated with variability at stop level. Thus, for our case study, announcement record data will be used to study the variability at the trip level & time-point data will be used to study the variability at the stop level only for the reasons that were discussed in chapter 2 and 3.

5.4.1. Variability at the trip level

By “Variability at the trip level”, we refer to the variability in the running time between first and last stop

Transit service that runs in mixed traffic has variability in the running time, which is unavoidable but controllable. For that, transit agencies uses different scheduled running time for different times of the day in order to reduce the effect of variability in running time on passengers and operating cost.

Thus, the observed running time at period p, could contain scheduled and unscheduled running time variability. The following section (5.4.1.1) will discuss how the unscheduled variability can be captured.

*Allowed time is scheduled running time plus recovery time for a trip.

5.4.1.1. Variations from the scheduled running time VFSch(RT)

The study period might include more than one scheduled running time (meaning we have variation in the scheduled running time at that period). Thus, in order to identify the traffic congestion impact on reliability, we have to identify the unscheduled variation at each period. In other words, the impact of traffic congestion on service reliability can be known by knowing how much the actual travel time varies from the scheduled using equation (5.12)

Let,

 The observed travel time are (tipj )

 The scheduled travel times are (Sip). Where i = scheduled trip. j = day. p = period of day. t = observed travel time. s = scheduled travel time.

t̿p = the mean observed running time for a period.

S̅P = the mean scheduled running time for a period. Fsch V (RT)P= estimated variance of the running time from scheduled running time in period p .

V(S)P= variance of scheduled travel time in period p.

V(RT)p = variance of (observed) travel time in period p .

Then, the observed variation in the running time for a period is

j 1 i V(푅푇) = ∑ ∑ (t − t̿ ) 2 (5.8) 푃 n − 1 ipj p

The variation in the scheduled running time for a period is

1 j i 푉(푆) = ∑ ∑ (S − S̅ ) 2 (5.9) 푝 n − 1 ip p

Ideally, we would estimate the mean squared deviation from scheduled running time, which we call the variance from scheduled running time, for a given period p, by

1 VFsch(p) = ∑i (t − s )2 (5.10) n−1 i i

Where the sum is over all trips in period p.

However, because AVL data used to measure running time (announcement data) doesn’t indicate trip number, it is not possible to match each trip with its pair trip that happened next day and the following day and so on. By using unmatched data, the variation from scheduled running time for a given period can be calculated as follow

퐹푆푐ℎ 2 2 푉 (푡) = 퐸(푡𝑖 − 푠𝑖) = 퐸{[(푡𝑖 − 휇푇) − (푠𝑖 − 휇푆) + (휇푇 − 휇푆)] } (5.11) 2 2 = 퐸(푡𝑖 − 휇푇) + 퐸(푠𝑖 − 휇푆) − 2퐸[(푡𝑖 − 휇푇)(푠𝑖 − 휇푆)]

(Expanding the square yields two other cross-product terms, but their values are zero.)

Rewriting the last term in terms of rts, the correlation between scheduled and actual running time in the period of analysis, and replacing population mean and variance with sample mean and variance where available, variation from scheduled running time is given by

퐹푆푐ℎ 2 푉 (푅푇)푃 = 푉(푅푇)푃 + 푉(푆)푃 + (S̅P − t̿p) − 2푟푡푠 √푉(푡) √푉(푠) (5.12)

Because this correlation cannot be measured with unmatched data, it must be assumed. It is both conservative (i.e., to avoid overestimation) and reasonable to assume strong correlation, since the aim of the scheduling function is to schedule longer running times when actual running times are longer. For the following case study, the assumption of rts = 0.8 will be followed.

5.4.1.2. Adjust running time variation for greater demand

Greater demand affects running time by affecting the number of passengers and number of stops. In order to adjust running time variation for greater demand, their variability should be determined and its effect subtracted from the running time variability.

Let,

 Serve Passengers Effect [Vserve (RT) p] is the part of the running time variability that

due to the variability in the number of boarding passengers per trip if we assumed

passenger arrival process followed Poisson distribution. then,

2 Vserve(RT)p = (OnTime + OffTime) * (boarding per trip) (5.13)

Where

OnTime = the estimated time needed for a passenger to board = 4.35 S.

OffTime = the estimated time needed for passenger to alight =1.85 S.

 Stopping Effect, Vstops(RT)p, is the portion of the running time variability due to

variability in the number of stops made per trips .

2 Vstops(RT)p = V(n_Stop)p * (LostTime) (5.14)

Where V(n_stop) = variation on the number of served stop. Lost Time = lost time per stop = 14.1 s. Note: V(n_stop) directly measured from AVL announcement data.

Thus, equation (5.15) shows the variability in running time that is not due to the effect of greater demand; the need to make additional stops, and longer service times.

AdjVFSch(RT)p = VFSch(RT)p – Vstops(RT)p – Vserve(RT)p (5.15)

5.4.1.3. Impact on Operating Cost

Running time variability affects operating cost because it is associated with layover time. The allowed time for cycle or half-cycle for a route is equal to the running time plus the layover time (or the recovery time). Furth et al. (2006) mentions that in order to limit the probability that a bus finishes one trip so late that its next trip starts late, allowed time for a route should be based on an extreme value such as the 95th-percentile running time [7]. While some of the agencies apply the 95 percentile rule at the round trip level, in this research we apply it to a one direction trip level (half-cycle) in order to match the MBTA methodology (for setting allowed time).

So, to find the extra deviation that due to traffic congestion:

Let’s consider that in any period, observed running time variation from the schedule for a period is a combination of:

a) Variation due to inherent randomness (e.g. arriving signals just after they turn

red; wheelchair use).

b) Variation due to operational control (e.g. whether dispatch is on time; holding

at time-points; difference in speed, acceleration, braking between operators).

c) Variation due to serve more or fewer passengers.

d) Variation due to making more or fewer stop.

e) Variation due to traffic congestion.

Then, o AdjVFSch(RT)p for period p represents components a,b and e . For period 0 ,

AdjVFSch(RT)o represents components a and b . Then, Incremental variation due to

traffic congestion (Ve,p ) is equal to the difference between adjusted running time at

period p and at low traffic period.

Ve,p = AdjVFSch(RT)p - AdjVFSch(RT)o (5.16)

Then, the estimated incremental recovery time (DelRec) can be represented as equal 1.64* sqrt[Variation due to traffic congestion]† as it is presented in equation (5.18).

Recoveryp =1.64√VFSch(RT)p (5.17)

DelRecp = 1.64 {√VFSch(RT)p – √VFSch(RT)p − 푉푒,푝} (5.18)

† It is about 95 percentile (with assuming random distribution)

5.4.2. Variability at stop level

The purpose of studying the variability at the stop level is to obtain the effect on passengers. Passenger’s reactions toward variability in running time is to budget more than the expected trip time in order to achieve a certain level of confidence to arrive to his/her destination not late. Thus, variability in running time of buses affects passengers’ riding and waiting time.

Since passengers arrival process to a stop is independent on published scheduled with high frequency service but dependent on schedule with low frequency service, the impact of variability on the waiting differ in each case as it will be described later in section (5.4.2.1 & 5.4.2.2). However, passenger’s behavior towards variability on travel time is the same with low or high frequency service as it will described in section

(5.4.2.3).

Our definition of high frequency service is that a bus route is operated with average headway equal to 13 min or less at that period.

5.4.2.1. Impact on waiting time “with high frequency service”

For high frequency service, the focus is on headway deviation because passenger arrival process is independent of schedule departure time and passengers always aim to the next trip. It is well known for such a case that the expected waiting time for passengers is given by

ℎ 푉(ℎ) 퐸[푊] = (1 + ) (5.19) 2 ℎ2

ℎ 푉(ℎ) The part that stems from variability is excess waiting time, given by ∗ ( ). 2 ℎ2

V(h) is partly due to traffic but also partly due to demand. Assuming on-time dispatching and independence in running time between two successive trips a and b,

Hb = tb-ta and so ,V(h) = 2V(t) where

t = running time from terminal to boarding stop involved variability generated from

demand; variability in stopping time and service time over this segment.

Because waiting time is dependent on the boarding stop, O-D matrix is needed to capture this level of data, which is not available for this research. In order to solve for this problem, we assumed that boarding stop is at a point at which 25% of route-level stopping and 33% of route-level service time has occurred (33%, not 25%, because boarding take more time than alighting and tend to be concentrated near the start of the route); then the adjusted headway variance for a given period is

AdjV(H)p = V(H)p – 0.5 [Vstops(RT)p – Vstops(RT)o] – 0.66 [Vpax(RT)p –Vpax(RT)o] (5.20)

Since low-traffic periods have no short headway service that can be used as a base for determining headway variation under low traffic conditions. However, the existence of headway variation on MBTA rapid transit lines indicates that there are causes for headway variation apart from traffic congestion, such as failing to start on time and variability in the speed operators choose. A reasonable assumption for headway variation

34 in the absence of traffic is 1 min2. Therefore, the incremental excess waiting time for a period is

퐻 AdjV(H)p − 1 DelWait = ∗ (5.21) p 2 퐻2

5.4.2.2. Impact on waiting time “with low frequency Service”

On other hand, for low frequency services, the focus is on departure time deviation from the schedule, because passengers’ arrival is dependent on the published schedule.

Following Furth & Muller (2006) [12], we assume that passengers limit their chance of missing the bus by arriving no later than the 2-percentile departure time

(relative to the schedule that is the 2-percentile departure deviation).

Thus, passengers’ excess waiting time is then the difference between 2-p departure deviation (DepDev0.02) and mean departure deviation (DepDevmean) from the boarding stop, as

ExcessWait = DepDevmean – DepDev0.02 (5.22)

Departure deviations at time-points can be calculated from time-point data. Since boarding tend to occur in the earlier part of a route, choose time-point closest to 25% of the way along the route.

Because excess waiting time applies only to lower demand periods that do not require high frequencies, no adjustment is made for demand. The impact of traffic on excess waiting time can be estimated as

DelWaitp = ExcessWaitp – ExcessWaito (5.23)

where

ExcessWaitp = excess waiting time at period p

ExcessWaito = excess waiting time at low traffic period

5.4.2.3. Impact on potential (Budgeted) Travel Time

Potential travel time is the extra time that people budget for travel but on average do not consume it.

In order to limit the probability of arriving late at a desired destination, we assume that people budget for arriving at the 95-percentile arrival time at destination stop.

Potential travel time is therefore 퐴푟푟95푝푐푡 − 퐴푟푟̅̅̅̅̅.

This can readily be calculated from time-point data; however, it is not amenable to adjustment to account for demand, and therefore it is difficult to isolate what part of it can be attributed to traffic congestion.

An approximate way to estimate potential travel time is to take the difference between the 95-percentile arrival time at a stop and the scheduled arrival time. If that step were the terminal this would be exactly the same as the needed recovery time. On average, a passenger’s destination stop will be toward the end of a route but not at the very end; we will assume that Potential travel time (Potentialp) is 75% of recovery layover, as

Potentialp = 0.75* Recoveryp = 0.75 * 1.64√VFSch(RT)p (5.24)

Therefore the incremental potential travel time due to traffic congestion (DelPotentialp) is

DelPotentialp = 0.75* DelRecp (5.25)

5.5. Value of time

According to the NTD website (data of 2012), the operations cost per bus/hr is

154.85 $/hr for MBTA [13]. However, traffic delays leave vehicle-miles unchanged. So, in this study 70% of the bus operation cost (108.39 $/hr) will be used to evaluate the cost of the delay due to traffic congestion since traffic delays leave vehicle –miles unchanged

[17].

U.S. DOT recommends using 12 $/h travel time values for personal and local trip

(within the city)[14]. The value is intended to reflect average cost of travel time value which is between 35-60% of average wages.

Potential travel time is the extra time that people budget for travel but on average do not consume it. Thus, it is reasonable to give it a unit cost less than the unit cost of in- vehicle time. (Otherwise, people arriving at a terminal before their budgeted time would sit in the bus until their budgeted arrival time.) Following Furth & Muller (2006), a unit cost of 75% of the value of in-vehicle time is assumed.

The ratio of waiting time cost to in-vehicle time should be more than 1. Therefore, the cost of excess waiting time will be considered 150% of the cost of in-vehicle [12].

Finally, to get the annual impact, we calculated for 56 Saturdays, 56 Sundays and

253 weekdays per year, based on the schedules the agency operates on holidays.

5.6. Summary

Traffic congestion increases cost to transit operators by increasing mean running time and running time variability. It increases costs to transit passengers by increasing riding time and by increasing service unreliability. This leads to increases in waiting time and potential (budgeted) travel time. The components of cost impact for a given period, are summarized in Table 6. The overall cost is then obtained by aggregating over periods.

Table 6 Annual cost component summery for a period p

Impact on operating agency Impact on passengers

DelRTp*tripsp* $108.39 0.4 DelRTp * (Ons/ tripp) * tripp *$12

Incremental Incremental to due cost increased (Average delay)

DelRecp*tripsp* $108.39 (DelWaitp * 1.5 + 0.75 DelRecp*0.75)* (Ons/ tripp) * tripp *$12

due to due

Incremental Incremental cost increased (Variability)

Where tripsp = numbers of trips per year in period p. ons/tripp = boarding per trip in period p. DelRTp = incremental running time, period p. DelRecp = incremental recovery time, period p. DelWaitp = incremental waiting time, period p.

5.7. AVL-Free Methodology

In this section, we also test to what extent the traffic congestion and its associated cost can be estimated using schedule data instead of AVL. The rationale for using the schedule running time data is based on the fact that scheduled running time ideally should reflect the observed running time (from AVL).

Travel time will be obtained from the scheduled running time. The number of stops made by a bus during a trip will be modeled, as it will be described in the following section. However, running time variability and its associated impact cannot be captured from schedule data only.

5.7.1. The Number of Stops Made Model.

The number of stop can be known by using AVL data [what we did]. However, without

AVL data, we can model the number of stops made based on a Poisson passenger arrival.

Let, m = movements (ons/offs) per stop

2 ∗ 푂푛푠/푡푟𝑖푝 푚̅ = (5.26) 푁

Where N= number of stops.

Then, the probability of a bus stopping at a given stop equals the probability of one or more people waiting to get on or off:

푃(푠푡표푝) = 1 − 푒−푚 (5.27)

Thus, the expected number of stops made in a trip is

푆푡표푝푠 푀푎푑푒 = 푁 ∗ (1 − 푒−푚) (5.28)

Figure 2 Demonstration to Poisson passenger arrival with 30 stops for a route

35 30 25 20 15

StopsMade 10 5 0 0 10 20 30 40 50 60 Ons/trip 5.7.2. Summary

Using scheduled running time will require following the same process as that we used for AVL data, except that the scheduled running time is used instead of observed running time (AVL); and the number of stops made is modeled instead of being observed.

The only impact that could be captured is the one that is due to lower average speed. The accuracy of the traffic congestion impact using secluded running time depends on the quality of the operations management for the transit service.

5.8. Application to MBTA Route 1

Route 1 is one of the key bus routes in Greater Boston area. It runs between Harvard Square and Dudley station. Harvard to Dudley is considered the inbound direction (see figure 3).

Figure 3 route 1 [18]

 The first step of analyzing Route 1 is to obtain the number of planned trips per day from the published schedule in order to weight the results of a period at different days (see Table 7).

Table 7 trips throughout a year for Route 1 , MBTA

Direction Period Day Scheduled Trip/day Scheduled Trip/year Scheduled Trips/year year’s of Share trips/period year’s of Share trips SAT 23 1288 18%

SUN 17 952 13% IB 0Low_Traffic WKDY 20 5060 69%

Total - - 7300 100% 20% SAT 37 2072 65%

IB 8Afternoon_WKEND SUN 20 1120 35%

Total - - 3192 100% 9% SAT 18 1008 58%

IB 9Evening _WKEND SUN 13 728 42%

Total - - 1736 100% 5% IB 1AM Peak WKDY 13 3289 3289 100% 9% IB 2Midday Base WKDY 20 5060 5060 100% 14% IB 3Midday School WKDY 13 3289 3289 100% 9% IB 4PM Peak WKDY 18 4554 4554 100% 13% IB 5Shoulder/Eve. WKDY 24 6072 6072 100% 17% IB 6Sat_Morning SAT 21 1176 1176 100% 3% IB 7SUN_Morning SUN 12 672 672 100% 2% Total 36340 100%

SAT 26 1456 18%

SUN 16 896 11% OB 0Low_Traffic WKDY 23 5819 71%

Total - - 8171 100% 22% SAT 37 2072 65%

OB 8Afternoon_WKEND SUN 20 1120 35%

Total - - 3192 100% 8% SAT 18 1008 58%

OB 9Evening _WKEND SUN 13 728 42%

Total - - 1736 100% 5% OB 1AM Peak WKDY 12 3036 3036 100% 8% OB 2Midday Base WKDY 20 5060 5060 100% 13% OB 3Midday School WKDY 14 3542 3542 100% 9% OB 4PM Peak WKDY 19 4807 4807 100% 13% OB 5Shoulder/Eve. WKDY 25 6325 6325 100% 17% OB 6Sat_Morning SAT 22 1232 1232 100% 3% OB 7SUN_Morning SUN 13 728 728 100% 2% Total 37829 100%

 Then, the results are weighted based on the number of trips per week (See table 7).  Then, we processed the data we have (different data sources) in order to get the needed inputs for the proposed methodology (See table 8).  Table 9 shows the first part of the methodology, where we adjusted all the needed values to capture traffic congestion impact.  Then, table 10 shows the annual impact of traffic congestion in $ and hours.  The used AVL data for the route covers the entire fall 2013 period trips (August 31 – December 27).  Note, abbreviation are defined in Appendix

Table 8 the data sources and its outputs

(Data_Source) Output APC Data Pax/trip %ons at First Stop % offs at last stop Announcement Record data Running time (RT) S(RT) standard deviation of (RT) (nStop) number of the serve stop during a trip S(nStop) standard deviation of (nStop) Scheduled Running time Running Time (RT ) Standard DEVIATION (RT ) Published Schedule Trip/yr Time-point_Data Headway Class (short, long) Headway mean Headway Standard deviation. Expected Departure Deviation 0.02 percentile departure deviation

Table 9 Route 1 variables and corresponding adjustments (using AVL data)

Rt. Dir. Period hdwy netOns netOffs RT nStop S(nStop) AdjRT AdjTT SFsch(RT) AdjSFsch(RT) (H) S(H) AdjS(H) DepDev DepDev0.02 Wait AdjPot'l 1 IB 0 LH 22.11 20.97 28.37 16.73 4.91 22.42 8.97 4.20 4.01 0.00 0.00 n/a 0.43 -2.48 2.91 5.88 1 IB 1 SH 64.55 61.92 43.03 24.72 3.21 30.85 12.34 6.06 5.95 9.52 4.73 4.70 0.00 0.00 5.94 8.73 1 IB 2 LH 64.90 54.81 43.01 24.25 3.40 31.13 12.45 6.77 6.67 0.00 0.00 n/a 0.67 -5.40 6.06 9.79 1 IB 3 SH 65.51 57.59 49.65 25.11 3.30 37.44 14.98 8.35 8.28 11.96 6.80 6.77 0.00 0.00 7.91 12.14 1 IB 4 SH 57.36 47.00 46.01 24.26 3.92 34.92 13.97 8.73 8.65 8.16 5.77 5.73 0.00 0.00 6.12 12.68 1 IB 5 SH 43.77 41.66 36.21 21.79 4.27 26.85 10.74 4.07 3.88 10.35 5.55 5.51 0.00 0.00 6.66 5.70 1 IB 6 SH 41.90 41.22 36.81 22.28 3.09 27.48 10.99 5.01 4.91 11.77 7.14 7.13 0.00 0.00 8.05 7.20 1 IB 7 LH 46.65 43.11 35.85 22.75 2.80 26.02 10.41 5.23 5.14 0.00 0.00 n/a -0.16 -4.40 4.24 7.54 1 IB 8 SH 50.35 48.53 42.36 24.06 3.04 31.78 12.71 5.94 5.85 12.78 5.74 5.73 0.00 0.00 7.68 8.59 1 IB 9 LH 38.51 37.72 36.94 22.69 3.43 27.88 11.15 4.62 4.51 0.00 0.00 n/a 1.98 -4.07 6.06 6.61 1 OB 0 LH 20.21 21.75 25.11 15.96 4.38 19.45 7.78 3.07 2.85 0.00 0.00 n/a 3.23 -1.29 4.52 4.18 1 OB 1 SH 50.14 57.11 34.01 21.60 3.60 23.75 9.50 5.93 5.82 9.45 5.41 5.38 0.00 0.00 6.27 8.54 1 OB 2 LH 55.60 59.61 35.98 22.27 3.30 25.10 10.04 4.81 4.68 0.00 0.00 n/a 4.50 -2.05 6.55 6.87 1 OB 3 SH 54.73 53.47 38.25 22.21 3.30 27.63 11.05 4.53 4.40 11.34 7.50 7.48 0.00 0.00 8.15 6.45 1 OB 4 SH 62.83 56.92 42.66 22.28 3.20 31.33 12.53 8.64 8.57 8.18 6.11 6.07 0.00 0.00 6.37 12.57 1 OB 5 SH 42.05 38.01 31.54 19.11 3.64 23.05 9.22 4.65 4.52 9.52 5.75 5.73 0.00 0.00 6.50 6.64 1 OB 6 SH 30.66 33.66 32.68 19.72 3.26 25.01 10.01 4.74 4.64 11.08 6.32 6.31 0.00 0.00 7.34 6.81 1 OB 7 LH 43.94 43.09 32.53 20.76 4.35 23.36 9.34 7.98 7.88 0.00 0.00 n/a 3.34 -1.23 4.57 11.56 1 OB 8 SH 48.96 44.13 37.08 21.60 3.12 27.32 10.93 6.81 6.73 12.63 6.05 6.03 0.00 0.00 7.76 9.88 1 OB 9 LH 42.54 34.60 30.89 19.26 3.33 22.44 8.98 2.90 2.71 0.00 0.00 n/a 3.69 -1.29 4.98 3.97

43 3 Negative sign indicate early departure

5.8.1. Annual Impact

Note, for more details about the unit of cost see section 5.5. The following equation was used to calculate the cost of the impact of traffic congestion on passenger and operating. Cost ($/yr) = Unit cost *[N_E/Y] * [∑TCI] (5.29) Where  Cost = cost of traffic congestion impact per year  Unit cost = ($/hr)  N_E/Y = number of effected trips or passengers

 ∑TCI= Annual traffic congestion impact (h/yr)

In the case, when the traffic congestion impact values were negative, values were set to be equal zero.

The total incurred loss due to traffic congestion on passengers and the agency is shown in table (10). The results show that the estimated annual traffic congestion cost impact on operating cost is $1.2 million and the cost impact on passengers is $6 million. The cost of unreliability due to traffic congestion on both passengers and operating cost is around $ 4.1 million/year.

Table 10 Traffic Congestion Impact on Route 1, MBTA, Boston.

Dir period hdwy trips/yr Ave (ons/trip) DelRT (min) DelRecov (min) DelTT (min) DelWait (min) DelPot'l (min) Total IB 0Low_Traffic Long_Headway 7300 26.3 0.0 0.0 0.0 0.0 0.0

IB 1AM Peak Short_Headway 3289 71.3 8.4 3.1 3.4 1.1 2.3

IB 2Midday Base Long_Headway 5060 71.2 8.7 4.3 3.5 3.1 3.2

IB 3Midday School Short_Headway 3289 75.6 15.0 6.9 6.0 1.9 5.1

IB 4PM Peak Short_Headway 4554 63.6 12.5 7.4 5.0 2.0 5.6

IB 5Shoulder/Eve. Short_Headway 6072 50.4 4.4 0.0 1.8 1.4 0.0

IB 6Sat_Morning Short_Headway 1176 48.9 5.1 1.4 2.0 2.1 1.1

IB 7SUN_Morning Long_Headway 672 50.8 3.6 1.8 1.4 1.3 1.4

IB 8Afternoon_WKEND Short_Headway 3192 59.0 9.4 3.0 3.7 1.2 2.2

IB 9Evening _WKEND Long_Headway 1736 44.8 5.5 0.8 2.2 3.1 0.6

OB 0Low_Traffic Long_Headway 8171 28.2 0.0 0.0 0.0 0.0 0.0

OB 1AM Peak Short_Headway 3036 66.9 4.3 4.7 1.7 1.5 3.5

OB 2Midday Base Long_Headway 5060 73.8 5.6 2.9 2.3 2.0 2.2

OB 3Midday School Short_Headway 3542 68.7 8.2 2.4 3.3 2.4 1.8

OB 4PM Peak Short_Headway 4807 70.1 11.9 9.2 4.8 2.2 6.9

OB 5Shoulder/Eve. Short_Headway 6325 50.8 3.6 2.6 1.4 1.7 2.0

OB 6Sat_Morning Short_Headway 1232 40.9 5.6 2.8 2.2 1.8 2.1

OB 7SUN_Morning Long_Headway 728 53.2 3.9 8.0 1.6 0.1 6.0

OB 8Afternoon_WKEND Short_Headway 3192 58.8 7.9 6.2 3.1 1.4 4.6

OB 9Evening _WKEND Long_Headway 1736 47.2 3.0 0.0 1.2 0.5 0.0

Total h/yr, INBOUND 4,213.2 1,698.9 109,516.2 58,407.4 86,072.2

Total h/yr, OUTBOUND 3,175.9 2,117.7 81,906.9 55,226.1 101,950.3

Total h/yr 7,389.1 3,816.6 191,423.0 113,633.5 188,022.5 unit cost ($/h) 108.4 108.4 12.0 18.0 9.0

45 Impact ($/yr) 800,977 413,699 2,297,076 2,045,403 1,692,202 7,249,376

5.8.2. Analyzing route 1 using scheduled RT. In this section, the scheduled running time is used instead of AVL data.

Table 11 Route 1 variables and corresponding adjustments (using scheduled RT)

Rt. Dir. Period RT Stdv(RT) Ave_Pax N_STOP Stop_time Net T.T. Delay /trip TT.Dealy min min PAX min min min min 1 IB 0 25.54 3.50 26.28 23.72 8.30 17.24 0.00 0.00 1 IB 1 37.92 3.95 71.27 27.83 13.93 24.00 6.76 2.70 1 IB 2 40.00 0.00 71.19 27.83 13.92 26.08 8.84 3.54 1 IB 3 45.92 2.63 75.60 27.87 14.39 31.54 14.30 5.72 1 IB 4 47.61 0.50 63.60 27.70 13.10 34.51 17.27 6.91 1 IB 5 34.54 2.13 50.38 27.23 11.62 22.92 5.68 2.27 1 IB 6 33.62 2.92 48.88 27.15 11.45 22.17 4.93 1.97 1 IB 7 31.58 3.45 50.81 27.26 11.67 19.91 2.67 1.07 1 IB 8 39.14 1.38 59.01 27.59 12.60 26.54 9.30 3.72 1 IB 9 34.65 2.18 44.81 26.86 10.96 23.69 6.45 2.58 1 OB 0 23.76 3.78 28.19 23.03 8.33 15.42 0.00 0.00 1 OB 1 36.00 0.00 66.89 25.85 13.01 22.99 7.57 3.03 1 OB 2 37.00 1.03 73.76 25.91 13.74 23.26 7.84 3.14 1 OB 3 40.00 2.08 68.65 25.87 13.20 26.80 11.38 4.55 1 OB 4 40.74 2.51 70.05 25.88 13.34 27.39 11.97 4.79 1 OB 5 30.16 3.18 50.85 25.48 11.26 18.90 3.48 1.39 1 OB 6 30.09 3.28 40.92 24.88 10.09 20.00 4.58 1.83 1 OB 7 28.38 4.11 53.16 25.56 11.52 16.87 1.44 0.58 1 OB 8 35.32 0.31 58.79 25.72 12.14 23.18 7.75 3.10 1 OB 9 30.97 2.18 47.24 25.31 10.85 20.12 4.70 1.88

Table 12 Traffic Congestion Impact on Route 1(using scheduled running time data)

Dir. Period Delay /trip /yr Trip Operation Cost Pax_delay (min) PaxEsti. Delay cost min/trip $/yr min/trip Pax/yr $/yr

IB 0 - 7,300 - - 191,861 - IB 1 6.8 3,289 40,152 2.7 234,412 126,718 IB 2 8.8 5,060 80,841 3.5 360,212 254,829 IB 3 14.3 3,289 84,959 5.7 248,648 284,409 IB 4 17.3 4,554 142,093 6.9 289,623 400,151 IB 5 5.7 6,072 62,329 2.3 305,877 139,032 IB 6 4.9 1,176 10,483 2.0 57,485 22,691 IB 7 2.7 672 3,245 1.1 34,146 7,300 IB 8 9.3 3,192 53,647 3.7 188,351 140,173 IB 9 6.4 1,736 20,228 2.6 77,798 40,140 OB 0 - 8,171 - - 230,318 - OB 1 7.6 3,036 41,506 3.0 203,065 122,928 OB 2 7.8 5,060 71,667 3.1 373,238 234,081 OB 3 11.4 3,542 72,818 4.6 243,171 221,369 OB 4 12.0 4,807 103,937 4.8 336,742 322,408 OB 5 3.5 6,325 39,722 1.4 321,626 89,440 OB 6 4.6 1,232 10,186 1.8 50,414 18,458 OB 7 1.4 728 1,895 0.6 38,702 4,462 OB 8 7.8 3,192 44,711 3.1 187,650 116,390 OB 9 4.7 1,736 14,733 1.9 82,004 30,817 IB Inbound-Total 497,977 1,415,444 OB The route _Total 401,176 1,160,353 Total 74,169 899,153 4,055,344 2,575,797

The results show that due to lower average speed, the estimated annual traffic congestion cost has impact on passengers about 2.3 million and 800 thousand on operating cost. Using AVL data, the corresponding figures were 2.5 million and 900 thousand, showing that, at least for this route, using schedule data yields a very similar estimate. Of course, using schedule data in place of AVL doesn’t allow one to estimate impacts due reliability.

Chapter 6. Results In this research we attempted to analyze 9 bus routes at Greater Boston area that have different demand level (see table 13). The used AVL data for the routes covers the entire fall 2013 period trips (August 31 – December 27). Table 13 list of the chosen routes

Route Def. Destinations (two ends stops) 1 Key route Harvard Square – Dudley station 23 Key route Ashmont Station - Ruggles Station 28 Key route Mattapan Station - Ruggles Station 39 Key route Forest Hills - Back Bay. 66 Key route Harvard square - Dudley Station 77 Key route Arlington Heights - Harvard Station 9 Regular route Copley square and City Point Bus Terminal 89 Regular route Clarendon Hill - Sullivan Station 89.2 Regular route Davis Square - Sullivan Station Boston Regional Medical Center -Wellington 99 Regular route Station

The annual impacts of traffic congestion on each of the routes are shown in the table below (table 14). The impact on the key bus route tends to be similar. The estimated average impact cost on a key bus route is equal to 6.8 million /year. The impact on operating cost is 1.14 million/year on average. The impact cost of unreliability alone is around 280,000 on operating cost and 3.3 million on passengers (see Table 14).

Table 14 Summary Annual Impact of Traffic Congestion on the chosen routes

Impact due to lower Variability impact Total Impact due to lower average speed average speed Estimated from AVL Estimated from the schedule Pax/yr Key? operating Pax operating Pax operating %capture Pax %capture 1 4,055,344 KEY 800,977 2,297,076 413,718 3,737,605 7,249,376 899,153 112% 2,575,797 112% 23 3,714,491 KEY 912,840 2,106,834 282,650 3,102,271 6,404,595 896,556 98% 2,056,136 98% 28 4,361,924 KEY 789,009 2,387,312 303,324 4,269,316 7,748,962 847,998 107% 2,534,759 106% 39_3 4,023,777 KEY 971,377 2,653,573 357,934 3,383,301 7,366,185 1,020,921 105% 2,776,231 105% 66-6 4,008,338 KEY 1,051,494 3,426,745 118,059 3,524,596 8,120,895 1,235,234 117% 4,024,789 117% 77 2,270,881 KEY 659,692 1,057,885 224,756 2,115,967 4,058,301 588,047 89% 934,875 88% 89 508,468 n/a 149,382 187,358 66,050 450,088 852,877 112,417 75% 135,163 72% 89.2 672,974 n/a 87,538 146,503 112,078 530,516 876,636 98,291 112% 174,454 119% 99-7 430,944 n/a 131,870 146,635 56,415 117,674 452,593 137,971 105% 147,721 101% 9 1,866,512 n/a 360,260 612,794 269,789.92 1,111,602 2,354,447 434,389 121% 804,713 131%

Table 14 shows that the variability impact on the operation cost on route 66 is equal to $ 118,054 /year. That doesn’t mean that the service at route 66 is more reliable than the other route. To illustrate that, when the variability in running time at low traffic period is high and exceeds the variability at other periods that will create negative variability impact on the route. Thus, in most cases negative variability impact is an indicator of poor control such as in route 66. For more details check table 21 and 22 in the appendix B.

Then, by normalizing the impact of traffic congestion on passengers according to the estimated annual ridership, the result shows that the highest impact on passengers is found on route 66 with 1.73$/pax-trip (see figure 4)

Average passenger impact of traffic congestion 2.10 1.73 1.80 1.49 1.53 1.50 1.50 1.40 1.40

1.20 1.11 0.92 0.90 $/Pax 0.61 0.60 0.30 - 1.00 23.00 28.00 39_3 66-6 77.00 89.00 99-7 9.00

Route Figure 4 the estimated impact cost of traffic congestion on passenger Also, The total cost of traffic congestion impact on passengers and operating cost are normalized according to the annual revenue hours, which are the sum of in service time and layover time, and the results are shown in table 15 and figure 5 .The highest impacts on operating cost per veh-hr is found on route 89 with value of $26. The impact on passengers per veh-hr for route 66, which is the highest, is $ 116.53.

Traffic congestion cost /Veh-hr 160 140 120 100 80 60 40 20 0 1 23 28 39_3 66-6 77 89 99-7 9

for operating cost for passengers

Figure 5 Travel congestion impact per Veh-hr

Table 15 Traffic congestion impact per Veh-hr

Traffic congestion cost /Veh-hr ROUTE Annual Pax/yr for operating for passengers $/pax- revenue hours cost trip 1.00 55,172 4,055,344 22.02 109.38 1.49 23.00 55,261 3,714,491 21.63 94.26 1.40 28.00 60,135 4,361,924 18.16 110.69 1.53 39_3 59,902 4,023,777 22.19 100.78 1.50 66-6 59,655 4,008,338 19.61 116.53 1.73 77.00 53,752 2,270,881 16.45 59.05 1.40 89.00 15,575 1,181,620 26.65 84.40 1.11 99-7 11,390 430,944 16.53 23.20 0.61 9.00 30,397 1,866,512 20.73 56.73 0.92

Finally, using scheduled running time instead of AVL data can capture on average 103% of the real impact due to lower average speed .The range of the captured real effect percentage is between 72% and 131% of the real impact. The results shows in many cases the schedule running time can be a good estimated to the traffic congestion impact on bus due to lower average speed (See figure 6).

5,000,000

4,000,000

3,000,000

2,000,000

1,000,000

0 Using Schedule running time data data time running Schedule Using 0 1,000,000 2,000,000 3,000,000 4,000,000 Using AVL Data impact on Pax Travel cost impact on Operating cost 45 degree line

Figure 6 Comparing Measuring travel congestion impact on buses due to lower average speed using AVL data & Scheduled running time data

Chapter 7. Summary and Conclusions 7.1. Conclusion

Even though it seems that leaving transit service without any congestion protection is the cheapest choice, transit service is incurring high loss due to traffic congestion on some routes. A case study was done in this research on 9 different bus routes. The results show that traffic congestion annual impact on operating cost for some routes may reach up to $1.7 million per route. Also, the loss on passenger may reach in some cases $6 million. Undoubtedly, passengers’ loss has its strong correlation with the service demand. In other words, higher travel cost leads to less likeability to maintain the same level of demand after a while. On the other hand, archived AVL data is very helpful for understanding how transit service performs. Time-point data is not all what the transit service analyzer would need to evaluate a service and suggest some improvements. Small things could be added to the system in order to make the data clear and easy to be analyzed such as a record for each different stop and a record for closing the bus’ door (that was discussed in depth on section 4.3). 7.2. Future Research

There are some gaps need to be filled in this research such as:-  In this research, for lack of data availability passengers travel distance were assumed. However, researches can look at the impact on passengers travel time using O.D matrix.  Considering traffic congestion impact on passenger’s transfer time since transfer time is sensitive to the service reliability.  Traffic congestion impact on future demand.

REFERENCES

1. Furth, P.G. "Public Transport Priority for Brussels: Lessons from Zurich, Eindhoven, and Dublin." Report to the Brussels Capital Region, Universite Libre de Bruxelles, 2005. 2. Moynihan, D.P. “The Politics and Economics of Regional Growth.” The public interest, 1978,51,3-21, quoted in Fielding, Gordon J. “Managing public transit strategically: a comprehensive approach to strengthening service and monitoring performance.” San Francisco: Jossey-Bass Publishers, 1987, pp. 59. 3. Massachusetts Bay Transportation Authority ScoreCard Archive. [Online] http://www.mbta.com/about_the_mbta/scorecard/default.asp?id=18476 4. Levinson H. S. Analyzing Transit Travel Time Performance. In Transportation Research Record 915, TRB, National Research Council,Washington, D.C., 1983, pp. 1-6. 5. McKnight, C., Levinson, H., Ozbay, K., Kamga, C., Paaswell, R.,Impact of traﬃc congestion on bus travel time in northern New Jersey. Transportation Research Record 1884, 2004, 27-35. 6. Muller, Th.H.J. and P.G. Furth, “Trip Time Analyzers: Key to Transit Service Quality,” Transportation Research Record 1760, 2001, pp. 10-19. 7. Furth, P.G., B. Hemily, T.H.J. Muller, J.G. Strathman,TCRP Report 113: Uses of Archived AVL–APC Data to Improve Transit Performance and Management, Transportation Research Board, National Research Council, Washington, D.C., 2006. 8. Cham, Laura Cecilia “Understanding Bus Service Reliability: A practical framework Using AVL/APC Data”. Thesis, Master of Science in Transportation. Massachusetts Institute of Technology. 2006. 9. Gerstle, David G., “Understanding Bus Travel time variation using AVL data”. Thesis, Master of Science in Transportation. Massachusetts Institute of Technology. 2009.

10. Abkowitz, M., L. Englisher, H. Slavin, R. Waksman, and N. Wilson. 1978. Transit service reliability. Report UMTA-MA-06-0049-78-1, United States Department of Transportation. 11. Ceder, A. (2007). Public Transit Planning and Operation: Theory, Modeling and Practice. Elsevier, Butterworth-Heinemann, Oxford, UK. 12. Furth, P.G. and Th.H.J. Muller, "Service Reliability and Hidden Waiting Time: Insights from AVL Data," Transportation Research Record 1955, 2006, pp. 79-87. 13. National Transit Database, Transit Agency Information, Boston, MA. [Online] http://www.ntdprogram.gov/ntdprogram/pubs/profiles/2012/agency_profiles/1003.pdf 14. Belenky, P., "The Value of Travel Time Savings: Departmental Guidance for Conducting Economic Evaluations," Office of the Secretary of Transportation, U.S. Department of Transportation, Washington D.C., 2011 15. Massachusetts Bay Transportation Authority, Bus Schedules & Maps. [Online] http://www.mbta.com/schedules_and_maps/bus/ 16. Furth, P.G., "Innovative Sampling Plans for Estimating Transit Passenger Miles, " Transportation Research Record 1618, 1998, pp. 87-95. 17. Walker, Jarrett. “on operating cost,” Human Transit 2014 Web. n.d . 18. Google map throughout Massachusetts Bay Transportation Authority website .http://www.mbta.com/schedules_and_maps/bus/routes/?route=1

APPENDIX A

Table 16 Abbreviations

Abbreviations Description Rt. Route ID hdwy Headway Class Dir Direction Period,0 0Low_Traffic Period,1 1AM Peak Period,2 2Midday Base Period,3 3Midday School Period,4 4PM Peak Period,5 5Shoulder/Eve. Period,6 6Sat_Morning Period,7 7SUN_Morning Period,8 8Afternoon_WKEND Period,9 9Evening _WKEND IB Inbound OB Outbound LH Long headway SH Short headway AVL Automated vehicle location MBTA Massachusetts Bay Transportation Authority TCI Traffic congestion impact RT Running Time nStop Average number of served stop at period p S(nStop) Standard deviation of (nStop) in period p TT Travel time H Headway S(H) Standard deviation of headway at period p DepDev 0.02 2 percentile of departure deviation

APPENDIX B

 Route 23 Route 23 runs between Ashmont Station and Ruggles Station. It is one of the key bus routes in Greater Boston area. Running from Ashmont Station to Ruggles Station is the inbound direction.

Figure 7 Route 23, MBTA, Boston [18]

Table 17 Route 23 variables and corresponding adjustments (using AVL data)

Rt. Dir. Period hdwy netOns netOffs RT nStop S(nStop) AdjRT AdjTT SFsch(RT) AdjSFsch(RT) (H) S(H) AdjS(H) DepDev DepDev0.02 Wait AdjPot'l (min) (min) (min) (min) (min) (min) (min) (min) (min) (min)

23 IB 0 LH 19.96 20.20 25.27 17.29 5.36 19.37 7.75 4.86 4.68 0.00 0.00 n/a 1.33 -1.58 2.91 6.86 23 IB 1 SH 39.69 34.21 36.12 20.89 4.48 27.51 11.00 5.36 5.22 5.39 4.52 4.47 0.00 0.00 4.59 7.65 23 IB 2 SH 50.35 47.57 36.21 23.47 3.71 25.79 10.32 5.79 5.68 11.70 6.79 6.76 0.00 0.00 7.82 8.33 23 IB 3 SH 44.35 41.99 38.85 22.28 4.38 29.33 11.73 6.27 6.14 7.83 7.07 7.04 0.00 0.00 7.11 9.01 23 IB 4 SH 32.62 35.13 36.50 21.75 4.46 28.17 11.27 3.94 3.75 7.87 5.99 5.95 0.00 0.00 6.21 5.50 23 IB 5 SH 35.97 37.14 33.20 20.51 4.75 24.85 9.94 6.58 6.45 10.90 7.20 7.16 0.00 0.00 7.83 9.46 23 IB 6 SH 36.20 35.96 31.52 21.30 3.66 23.00 9.20 4.63 4.51 12.20 7.74 7.72 0.00 0.00 8.55 6.62 23 IB 7 LH 34.32 32.42 26.93 20.19 3.61 18.92 7.57 3.46 3.30 0.00 0.00 n/a 2.81 -1.47 4.28 4.83 23 IB 8 LH 38.40 39.25 31.87 20.23 3.76 23.35 9.34 4.49 4.36 0.00 0.00 n/a 6.80 -1.30 8.10 6.40 23 IB 9 LH 26.83 26.67 25.63 16.41 4.25 19.23 7.69 3.11 2.90 0.00 0.00 n/a 5.65 -0.68 6.33 4.25 23 OB 0 LH 19.38 20.83 23.61 15.78 4.77 18.09 7.23 2.29 1.95 0.00 0.00 n/a 2.89 -2.31 5.20 2.86 23 OB 1 SH 27.90 24.66 30.73 17.62 4.77 24.03 9.61 4.59 4.41 5.37 4.05 3.98 0.00 0.00 4.21 6.47 23 OB 2 SH 51.27 48.83 35.82 21.80 3.83 25.69 10.28 3.72 3.53 11.22 6.62 6.59 0.00 0.00 7.56 5.18 23 OB 3 SH 51.51 60.48 43.98 23.97 3.58 32.96 13.18 4.84 4.71 8.17 5.79 5.75 0.00 0.00 6.13 6.91 23 OB 4 SH 43.42 47.61 43.14 23.37 3.59 33.25 13.30 7.06 6.98 7.41 5.32 5.28 0.00 0.00 5.61 10.23 23 OB 5 SH 40.70 43.35 31.37 21.36 4.31 22.28 8.91 3.00 2.75 11.29 7.06 7.03 0.00 0.00 7.85 4.03 23 OB 6 SH 28.84 29.11 30.24 19.24 3.85 22.95 9.18 3.94 3.79 11.98 5.93 5.91 0.00 0.00 7.46 5.56 23 OB 7 LH 30.60 31.88 27.64 19.88 2.98 19.99 8.00 2.58 2.42 0.00 0.00 n/a 3.35 -0.92 4.26 3.54 23 OB 8 LH 36.85 37.44 33.41 20.92 3.81 24.89 9.95 3.36 3.18 0.00 0.00 n/a 6.50 -1.31 7.82 4.66 23 OB 9 LH 27.47 32.61 27.96 19.14 3.99 20.69 8.28 3.20 3.01 0.00 0.00 n/a 6.37 -0.09 6.46 4.42 57

Table 18 Traffic Congestion Impact on Route 23 (Using AVL Data)

Dir period hdwy trips/yr Ave.(ons/trip ) DelRT DelRecov DelTT DelWait DelPot'l Total class (min) (min) (min) (min) (min)

IB 0Low_Traffic Long_Headway 7244 27.3 0.0 0.0 0.0 0.0 0.0

IB 1AM Peak Short_Headway 5566 49.2 8.1 0.9 3.3 1.8 0.6

IB 2Midday Base Short_Headway 5819 58.6 6.4 1.6 2.6 1.9 1.2

IB 3Midday School Short_Headway 5313 59.2 10.0 2.3 4.0 3.1 1.8

IB 4PM Peak Short_Headway 4554 40.6 8.8 0.0 3.5 2.2 0.0

IB 5Shoulder/Eve. Short_Headway 5566 44.0 5.5 2.8 2.2 2.3 2.1

IB 6Sat_Morning Short_Headway 1120 44.8 3.6 0.0 1.5 2.4 0.0

IB 7SUN_Morning Long_Headway 728 44.6 0.0 0.0 0.0 1.4 0.0

IB 8Afternoon_WKEND Long_Headway 3024 48.0 4.0 0.0 1.6 5.2 0.0

IB 9Evening _WKEND Long_Headway 1736 34.9 0.0 0.0 0.0 3.4 0.0

OB 0Low_Traffic Long_Headway 7244 26.4 0.0 0.0 0.0 0.0 0.0

OB 1AM Peak Short_Headway 5566 32.6 5.9 3.7 2.4 1.4 2.8

OB 2Midday Base Short_Headway 6072 59.5 7.6 2.4 3.0 1.9 1.8

OB 3Midday School Short_Headway 4807 65.6 14.9 4.3 5.9 2.0 3.2

OB 4PM Peak Short_Headway 5060 51.9 15.2 7.9 6.1 1.8 5.9

OB 5Shoulder/Eve. Short_Headway 5566 50.3 4.2 1.2 1.7 2.1 0.9

OB 6Sat_Morning Short_Headway 1120 36.8 4.9 2.8 1.9 1.4 2.1

OB 7SUN_Morning Long_Headway 728 38.3 1.9 0.7 0.8 0.0 0.5

OB 8Afternoon_WKEND Long_Headway 3080 45.7 6.8 1.8 2.7 2.6 1.4

OB 9Evening _WKEND Long_Headway 1680 40.5 2.6 1.6 1.0 1.3 1.2

Total h/yr, INBOUND 3,704.6 705.7 75,197.5 70,035.8 27,653.2

Total h/yr, OUTBOUND 4,716.4 1,901.7 100,372.0 52,369.1 72,233.8

Total h/yr 8,421.0 2,607.5 175,569.5 122,404.9 99,887.0

unit cost ($/h) 108.4 108.4 12.0 18.0 9.0

Impact ($/yr) 912,840 282,637 2,106,834 2,203,288 898,983 6,404,595 58

Table 19 Route 23 variables and corresponding adjustments ( using scheduled running time)

Rt. Dir. Period RT Stdv(RT) Ave_Pax N_STOP Stop_time Net T.T. Delay /trip TT.Dealy min min PAX min min min min 23 IB 0Low_Traffic 21.77 2.24 27.34 25.69 8.87 12.90 0.00 0.00 23 IB 1AM Peak 34.00 0.00 49.24 29.71 12.09 21.91 9.02 3.61 23 IB 2Midday Base 33.35 0.49 58.58 30.29 13.19 20.16 7.26 2.90 23 IB 3Midday School 34.14 1.49 59.15 30.32 13.26 20.89 7.99 3.19 23 IB 4PM Peak 34.44 2.01 40.63 28.75 10.97 23.48 10.58 4.23 23 IB 5Shoulder/Eve. 28.27 3.45 43.96 29.18 11.41 16.86 3.96 1.58 23 IB 6Sat_Morning 28.90 1.41 44.76 29.27 11.52 17.38 4.48 1.79 23 IB 7SUN_Morning 24.85 2.19 44.58 29.25 11.50 13.35 0.45 0.18 23 IB 8Afternoon_WKEND 28.43 1.85 48.03 29.60 11.94 16.49 3.59 1.44 23 IB 9Evening _WKEND 24.87 0.72 34.91 27.74 10.14 14.73 1.83 0.73 23 OB 0Low_Traffic 24.02 3.05 26.38 24.83 8.57 15.45 0.00 0.00 23 OB 1AM Peak 30.00 0.00 32.56 26.58 9.62 20.38 4.93 1.97 23 OB 2Midday Base 34.29 3.65 59.47 29.43 13.08 21.21 5.76 2.30 23 OB 3Midday School 42.89 3.77 65.58 29.62 13.76 29.14 13.68 5.47 23 OB 4PM Peak 46.80 1.64 51.89 29.06 12.21 34.59 19.14 7.66 23 OB 5Shoulder/Eve. 30.68 2.61 50.28 28.95 12.02 18.67 3.21 1.29 23 OB 6Sat_Morning 31.50 2.35 36.80 27.42 10.26 21.24 5.79 2.32 23 OB 7SUN_Morning 27.08 1.75 38.31 27.67 10.47 16.60 1.15 0.46 23 OB 8Afternoon_WKEND 32.73 1.57 45.73 28.58 11.46 21.27 5.82 2.33 23 OB 9Evening _WKEND 28.40 0.67 40.52 27.99 10.78 17.62 2.17 0.87

Table 20 Annual Impact of The Traffic Congestion Impact on Route 23 (Using Scheduled Running Time Data)

Rt. Dir. Period Delay /trip Trip /yr Operation Pax_delay (min) Total pax_yr Delay cost Cost min/trip $/yr min/trip pax/yr $/yr 23 IB 0Low_Traffic 0.0 7244.0 - 0.0 198060.9 0.0 23 IB 1AM Peak 9.0 5566.0 90,656 3.6 274087.5 197676.4 23 IB 2Midday Base 7.3 5819.0 76,305 2.9 340847.9 197915.0 23 IB 3Midday School 8.0 5313.0 76,666 3.2 314289.2 200818.4 23 IB 4PM Peak 10.6 4554.0 87,038 4.2 185011.7 156577.4 23 IB 5Shoulder/Eve. 4.0 5566.0 39,812 1.6 244678.7 77496.5 23 IB 6Sat_Morning 4.5 1120.0 9,068 1.8 50134.0 17974.3 23 IB 7SUN_Morning 0.5 728.0 594 0.2 32452.0 1173.0 23 IB 8Afternoon_WKEND 3.6 3024.0 19,624 1.4 145236.0 41734.0 23 IB 9Evening _WKEND 1.8 1736.0 5,752 0.7 60606.0 8892.5 23 OB 0Low_Traffic 0.0 7244.0 - 0.0 191067.0 0.0 23 OB 1AM Peak 4.9 5566.0 49,546 2.0 181223.9 71431.6 23 OB 2Midday Base 5.8 6072.0 63,165 2.3 361119.5 166343.3 23 OB 3Midday School 13.7 4807.0 118,833 5.5 315243.1 345081.2 23 OB 4PM Peak 19.1 5060.0 174,980 7.7 262541.7 402021.2 23 OB 5Shoulder/Eve. 3.2 5566.0 32,316 1.3 279881.3 71955.7 23 OB 6Sat_Morning 5.8 1120.0 11,714 2.3 41216.0 19088.8 23 OB 7SUN_Morning 1.2 728.0 1,515 0.5 27888.0 2570.0 23 OB 8Afternoon_WKEND 5.8 3080.0 32,381 2.3 140838.6 65565.2 23 OB 9Evening _WKEND 2.2 1680.0 6,589 0.9 68068.0 11821.5 IB Inbound-Total 405,516 900,257 OB The route _Total 491,039 1,155,878 Total 81,593 896,556 3,714,491 2,056,135

 Route 66 (66_6) Route 66 runs between Harvard square and Dudley Station. It is one of the major bus routes in Greater Boston area. Running from Harvard Station to Dudley Station is the inbound direction.

Table 21 Route 66 variables and corresponding adjustments (using AVL data)

Rt. Dir. Period hdwy netOns netOffs RT nStop S(nStop) AdjRT AdjTT SFsch(RT) AdjSFsch(RT) (H) S(H) AdjS(H) DepDev DepDev0.02 Wait AdjPot'l (min) (min) (min) (min) 0 (min) (min) (min) (min) (min) (min)

66 IB 0 LH 32.14 37.98 36.06 23.36 5.75 27.30 10.92 6.93 6.77 0.00 0.00 n/a 0.39 -2.01 2.40 9.94 66 IB 1 SH 64.69 62.95 52.22 28.77 4.21 39.05 15.62 5.34 5.18 9.48 5.19 5.15 0.00 0.00 6.16 7.60 66 IB 2 LH 56.14 48.16 49.90 28.34 4.45 37.90 15.16 5.97 5.82 0.00 0.00 n/a 2.29 -4.47 6.76 8.54 66 IB 3 SH 50.84 64.68 55.93 30.09 4.15 43.39 17.36 5.92 5.79 11.10 6.51 6.49 0.00 0.00 7.46 8.50 66 IB 4 SH 64.46 70.08 55.67 30.17 4.05 41.96 16.78 8.28 8.19 10.66 6.82 6.79 0.00 0.00 7.51 12.01 66 IB 5 LH 53.12 50.45 45.49 29.13 4.02 33.45 13.38 5.76 5.63 0.00 0.00 n/a 2.83 -4.37 7.20 8.25 66 IB 6 LH 48.18 45.72 46.23 28.62 2.76 34.83 13.93 10.45 10.40 0.00 0.00 n/a 2.41 -2.04 4.46 15.26 66 IB 7 LH 34.60 33.38 40.90 26.80 3.38 31.29 12.52 6.04 5.95 0.00 0.00 n/a 2.26 -1.88 4.14 8.73 66 IB 8 LH 60.74 60.98 53.17 30.38 2.71 39.96 15.98 9.55 9.50 0.00 0.00 n/a 5.41 -3.76 9.16 13.93 66 IB 9 LH 49.47 54.98 40.68 27.09 2.99 29.25 11.70 3.08 2.91 0.00 0.00 n/a 3.46 -2.58 6.04 4.26 66 OB 0 LH 27.58 32.66 39.16 20.71 4.64 31.51 12.61 6.92 6.81 0.00 0.00 n/a 2.78 -2.67 5.46 10.00 66 OB 1 SH 59.18 57.27 57.69 27.10 4.23 45.48 18.19 6.05 5.92 9.13 5.53 5.50 0.00 0.00 6.24 8.68 66 OB 2 LH 59.24 57.16 54.10 28.06 3.88 41.67 16.67 8.80 8.72 0.00 0.00 n/a 3.67 -4.10 7.77 12.79 66 OB 3 SH 62.24 63.50 62.15 28.13 3.84 49.28 19.71 8.56 8.47 11.20 8.00 7.98 0.00 0.00 8.46 12.43 66 OB 4 SH 65.91 66.70 63.69 27.52 4.14 50.60 20.24 6.02 5.88 10.05 7.12 7.09 0.00 0.00 7.55 8.62 66 OB 5 LH 38.38 57.97 50.86 25.05 4.56 40.62 16.25 6.04 5.90 0.00 0.00 n/a 2.15 -6.65 8.80 8.66 66 OB 6 LH 49.51 54.04 52.58 27.91 2.37 40.98 16.39 11.89 11.85 0.00 0.00 n/a 4.38 -1.42 5.81 17.38 66 OB 7 LH 55.29 44.23 46.37 25.71 2.81 35.17 14.07 11.71 11.67 0.00 0.00 n/a 3.15 -2.47 5.63 17.11 66 OB 8 LH 62.55 63.85 56.95 27.64 3.72 44.17 17.67 8.99 8.91 0.00 0.00 n/a 6.20 -2.69 8.89 13.06 66 OB 9 LH 45.72 41.15 47.27 23.68 3.76 37.34 14.94 5.76 5.65 0.00 0.00 n/a 3.09 -3.24 6.33 8.28

Table 22 Traffic Congestion Impact on Route 66 (Using AVL Data).

Dir period hdwy trips/yr Ave.(ons/trip) DelRT DelRecov DelTT DelWait DelPot'l Total class (min) (min) (min) (min) (min)

IB 0Low_Traffic Long_Headway 5446 43.9 0.0 0.0 0.0 0.0 0.0

IB 1AM Peak Short_Headway 3289 77.2 11.8 0.0 4.7 1.3 0.0

IB 2Midday Base Long_Headway 4807 63.1 10.6 0.0 4.2 4.4 0.0

IB 3Midday School Short_Headway 3542 74.4 16.1 0.0 6.4 1.9 0.0

IB 4PM Peak Short_Headway 3795 82.3 14.7 2.3 5.9 2.1 1.7

IB 5Shoulder/Eve. Long_Headway 4554 63.7 6.2 0.0 2.5 4.8 0.0

IB 6Sat_Morning Long_Headway 784 55.5 7.5 5.9 3.0 2.1 4.4

IB 7SUN_Morning Long_Headway 728 38.9 4.0 0.0 1.6 1.7 0.0

IB 8Afternoon_WKEND Long_Headway 2184 72.6 12.7 4.4 5.1 6.8 3.3

IB 9Evening _WKEND Long_Headway 1344 62.5 2.0 0.0 0.8 3.6 0.0

OB 0Low_Traffic Long_Headway 6261 39.3 0.0 0.0 0.0 0.0 0.0

OB 1AM Peak Short_Headway 3289 82.1 14.0 0.0 5.6 1.6 0.0

OB 2Midday Base Long_Headway 4301 79.0 10.2 3.1 4.1 2.3 2.3

OB 3Midday School Short_Headway 3542 73.3 17.8 2.7 7.1 2.8 2.0

OB 4PM Peak Short_Headway 4048 78.6 19.1 0.0 7.6 2.5 0.0

OB 5Shoulder/Eve. Long_Headway 4048 61.0 9.1 0.0 3.6 3.3 0.0

OB 6Sat_Morning Long_Headway 784 75.6 9.5 8.2 3.8 0.3 6.2

OB 7SUN_Morning Long_Headway 728 70.3 3.7 7.9 1.5 0.2 5.9

OB 8Afternoon_WKEND Long_Headway 2184 74.3 12.7 3.4 5.1 3.4 2.5

OB 9Evening _WKEND Long_Headway 1400 55.9 5.8 0.0 2.3 0.9 0.0

Total h/yr, INBOUND 4,490.6 383.0 129,480.4 95,374.1 20,920.2

Total h/yr, OUTBOUND 5,209.6 706.1 156,081.7 70,065.9 39,821.6

Total h/yr 9,700.1 1,089.1 285,562.1 165,440.0 60,741.7

unit cost ($/h) 108.4 108.4 12.0 18.0 9.0

Impact ($/yr) 1,051,494 118,054 3,426,745 2,977,920 546,676 8,571,461

Table 23 Route 66 variables and corresponding adjustments ( using scheduled running time)

Rt. Dir. Period RT Stdv(RT) Ave_Pax N_STOP Stop_time Net T.T. Delay /trip TT.Dealy min min PAX min min min min 66 IB 0Low_Traffic 30.07 4.03 43.92 32.86 12.28 17.79 0.00 0.0 66 IB 1AM Peak 49.54 6.58 77.17 35.51 16.34 33.19 15.40 6.2 66 IB 2Midday Base 45.89 3.77 63.07 34.92 14.74 31.15 13.36 5.3 66 IB 3Midday School 51.71 5.97 74.42 35.42 16.04 35.67 17.88 7.2 66 IB 4PM Peak 56.00 0.00 82.33 35.63 16.91 39.09 21.30 8.5 66 IB 5Shoulder/Eve. 41.72 6.99 63.67 34.95 14.81 26.91 9.12 3.6 66 IB 6Sat_Morning 36.43 4.47 55.52 34.35 13.83 22.60 4.81 1.9 66 IB 7SUN_Morning 35.62 3.59 38.94 31.86 11.52 24.09 6.30 2.5 66 IB 8Afternoon_WKEND 45.90 0.95 72.61 35.36 15.84 30.06 12.27 4.9 66 IB 9Evening _WKEND 40.38 3.76 62.52 34.88 14.68 25.70 7.90 3.2 66 OB 0Low_Traffic 34.95 4.69 39.35 30.64 11.28 23.67 0.00 0.0 66 OB 1AM Peak 58.00 6.75 82.09 33.73 16.44 41.56 17.89 7.2 66 OB 2Midday Base 51.00 0.00 79.00 33.67 16.10 34.90 11.23 4.5 66 OB 3Midday School 57.00 0.00 73.29 33.54 15.48 41.52 17.85 7.1 66 OB 4PM Peak 61.06 2.02 78.57 33.67 16.06 45.01 21.34 8.5 66 OB 5Shoulder/Eve. 48.56 7.50 60.99 33.06 14.09 34.47 10.80 4.3 66 OB 6Sat_Morning 43.14 4.88 75.56 33.60 15.73 27.41 3.74 1.5 66 OB 7SUN_Morning 37.08 4.37 70.27 33.46 15.15 21.93 -1.74 -0.7 66 OB 8Afternoon_WKEND 51.36 2.11 74.32 33.57 15.59 35.77 12.09 4.8 66 OB 9Evening _WKEND 42.56 5.33 55.92 32.73 13.49 29.07 5.40 2.2

Table 24 Traffic Congestion Impact on Route 66(using scheduled running time data)

Rt. Dir. Period Delay /trip Trip /yr Operation Cost Pax_delay (min) Total pax_yr Delay cost min/trip $/yr min/trip pax/yr $/yr 66 IB 0Low_Traffic 0.00 5446.00 0.00 0.00 239199.84 0.00 66 IB 1AM Peak 15.40 3289.00 91521.63 6.16 253828.57 312775.24 66 IB 2Midday Base 13.36 4807.00 116022.50 5.34 303157.25 324017.18 66 IB 3Midday School 17.88 3542.00 114431.67 7.15 263613.35 377135.21 66 IB 4PM Peak 21.30 3795.00 146036.53 8.52 312458.95 532444.91 66 IB 5Shoulder/Eve. 9.12 4554.00 75005.84 3.65 289942.46 211468.60 66 IB 6Sat_Morning 4.81 784.00 6811.70 1.92 43524.25 16745.67 66 IB 7SUN_Morning 6.30 728.00 8284.80 2.52 28350.00 14286.82 66 IB 8Afternoon_WKEND 12.27 2184.00 48404.96 4.91 158589.81 155648.33 66 IB 9Evening _WKEND 7.90 1344.00 19192.97 3.16 84028.00 53137.24 66 OB 0Low_Traffic 0.00 6261.00 0.00 0.00 246357.31 0.00 66 OB 1AM Peak 17.89 3289.00 106323.20 7.16 269979.91 386480.52 66 OB 2Midday Base 11.23 4301.00 87233.94 4.49 339767.05 305161.17 66 OB 3Midday School 17.85 3542.00 114216.01 7.14 259586.93 370674.95 66 OB 4PM Peak 21.34 4048.00 156026.75 8.53 318058.95 542872.91 66 OB 5Shoulder/Eve. 10.80 4048.00 78984.03 4.32 246890.05 213321.38 66 OB 6Sat_Morning 3.74 784.00 5301.35 1.50 59241.00 17738.82 66 OB 7SUN_Morning 0.00 728.00 0.00 0.00 51156.00 0.00 66 OB 8Afternoon_WKEND 12.09 2184.00 47720.54 4.84 162320.48 157057.25 66 OB 9Evening _WKEND 5.40 1400.00 13658.76 2.16 78288.00 33822.86 IB Inbound-Total 625,713 1,997,659 OB The route _Total 609,465 2,027,130 Total 61,058 1,235,177 4,008,338 4,024,789

 Route 77 77 route runs from Arlington Heights to Harvard Station. It is one of the key bus routes at Great Boston area. Running from Arlington Heights to Harvard Station is the inbound direction.

Table 25 Route 77 variables and corresponding adjustments (using AVL data)

Rt. Dir. Period hdwy netOns netOffs RT nStop S(nStop) AdjRT AdjTT SFsch(RT) AdjSFsch(RT) (H) S(H) AdjS(H) DepDev DepDev0.02 Wait AdjPot'l (min) (min) (min) (min) (min) (min) (min) (min) (min) (min)

77 IB 0 LH 11.50 11.63 22.97 12.85 5.66 18.99 7.60 3.78 3.52 0.00 0.00 n/a 1.51 -2.28 3.79 5.16 77 IB 1 SH 49.27 50.66 39.27 25.77 6.02 28.29 11.32 5.65 5.42 9.19 6.36 6.27 0.00 0.00 6.79 7.95 77 IB 2 SH 31.72 32.38 32.26 23.29 5.56 23.71 9.48 5.38 5.18 10.98 5.43 5.36 0.00 0.00 6.84 7.60 77 IB 3 SH 31.36 30.12 32.81 20.17 6.07 25.09 10.04 6.29 6.10 9.65 7.10 7.03 0.00 0.00 7.44 8.95 77 IB 4 SH 24.54 24.27 35.09 19.68 6.07 28.17 11.27 6.19 6.01 9.63 6.52 6.45 0.00 0.00 7.02 8.81 77 IB 5 SH 18.81 18.55 27.91 16.01 6.80 22.44 8.98 4.48 4.16 11.44 6.12 6.03 0.00 0.00 7.36 6.10 77 IB 6 SH 32.26 33.18 30.41 20.65 5.21 22.42 8.97 4.27 4.05 10.49 5.89 5.84 0.00 0.00 6.90 5.93 77 IB 7 LH 36.09 37.28 29.22 21.15 4.97 20.71 8.28 2.79 2.45 0.00 0.00 n/a 2.88 -1.52 4.40 3.60 77 IB 8 SH 35.24 36.13 32.01 20.88 5.68 23.65 9.46 3.50 3.18 13.06 16.24 16.21 0.00 0.00 16.62 4.67 77 IB 9 LH 20.26 20.87 26.48 15.86 4.59 20.87 8.35 3.70 3.51 0.00 0.00 n/a -0.37 -4.30 3.93 5.15 77 OB 0 LH 9.18 14.97 20.72 16.86 5.78 15.86 6.35 3.06 2.73 0.00 0.00 n/a 0.38 -2.99 3.38 4.00 77 OB 1 SH 21.11 28.30 28.64 18.49 5.21 22.12 8.85 5.56 5.40 10.44 6.96 6.92 0.00 0.00 7.54 7.93 77 OB 2 SH 14.23 19.03 26.66 19.12 4.75 20.77 8.31 3.37 3.16 10.26 5.88 5.84 0.00 0.00 6.82 4.63 77 OB 3 SH 25.84 35.72 31.33 22.86 5.53 23.21 9.28 4.25 4.02 10.43 8.15 8.10 0.00 0.00 8.40 5.89 77 OB 4 SH 33.14 44.61 37.93 25.48 5.13 28.38 11.35 4.41 4.20 9.82 7.46 7.42 0.00 0.00 7.75 6.16 77 OB 5 SH 24.61 36.40 27.16 22.87 5.72 19.10 7.64 3.81 3.52 10.49 7.10 7.04 0.00 0.00 7.65 5.17 77 OB 6 SH 14.46 19.37 26.34 17.27 5.11 20.86 8.34 3.58 3.35 11.11 6.43 6.39 0.00 0.00 7.42 4.91 77 OB 7 LH 18.96 20.36 23.85 17.40 4.91 17.98 7.19 5.36 5.22 0.00 0.00 n/a 5.26 -0.84 6.09 7.66 77 OB 8 SH 20.44 30.60 27.93 21.47 4.63 20.68 8.27 3.13 2.90 12.44 7.98 7.95 0.00 0.00 8.78 4.25 77 OB 9 LH 17.70 29.24 24.74 19.24 4.82 18.26 7.30 5.05 4.90 0.00 0.00 n/a 3.13 -2.79 5.91 7.19

Table 26 Traffic Congestion Impact on Route 77 (Using AVL Data).

Dir period hdwy trips/yr Ave.(ons/trip) DelRT DelRecov DelTT DelWait DelPot'l Total class (min) (min) (min) (min) (min)

IB 0Low_Traffic Long_Headway 7833 13.3 0.0 0.0 0.0 0.0 0.0

IB 1AM Peak Short_Headway 4048 53.4 9.3 2.9 3.7 2.1 2.2

IB 2Midday Base Short_Headway 6578 34.6 4.7 2.6 1.9 1.3 1.9

IB 3Midday School Short_Headway 4554 33.3 6.1 4.0 2.4 2.5 3.0

IB 4PM Peak Short_Headway 4554 26.8 9.2 3.9 3.7 2.1 2.9

IB 5Shoulder/Eve. Short_Headway 5819 21.0 3.5 1.0 1.4 1.5 0.7

IB 6Sat_Morning Short_Headway 1344 35.3 3.4 0.8 1.4 1.6 0.6

IB 7SUN_Morning Long_Headway 784 38.2 1.7 0.0 0.7 0.6 0.0

IB 8Afternoon_WKEND Short_Headway 3360 38.7 4.7 0.0 1.9 10.0 0.0

IB 9Evening _WKEND Long_Headway 1960 22.8 1.9 0.0 0.8 0.1 0.0

OB 0Low_Traffic Long_Headway 4291 18.2 0.0 0.0 0.0 0.0 0.0

OB 1AM Peak Short_Headway 3542 32.6 6.3 4.2 2.5 2.2 3.1

OB 2Midday Base Short_Headway 6831 22.6 4.9 0.7 2.0 1.6 0.5

OB 3Midday School Short_Headway 4301 40.1 7.3 1.9 2.9 3.1 1.5

OB 4PM Peak Short_Headway 4554 48.8 12.5 2.2 5.0 2.7 1.7

OB 5Shoulder/Eve. Short_Headway 2530 39.9 3.2 1.2 1.3 2.3 0.9

OB 6Sat_Morning Short_Headway 1232 22.6 5.0 0.9 2.0 1.8 0.7

OB 7SUN_Morning Long_Headway 728 26.7 2.1 3.9 0.8 2.7 2.9

OB 8Afternoon_WKEND Short_Headway 3360 35.1 4.8 0.3 1.9 2.5 0.2

OB 9Evening _WKEND Long_Headway 2072 32.1 2.4 3.4 1.0 2.5 2.5

Total h/yr, INBOUND 3,063.1 1,194.8 43,070.9 49,436.7 30,837.6

Total h/yr, OUTBOUND 3,022.6 878.6 45,086.2 40,861.7 23,672.9

Total h/yr 6,085.7 2,073.4 88,157.1 90,298.5 54,510.5

unit cost ($/h) 108.4 108.4 12.0 18.0 9.0 66

Impact ($/yr) 659,692 224,746 1,057,885 1,625,373 490,594 4,340,972

Table 27 Route 77 variables and corresponding adjustments ( using scheduled running time)

Rt. Dir. Period RT Stdv(RT) Ave_Pax N_STOP Stop_time Net T.T. Delay /trip TT.Dealy min min PAX min min min min 77 IB 0Low_Traffic 21.13 1.99 13.27 18.42 5.70 15.42 0.00 0.00 77 IB 1AM Peak 38.50 3.61 53.43 32.53 13.18 25.32 9.89 3.96 77 IB 2Midday Base 30.00 0.00 34.61 29.56 10.54 19.46 4.04 1.62 77 IB 3Midday School 30.00 0.00 33.33 29.21 10.32 19.68 4.26 1.70 77 IB 4PM Peak 34.00 0.00 26.76 26.95 9.11 24.89 9.47 3.79 77 IB 5Shoulder/Eve. 25.70 2.93 21.01 24.12 7.85 17.85 2.43 0.97 77 IB 6Sat_Morning 28.63 1.01 35.32 29.74 10.65 17.97 2.55 1.02 77 IB 7SUN_Morning 30.57 3.63 38.20 30.41 11.11 19.47 4.04 1.62 77 IB 8Afternoon_WKEND 31.25 1.44 38.65 30.50 11.17 20.08 4.65 1.86 77 IB 9Evening _WKEND 28.97 1.49 22.80 25.11 8.26 20.71 5.28 2.11 77 OB 0Low_Traffic 22.81 1.93 18.23 21.76 7.00 15.81 0.00 0.00 77 OB 1AM Peak 31.00 0.00 32.55 27.82 9.91 21.09 5.28 2.11 77 OB 2Midday Base 28.89 1.40 22.56 24.19 8.02 20.87 5.06 2.02 77 OB 3Midday School 32.41 1.54 40.11 29.39 11.06 21.35 5.54 2.22 77 OB 4PM Peak 37.67 3.01 48.82 30.49 12.23 25.44 9.64 3.85 77 OB 5Shoulder/Eve. 29.12 3.37 39.94 29.36 11.04 18.08 2.27 0.91 77 OB 6Sat_Morning 25.82 0.59 22.60 24.20 8.03 17.79 1.98 0.79 77 OB 7SUN_Morning 28.85 2.19 26.68 25.96 8.87 19.98 4.17 1.67 77 OB 8Afternoon_WKEND 29.60 0.91 35.09 28.43 10.32 19.28 3.48 1.39 77 OB 9Evening _WKEND 28.41 0.56 32.08 27.69 9.83 18.57 2.77 1.11

Table 28 Traffic Congestion Impact on Route 77(using scheduled running time data)

Rt. Dir. Period Delay /trip Trip /yr Operation Cost Pax_delay (min) Total pax_yr Delay cost min/trip $/yr min/trip pax/yr $/yr 77 IB 0Low_Traffic - 7,833 - - 103,915 - 77 IB 1AM Peak 9.9 4,048 72,349 4.0 216,302 171,184 77 IB 2Midday Base 4.0 6,578 48,037 1.6 227,687 73,626 77 IB 3Midday School 4.3 4,554 35,018 1.7 151,804 51,688 77 IB 4PM Peak 9.5 4,554 77,906 3.8 121,857 92,309 77 IB 5Shoulder/Eve. 2.4 5,819 25,509 1.0 122,262 23,733 77 IB 6Sat_Morning 2.6 1,344 6,196 1.0 47,466 9,690 77 IB 7SUN_Morning 4.0 784 5,727 1.6 29,949 9,687 77 IB 8Afternoon_WKEND 4.7 3,360 28,246 1.9 129,867 48,342 77 IB 9Evening _WKEND 5.3 1,960 18,712 2.1 44,694 18,893 77 OB 0Low_Traffic - 4,291 - - 78,209 - 77 OB 1AM Peak 5.3 3,542 33,803 2.1 115,305 48,727 77 OB 2Midday Base 5.1 6,831 62,461 2.0 154,077 62,384 77 OB 3Midday School 5.5 4,301 43,059 2.2 172,494 76,469 77 OB 4PM Peak 9.6 4,554 79,276 3.9 222,324 171,375 77 OB 5Shoulder/Eve. 2.3 2,530 10,389 0.9 101,053 18,375 77 OB 6Sat_Morning 2.0 1,232 4,411 0.8 27,838 4,413 77 OB 7SUN_Morning 4.2 728 5,490 1.7 19,421 6,485 77 OB 8Afternoon_WKEND 3.5 3,360 21,102 1.4 117,886 32,783 77 OB 9Evening _WKEND 2.8 2,072 10,355 1.1 66,472 14,710 Total IB Inbound-Total 317,699 499,153 OB The route _Total 270,347 435,722 Total 74,275 588,047 2,270,881 934,875

 Route 28 Route 28 runs from Mattapan Station to Ruggles Station. It is one of the key bus routes at Great Boston area. Running from Mattapan Station to Ruggles Station is the inbound direction.

Table 29 Route 28 variables and corresponding adjustments (using AVL data)

Rt. Dir. Period hdwy netOns netOffs RT nStop S(nStop) AdjRT AdjTT SFsch(RT) AdjSFsch(RT) (H) S(H) AdjS(H) DepDev DepDev0.02 Wait AdjPot'l (min) (min) (min) (min) (min) (min) (min) (min) (min) (min)

28 IB 0 LH 28.55 24.63 29.24 16.57 6.14 22.74 9.10 4.77 4.51 0.00 0.00 n/a -12.59 -3.51 -9.08 6.62 28 IB 1 SH 57.86 50.44 42.74 25.55 5.03 31.20 12.48 6.01 5.84 7.15 5.23 5.17 0.00 0.00 5.49 8.57 28 IB 2 SH 67.72 58.75 41.78 27.05 4.22 28.92 11.57 7.10 6.98 10.16 6.94 6.91 0.00 0.00 7.45 10.24 28 IB 3 SH 65.03 57.03 44.39 26.94 4.26 31.80 12.72 8.96 8.87 9.41 6.91 6.88 0.00 0.00 7.24 13.01 28 IB 4 SH 44.12 41.71 40.44 22.11 5.54 30.99 12.39 5.86 5.68 8.97 7.22 7.17 0.00 0.00 7.39 8.33 28 IB 5 SH 43.72 40.00 34.56 21.18 5.41 25.40 10.16 6.05 5.88 11.68 8.08 8.04 0.00 0.00 8.64 8.62 28 IB 6 SH 56.47 47.73 39.63 26.88 4.86 27.96 11.19 9.31 9.21 11.11 5.90 5.85 0.00 0.00 7.12 13.51 28 IB 7 LH 52.81 42.26 34.81 25.31 4.28 23.95 9.58 5.75 5.61 0.00 0.00 n/a 2.77 -2.81 5.59 8.22 28 IB 8 SH 53.21 44.33 39.34 26.28 4.72 28.16 11.26 6.76 6.62 12.40 6.50 6.46 0.00 0.00 7.91 9.72 28 IB 9 LH 33.04 29.41 32.79 20.87 4.66 24.81 9.93 5.02 4.86 0.00 0.00 n/a 3.59 -2.26 5.84 7.13 28 OB 0 LH 21.27 29.56 32.15 19.61 4.94 25.32 10.13 5.15 5.00 0.00 0.00 n/a 2.21 -2.67 4.88 7.33 28 OB 1 SH 27.32 32.97 37.79 18.20 5.54 30.74 12.30 5.21 5.02 8.45 5.72 5.67 0.00 0.00 6.16 7.36 28 OB 2 SH 49.26 57.70 42.14 24.40 5.52 31.27 12.51 3.59 3.27 10.08 6.82 6.76 0.00 0.00 7.35 4.79 28 OB 3 SH 75.04 90.89 53.39 28.76 4.04 38.59 15.44 6.63 6.50 9.93 6.90 6.86 0.00 0.00 7.36 9.54 28 OB 4 SH 58.44 76.67 52.81 25.99 5.45 40.31 16.12 9.39 9.27 9.36 6.73 6.67 0.00 0.00 7.10 13.60 28 OB 5 LH 45.61 60.67 39.93 24.76 5.08 29.14 11.66 7.17 7.04 0.00 0.00 n/a 2.85 -3.77 6.62 10.32 28 OB 6 SH 38.54 45.80 40.02 23.85 4.96 30.43 12.17 4.97 4.79 11.23 5.05 4.99 0.00 0.00 6.75 7.03 28 OB 7 LH 37.42 44.04 34.41 22.86 4.83 25.19 10.08 2.84 2.52 0.00 0.00 n/a 1.78 -2.99 4.76 3.70 28 OB 8 SH 45.52 57.09 42.51 24.64 4.76 31.87 12.75 6.00 5.85 12.89 6.63 6.59 0.00 0.00 8.15 8.58

28 OB 9 LH 35.83 48.27 37.79 23.60 4.43 28.38 11.35 4.00 3.81 0.00 0.00 n/a 2.99 -2.17 5.16 5.58 69

Table 30 Traffic Congestion Impact on Route 28 (Using AVL Data).

Dir period hdwy trips/yr Ave.(ons/trip) DelRT DelRecov DelTT DelWait DelPot'l Total class (min) (min) (min) (min) (min)

IB 0Low_Traffic Long_Headway 5981 33.0 0.0 0.0 0.0 0.0 0.0

IB 1AM Peak Short_Headway 4301 63.6 8.5 2.1 3.4 1.8 1.6

IB 2Midday Base Short_Headway 6831 73.7 6.2 3.9 2.5 2.3 3.0

IB 3Midday School Short_Headway 4301 73.1 9.1 7.0 3.6 2.5 5.2

IB 4PM Peak Short_Headway 4301 50.9 8.2 1.8 3.3 2.8 1.4

IB 5Shoulder/Eve. Short_Headway 5060 49.5 2.7 2.2 1.1 2.7 1.6

IB 6Sat_Morning Short_Headway 1232 61.7 5.2 7.5 2.1 1.5 5.6

IB 7SUN_Morning Long_Headway 952 57.1 1.2 1.7 0.5 14.7 1.3

IB 8Afternoon_WKEND Short_Headway 3304 58.8 5.4 3.4 2.2 1.6 2.5

IB 9Evening _WKEND Long_Headway 1568 37.4 2.1 0.6 0.8 14.9 0.4

OB 0Low_Traffic Long_Headway 4969 33.0 0.0 0.0 0.0 0.0 0.0

OB 1AM Peak Short_Headway 4301 37.1 5.4 0.0 2.2 1.8 0.0

OB 2Midday Base Short_Headway 7337 61.5 6.0 0.0 2.4 2.2 0.0

OB 3Midday School Short_Headway 4301 95.8 13.3 2.4 5.3 2.3 1.8

OB 4PM Peak Short_Headway 4554 80.9 15.0 6.9 6.0 2.3 5.1

OB 5Shoulder/Eve. Long_Headway 4301 64.4 3.8 3.3 1.5 1.7 2.4

OB 6Sat_Morning Short_Headway 1232 49.1 5.1 0.0 2.0 1.1 0.0

OB 7SUN_Morning Long_Headway 952 47.4 0.0 0.0 0.0 0.0 0.0

OB 8Afternoon_WKEND Short_Headway 3248 60.7 6.6 1.4 2.6 1.6 1.0

OB 9Evening _WKEND Long_Headway 1680 51.0 3.1 0.0 1.2 0.3 0.0

Total h/yr, INBOUND 3,253.1 1,795.2 82,519.0 97,073.5 88,130.6

Total h/yr, OUTBOUND 4,025.6 1,003.0 116,423.7 66,706.4 58,678.0

Total h/yr 7,278.7 2,798.2 198,942.7 163,779.9 146,808.6

unit cost ($/h) 108.4 108.4 12.0 18.0 9.0

Impact ($/yr) 789,009 303,310 2,387,312 2,948,039 1,321,278 7,748,962 70

Table 31 Route 28 variables and corresponding adjustments ( using scheduled running time)

Rt. Dir. Period RT Stdv(RT) Ave_Pax N_STOP Stop_time Net T.T. Delay /trip TT.Dealy min min PAX min min min min 28 IB 0Low_Traffic 26.02 3.74 33.01 30.79 10.66 15.36 0.00 0.00 28 IB 1AM Peak 38.94 1.03 63.56 35.81 15.00 23.94 8.58 3.43 28 IB 2Midday Base 38.00 0.00 73.68 36.31 16.17 21.83 6.47 2.59 28 IB 3Midday School 38.00 0.00 73.13 36.29 16.11 21.89 6.53 2.61 28 IB 4PM Peak 36.94 2.36 50.89 34.64 13.41 23.53 8.17 3.27 28 IB 5Shoulder/Eve. 31.10 2.99 49.55 34.46 13.23 17.87 2.51 1.00 28 IB 6Sat_Morning 32.14 2.01 61.73 35.68 14.79 17.35 1.99 0.80 28 IB 7SUN_Morning 32.24 2.46 57.08 35.31 14.22 18.02 2.66 1.06 28 IB 8Afternoon_WKEND 33.69 1.76 58.78 35.46 14.43 19.27 3.91 1.56 28 IB 9Evening _WKEND 29.68 0.82 37.43 32.11 11.43 18.25 2.89 1.16 28 OB 0Low_Traffic 27.68 3.88 32.97 30.23 10.52 17.16 0.00 0.00 28 OB 1AM Peak 36.00 0.00 37.08 31.41 11.23 24.77 7.61 3.05 28 OB 2Midday Base 40.76 4.36 61.54 34.82 14.56 26.20 9.03 3.61 28 OB 3Midday School 50.94 3.67 95.76 35.82 18.35 32.60 15.43 6.17 28 OB 4PM Peak 49.17 5.50 80.88 35.60 16.75 32.42 15.26 6.10 28 OB 5Shoulder/Eve. 34.24 7.28 64.44 35.00 14.90 19.33 2.17 0.87 28 OB 6Sat_Morning 37.00 3.07 49.08 33.64 12.99 24.01 6.84 2.74 28 OB 7SUN_Morning 34.71 2.31 47.42 33.42 12.77 21.94 4.77 1.91 28 OB 8Afternoon_WKEND 38.60 1.13 60.69 34.76 14.46 24.14 6.98 2.79 28 OB 9Evening _WKEND 36.43 1.18 50.96 33.88 13.24 23.19 6.03 2.41

Table 32 Traffic Congestion Impact on Route 28(using scheduled running time data)

Rt. Dir. Period Delay /trip Trip /yr Operation Cost Pax_delay (min) Total pax_yr Delay cost min/trip $/yr min/trip pax/yr $/yr 28 IB 0Low_Traffic - 5,981 - - 197,413 - 28 IB 1AM Peak 8.6 4,301 66,645 3.4 273,354 187,558 28 IB 2Midday Base 6.5 6,831 79,823 2.6 503,306 260,429 28 IB 3Midday School 6.5 4,301 50,744 2.6 314,511 164,310 28 IB 4PM Peak 8.2 4,301 63,448 3.3 218,870 142,971 28 IB 5Shoulder/Eve. 2.5 5,060 22,902 1.0 250,698 50,243 28 IB 6Sat_Morning 2.0 1,232 4,429 0.8 76,053 12,108 28 IB 7SUN_Morning 2.7 952 4,574 1.1 54,342 11,561 28 IB 8Afternoon_WKEND 3.9 3,304 23,328 1.6 194,207 60,718 28 IB 9Evening _WKEND 2.9 1,568 8,193 1.2 58,689 13,579 28 OB 0Low_Traffic - 4,969 - - 163,812 - 28 OB 1AM Peak 7.6 4,301 59,158 3.0 159,466 97,123 28 OB 2Midday Base 9.0 7,337 119,755 3.6 451,516 326,332 28 OB 3Midday School 15.4 4,301 119,931 6.2 411,846 508,520 28 OB 4PM Peak 15.3 4,554 125,513 6.1 368,317 449,502 28 OB 5Shoulder/Eve. 2.2 4,301 16,851 0.9 277,174 48,087 28 OB 6Sat_Morning 6.8 1,232 15,234 2.7 60,463 33,106 28 OB 7SUN_Morning 4.8 952 8,213 1.9 45,147 17,246 28 OB 8Afternoon_WKEND 7.0 3,248 40,963 2.8 197,119 110,083 28 OB 9Evening _WKEND 6.0 1,680 18,293 2.4 85,620 41,282 Total IB Inbound-Total 324,087 903,478 OB The route _Total 523,911 1,631,281 Total 74,706 847,998 4,361,924 2,534,759

 Route 39 (39_3) Route 39 variant (39_3)runs from Forest Hills to Back Bay. It is one of the key bus routes at Great Boston area. Running from Forest Hills to Back Bay is the inbound direction.

Table 33 Route 39 variables and corresponding adjustments (using AVL data)

Rt. Dir. Period hdwy netOns netOffs RT nStop S(nStop) AdjRT AdjTT SFsch(RT) AdjSFsch(RT) (H) S(H) AdjS(H) DepDev DepDev0.02 Wait AdjPot'l (min) (min) (min) (min) (min) (min) (min) (min) (min) (min) 39 IB 0 SH 13.57 20.73 24.34 15.28 5.48 19.36 7.74 3.85 3.61 11.23 4.65 4.59 0.00 0.00 6.58 5.30 39 IB 1 SH 56.27 72.32 41.43 24.96 3.50 29.47 11.79 5.29 5.16 5.96 3.72 3.66 0.00 0.00 4.14 7.58 39 IB 2 SH 48.43 69.24 36.91 23.86 3.96 25.87 10.35 4.72 4.57 10.81 6.34 6.30 0.00 0.00 7.26 6.71 39 IB 3 SH 53.38 67.82 40.20 24.02 4.01 28.80 11.52 6.12 6.00 11.14 6.57 6.53 0.00 0.00 7.51 8.81 39 IB 4 SH 38.90 52.22 39.70 20.25 5.35 30.73 12.29 5.87 5.70 7.05 5.92 5.86 0.00 0.00 6.01 8.36 39 IB 5 SH 28.46 37.68 33.14 19.31 4.99 25.60 10.24 5.41 5.25 9.66 5.27 5.21 0.00 0.00 6.27 7.71 39 IB 6 SH 26.81 37.86 34.97 22.04 3.51 26.90 10.76 5.05 4.96 10.81 4.76 4.74 0.00 0.00 6.45 7.27 39 IB 7 SH 20.50 28.94 31.22 21.00 3.44 24.13 9.65 4.96 4.88 12.12 4.27 4.25 0.00 0.00 6.81 7.15 39 IB 8 SH 34.43 47.88 35.82 21.43 4.49 27.03 10.81 5.39 5.25 11.18 7.31 7.27 0.00 0.00 7.98 7.71 39 IB 9 SH 21.83 26.69 30.07 16.98 4.67 23.91 9.56 2.95 2.70 12.82 8.04 8.01 0.00 0.00 8.93 3.96 39 OB 0 SH 19.44 15.10 22.92 13.90 4.54 18.01 7.20 2.93 2.69 12.85 5.64 5.60 0.00 0.00 7.66 3.95 39 OB 1 SH 38.57 37.62 32.31 17.75 4.26 24.41 9.76 3.63 3.43 6.16 3.94 3.88 0.00 0.00 4.34 5.04 39 OB 2 SH 41.52 33.04 34.03 19.76 4.04 25.58 10.23 4.28 4.12 11.76 4.71 4.67 0.00 0.00 6.82 6.05 39 OB 3 SH 43.51 56.49 42.29 21.94 3.73 32.46 12.98 6.74 6.65 10.35 6.32 6.29 0.00 0.00 7.11 9.75 39 OB 4 SH 66.02 53.03 43.72 21.74 4.11 32.41 12.96 9.48 9.40 7.33 5.35 5.30 0.00 0.00 5.62 13.78 39 OB 5 SH 48.45 39.73 31.33 19.64 4.22 22.20 8.88 4.00 3.81 10.07 5.41 5.36 0.00 0.00 6.49 5.59 39 OB 6 SH 16.97 11.76 28.72 15.70 3.99 23.66 9.47 3.10 2.92 10.73 4.88 4.85 0.00 0.00 6.47 4.29 39 OB 7 SH 13.58 9.41 25.15 14.80 4.28 20.63 8.25 2.48 2.23 12.68 4.94 4.91 0.00 0.00 7.30 3.28 39 OB 8 SH 40.49 27.27 33.15 18.89 3.78 25.17 10.07 4.47 4.33 11.58 7.54 7.51 0.00 0.00 8.24 6.35 39 OB 9 SH 43.13 34.45 28.50 18.31 3.39 20.24 8.09 2.82 2.62 12.85 6.33 6.31 0.00 0.00 7.98 3.84

Table 34 Traffic Congestion Impact on Route 39 (Using AVL Data).

Dir period hdwy trips/yr Ave.(ons/trip) DelRT DelRecov DelTT DelWait DelPot'l Total class (min) (min) (min) (min) (min)

IB 0Low_Traffic Short_Headway 9210 24.7 0.0 0.0 0.0 0.9 0.0

IB 1AM Peak Short_Headway 5060 75.8 10.1 2.5 4.0 1.0 1.8

IB 2Midday Base Short_Headway 6325 73.6 6.5 1.5 2.6 1.8 1.1

IB 3Midday School Short_Headway 3289 81.1 9.4 3.8 3.8 1.9 2.8

IB 4PM Peak Short_Headway 5819 60.5 11.4 3.3 4.5 2.4 2.5

IB 5Shoulder/Eve. Short_Headway 5819 42.0 6.2 2.6 2.5 1.4 1.9

IB 6Sat_Morning Short_Headway 1288 43.4 7.5 2.2 3.0 1.0 1.6

IB 7SUN_Morning Short_Headway 1120 33.2 4.8 2.0 1.9 0.7 1.5

IB 8Afternoon_WKEND Short_Headway 3752 49.9 7.7 2.6 3.1 2.3 1.9

IB 9Evening _WKEND Short_Headway 2128 30.8 4.5 0.0 1.8 2.5 0.0

OB 0Low_Traffic Short_Headway 4797 24.2 0.0 0.0 0.0 1.2 0.0

OB 1AM Peak Short_Headway 4807 51.1 6.4 1.1 2.6 1.1 0.9

OB 2Midday Base Short_Headway 5566 49.9 7.6 2.2 3.0 0.9 1.7

OB 3Midday School Short_Headway 4048 74.9 14.4 6.3 5.8 1.9 4.7

OB 4PM Peak Short_Headway 5566 73.8 14.4 10.7 5.8 1.8 8.0

OB 5Shoulder/Eve. Short_Headway 1265 54.3 4.2 1.7 1.7 1.4 1.3

OB 6Sat_Morning Short_Headway 1288 22.5 5.7 0.4 2.3 1.1 0.3

OB 7SUN_Morning Short_Headway 1120 18.0 2.6 0.0 1.0 0.9 0.0

OB 8Afternoon_WKEND Short_Headway 3696 44.6 7.2 2.6 2.9 2.4 1.9

OB 9Evening _WKEND Short_Headway 2128 48.1 2.2 0.0 0.9 1.5 0.0

Total h/yr, INBOUND 4,657.7 1,390.4 115,287.4 62,928.5 64,129.6

Total h/yr, OUTBOUND 4,303.4 1,911.6 105,843.7 44,661.8 96,612.0

Total h/yr 8,961.0 3,302.0 221,131.1 107,590.4 160,741.6

108.4 unit cost ($/h) 108.4 12.0 18.0 9.0 74

Impact ($/yr) 971,377 357,917 2,653,572 1,936,626 1,446,674 7,782,408

Table 35 Route 39 variables and corresponding adjustments ( using scheduled running time)

Rt. Dir. Period RT Stdv(RT) Ave_Pax N_STOP Stop_time Net T.T. Delay /trip TT.Dealy min min PAX min min min min 39 IB 0Low_Traffic 21.34 3.39 24.72 23.21 8.02 13.33 0.00 0.00 39 IB 1AM Peak 39.00 1.03 75.79 27.88 14.41 24.59 11.26 4.51 39 IB 2Midday Base 37.00 0.00 73.57 27.85 14.17 22.83 9.50 3.80 39 IB 3Midday School 37.00 0.00 81.09 27.91 14.97 22.03 8.71 3.48 39 IB 4PM Peak 39.00 0.00 60.54 27.63 12.77 26.23 12.90 5.16 39 IB 5Shoulder/Eve. 28.70 3.67 41.99 26.60 10.61 18.09 4.76 1.91 39 IB 6Sat_Morning 30.70 2.84 43.37 26.74 10.78 19.92 6.59 2.64 39 IB 7SUN_Morning 29.00 0.00 33.15 25.38 9.40 19.60 6.27 2.51 39 IB 8Afternoon_WKEND 32.06 0.53 49.93 27.21 11.57 20.49 7.16 2.86 39 IB 9Evening _WKEND 28.34 3.17 30.81 24.90 9.05 19.30 5.97 2.39 39 OB 0Low_Traffic 21.22 3.27 24.17 21.95 7.66 13.56 0.00 0.00 39 OB 1AM Peak 30.74 2.51 51.10 25.49 11.29 19.45 5.89 2.36 39 OB 2Midday Base 33.27 1.91 49.86 25.44 11.15 22.13 8.57 3.43 39 OB 3Midday School 38.25 3.00 74.88 25.92 13.85 24.40 10.84 4.34 39 OB 4PM Peak 42.00 0.00 73.78 25.91 13.74 28.26 14.71 5.88 39 OB 5Shoulder/Eve. 30.96 4.19 54.33 25.60 11.65 19.31 5.75 2.30 39 OB 6Sat_Morning 28.00 3.87 22.52 21.40 7.36 20.64 7.08 2.83 39 OB 7SUN_Morning 24.20 2.04 18.03 19.50 6.45 17.75 4.19 1.68 39 OB 8Afternoon_WKEND 31.68 0.71 44.63 25.16 10.54 21.14 7.59 3.03 39 OB 9Evening _WKEND 29.47 2.74 48.06 25.36 10.94 18.53 4.98 1.99

Table 36 Traffic Congestion Impact on Route 39(using scheduled running time data)

Rt. Dir. Period Delay /trip Trip /yr Operation Cost Pax_delay (min) Total pax_yr Delay cost min/trip $/yr min/trip pax/yr $/yr

39 IB 0Low_Traffic - 9,210 - - 227,651 - 39 IB 1AM Peak 11.3 5,060 102,974 4.5 383,510 345,593 39 IB 2Midday Base 9.5 6,325 108,556 3.8 465,318 353,634 39 IB 3Midday School 8.7 3,289 51,729 3.5 266,713 185,749 39 IB 4PM Peak 12.9 5,819 135,647 5.2 352,302 363,654 39 IB 5Shoulder/Eve. 4.8 5,819 50,070 1.9 244,335 93,094 39 IB 6Sat_Morning 6.6 1,288 15,332 2.6 55,860 29,444 39 IB 7SUN_Morning 6.3 1,120 12,691 2.5 37,128 18,629 39 IB 8Afternoon_WKEND 7.2 3,752 48,548 2.9 187,327 107,329 39 IB 9Evening _WKEND 6.0 2,128 22,948 2.4 65,559 31,306 39 OB 0Low_Traffic - 4,797 - - 115,954 - 39 OB 1AM Peak 5.9 4,807 51,181 2.4 245,650 115,814 39 OB 2Midday Base 8.6 5,566 86,177 3.4 277,528 190,269 39 OB 3Midday School 10.8 4,048 79,284 4.3 303,107 262,878 39 OB 4PM Peak 14.7 5,566 147,883 5.9 410,682 483,163 39 OB 5Shoulder/Eve. 5.8 1,265 13,150 2.3 68,729 31,635 39 OB 6Sat_Morning 7.1 1,288 16,475 2.8 29,008 16,430 39 OB 7SUN_Morning 4.2 1,120 8,484 1.7 20,188 6,771 39 OB 8Afternoon_WKEND 7.6 3,696 50,658 3.0 164,958 100,115 39 OB 9Evening _WKEND 5.0 2,128 19,135 2.0 102,270 40,721 Total

IB Inbound-Total 548,494 1,528,433

OB The route _Total 472,427 1,247,797

Total 78,091 1,020,921 4,023,777 2,776,231

 Route 99 (99_7) Route 99 (variant 99_7) runs from Boston Regional Medical Center to Wellington Station. Running from Boston Regional Medical Center to Wellington Station is the inbound direction .

Table 37 Route 99 variables and corresponding adjustments (using AVL data)

Rt. Dir. Period hdwy netOns netOffs RT nStop S(nStop) AdjRT AdjTT SFsch(RT) AdjSFsch(RT) (H) S(H) AdjS(H) DepDev DepDev0.02 Wait AdjPot'l (min) (min) (min) (min) (min) (min) (min) (min) (min) (min) 99 IB 0 LH 10.73 8.69 21.36 10.22 4.45 18.14 7.26 3.78 3.62 n/a n/a n/a 2.04 -1.91 3.96 5.30 99 IB 1 LH 29.41 16.69 32.34 17.54 2.70 25.80 10.32 7.37 7.32 n/a n/a n/a 5.14 -0.81 5.95 10.73 99 IB 2 LH 21.70 11.09 28.96 15.35 3.76 23.67 9.47 5.60 5.51 n/a n/a n/a 3.19 -0.44 3.64 8.08 99 IB 3 LH 21.76 20.60 30.88 14.54 3.38 25.48 10.19 3.83 3.71 n/a n/a n/a 2.20 -0.30 2.50 5.44 99 IB 4 LH 17.71 15.96 28.70 11.09 3.27 24.55 9.82 6.40 6.34 n/a n/a n/a 3.65 -0.16 3.81 9.30 99 IB 5 LH 20.34 13.82 26.35 13.08 4.69 21.60 8.64 2.86 2.60 n/a n/a n/a 3.93 -1.00 4.93 3.81 99 IB 6 LH 14.54 11.13 34.02 15.50 3.54 29.21 11.68 12.04 12.01 n/a n/a n/a 2.39 -0.73 3.12 17.62 99 IB 7 LH 18.43 5.96 24.85 10.83 3.05 21.02 8.41 5.54 5.48 n/a n/a n/a 0.27 -2.31 2.58 8.03 99 IB 8 LH 16.49 10.51 27.79 12.93 3.46 23.46 9.38 6.22 6.15 n/a n/a n/a 0.75 -2.14 2.90 9.02 99 IB 9 LH 9.76 5.52 25.49 9.94 2.39 22.51 9.00 4.63 4.58 n/a n/a n/a 2.14 -1.38 3.53 6.72 99 OB 0 LH 5.17 21.01 24.17 14.29 3.42 20.02 8.01 2.78 2.66 n/a n/a n/a 6.23 -1.27 7.50 3.90 99 OB 1 LH 25.48 28.73 29.36 15.59 3.44 23.19 9.28 4.70 4.60 n/a n/a n/a 8.69 0.15 8.54 6.75 99 OB 2 LH 14.23 20.77 28.63 13.71 4.10 23.96 9.58 4.73 4.61 n/a n/a n/a 3.70 -0.67 4.37 6.77 99 OB 3 LH 14.34 28.66 30.86 17.69 4.08 25.00 10.00 2.57 2.35 n/a n/a n/a 2.99 -1.58 4.57 3.44 99 OB 4 LH 19.03 29.00 32.42 18.34 4.63 26.07 10.43 3.32 3.11 n/a n/a n/a 2.84 -2.15 4.99 4.56 99 OB 5 LH 16.33 33.54 28.58 17.26 4.12 22.53 9.01 2.92 2.72 n/a n/a n/a 3.15 -1.77 4.91 3.99 99 OB 7 LH 7.76 13.43 24.98 12.45 2.42 21.31 8.52 4.60 4.56 n/a n/a n/a 0.59 -2.25 2.84 6.68 99 OB 8 LH 11.75 20.39 25.17 15.19 3.67 20.35 8.14 2.79 2.62 n/a n/a n/a 2.67 -0.97 3.64 3.85

99 OB 9 LH 5.44 24.22 24.11 13.95 3.98 19.92 7.97 3.44 3.30 n/a n/a n/a 4.89 0.32 4.57 4.84 77

Table 38 Traffic Congestion Impact on Route 99 (Using AVL Data).

Dir period hdwy trips/yr Ave.(ons/trip) DelRT DelRecov DelTT DelWait DelPot'l Total class (min) (min) (min) (min) (min)

IB 0Low_Traffic Long_Headway 1601 11.1 0.0 0.0 0.0 0.0 0.0

IB 1AM Peak Long_Headway 1012 29.6 7.7 6.0 3.1 2.0 4.5

IB 2Midday Base Long_Headway 1518 23.6 5.5 3.0 2.2 0.0 2.3

IB 3Midday School Long_Headway 1265 25.2 7.3 0.2 2.9 0.0 0.1

IB 4PM Peak Long_Headway 1518 19.7 6.4 4.4 2.6 0.0 3.3

IB 5Shoulder/Eve. Long_Headway 1265 21.3 3.5 0.0 1.4 1.0 0.0

IB 6Sat_Morning Long_Headway 112 15.1 11.1 13.6 4.4 0.0 10.2

IB 7SUN_Morning Long_Headway 224 20.3 2.9 3.0 1.2 0.0 2.2

IB 8Afternoon_WKEND Long_Headway 336 17.0 5.3 4.1 2.1 0.0 3.1

IB 9Evening _WKEND Long_Headway 224 11.3 4.4 1.6 1.7 0.0 1.2

OB 0Low_Traffic Long_Headway 1460 21.6 0.0 0.0 0.0 0.0 0.0

OB 1AM Peak Long_Headway 1012 29.6 3.2 3.1 1.3 1.0 2.3

OB 2Midday Base Long_Headway 1518 21.9 3.9 3.1 1.6 0.0 2.3

OB 3Midday School Long_Headway 1265 29.2 5.0 0.0 2.0 0.0 0.0

OB 4PM Peak Long_Headway 1518 29.2 6.0 0.7 2.4 0.0 0.5

OB 5Shoulder/Eve. Long_Headway 1518 33.9 2.5 0.1 1.0 0.0 0.1

OB 7SUN_Morning Long_Headway 168 14.7 1.3 3.1 0.5 0.0 2.3

OB 8Afternoon_WKEND Long_Headway 336 20.8 0.3 0.0 0.1 0.0 0.0

OB 9Evening _WKEND Long_Headway 280 25.4 0.0 1.0 0.0 0.0 0.8

Total h/yr, INBOUND 736.3 357.5 6,800.3 1,432.8 6,109.2

Total h/yr, OUTBOUND 480.2 162.9 5,419.3 521.1 3,057.8

Total h/yr 1,216.5 520.4 12,219.6 1,954.0 9,167.0

unit cost ($/h) 108.4 108.4 12.0 18.0 9.0

Impact ($/yr) 131,870 56,412 146,635 35,171 82,503 509,097 78

Table 39 Route 99 variables and corresponding adjustments ( using scheduled running time)

Rt. Dir. Period RT Stdv(RT) Ave_Pax N_STOP Stop_time Net T.T. Delay /trip TT.Dealy min min PAX min min min min 99 IB 0Low_Traffic 18.17 1.85 11.11 16.82 5.10 13.06 0.00 0.00 99 IB 1AM Peak 38.50 3.61 29.64 30.01 10.13 28.37 15.31 6.12 99 IB 2Midday Base 30.00 0.00 23.62 27.04 8.80 21.20 8.14 3.25 99 IB 3Midday School 30.00 0.00 25.20 27.91 9.17 20.83 7.77 3.11 99 IB 4PM Peak 34.00 0.00 19.74 24.56 7.82 26.18 13.12 5.25 99 IB 5Shoulder/Eve. 25.70 2.93 21.28 25.60 8.22 17.47 4.41 1.76 99 IB 6Sat_Morning 22.00 0.00 15.05 20.79 6.45 15.55 2.49 1.00 99 IB 7SUN_Morning 22.00 0.00 20.25 24.91 7.95 14.05 0.98 0.39 99 IB 8Afternoon_WKEND 22.83 0.41 16.95 22.43 7.03 15.81 2.74 1.10 99 IB 9Evening _WKEND 22.75 0.50 11.34 17.08 5.19 17.56 4.50 1.80 99 OB 0Low_Traffic 25.64 0.71 21.63 26.72 8.52 17.12 0.00 0.00 99 OB 1AM Peak 27.00 0.00 29.55 31.30 10.42 16.58 -0.54 -0.22 99 OB 2Midday Base 27.00 0.00 21.91 26.92 8.60 18.40 1.28 0.51 99 OB 3Midday School 30.40 2.07 29.18 31.12 10.34 20.06 2.94 1.18 99 OB 4PM Peak 30.83 1.60 29.22 31.14 10.35 20.49 3.37 1.35 99 OB 5Shoulder/Eve. 26.67 2.07 33.93 33.17 11.31 15.35 -1.77 -0.71 99 OB 7SUN_Morning 22.00 0.00 14.67 20.95 6.44 15.56 -1.57 -0.63 99 OB 8Afternoon_WKEND 22.83 0.41 20.83 26.16 8.31 14.53 -2.59 -1.04 99 OB 9Evening _WKEND 23.20 0.40 25.43 29.14 9.48 13.72 -3.41 -1.36

Table 40 Traffic Congestion Impact on Route 99(using scheduled running time data)

Rt. Dir. Period Delay /trip Trip /yr Operation Pax_delay Total pax_yr Delay cost Cost (min) min/trip $/yr min/trip pax/yr $/yr 99 IB 0Low_Traffic 0.00 1601.00 - 0.00 17784.01 0.00 99 IB 1AM Peak 15.31 1012.00 27,995 6.12 29993.15 36739.62 99 IB 2Midday Base 8.14 1518.00 22,312 3.25 35850.10 23332.89 99 IB 3Midday School 7.77 1265.00 17,747 3.11 31878.00 19803.71 99 IB 4PM Peak 13.12 1518.00 35,982 5.25 29967.85 31454.67 99 IB 5Shoulder/Eve. 4.41 1265.00 10,080 1.76 26919.20 9498.46 99 IB 6Sat_Morning 2.49 112.00 504 1.00 1685.60 335.97 99 IB 7SUN_Morning 0.98 224.00 398 0.39 4536.00 357.10 99 IB 8Afternoon_WKEND 2.74 336.00 1,665 1.10 5695.20 1249.73 99 IB 9Evening _WKEND 4.50 224.00 1,821 1.80 2539.60 914.06 99 OB 0Low_Traffic 0.00 1460.00 - 0.00 31575.11 0.00 99 OB 1AM Peak 0.00 1012.00 - 0.00 29904.60 0.00 99 OB 2Midday Base 1.28 1518.00 3,516 0.51 33256.85 3411.34 99 OB 3Midday School 2.94 1265.00 6,720 1.18 36912.70 8682.31 99 OB 4PM Peak 3.37 1518.00 9,230 1.35 44350.90 11941.16 99 OB 5Shoulder/Eve. 0.00 1518.00 - 0.00 51510.80 0.00 99 OB 7SUN_Morning 0.00 168.00 - 0.00 2464.00 0.00 99 OB 8Afternoon_WKEND 0.00 336.00 - 0.00 7000.00 0.00 99 OB 9Evening _WKEND 0.00 280.00 - 0.00 7120.40 0.00 Total IB Inbound-Total 118,505 123,686 OB The route _Total 19,466 24,035 Total 18,150 137,971 430,944 147,721

 Route 9 Route 9 runs between Copley square and City Point Bus Terminal. Running from City Point Bus Terminal to Copley Sq is the inbound direction.

Table 41 Route 9 variables and corresponding adjustments (using AVL data)

Rt. Dir. Period hdwy netOns netOffs RT nStop S(nStop) AdjRT AdjTT SFsch(RT) AdjSFsch(RT) (H) S(H) AdjS(H) DepDev DepDev0.02 Wait AdjPot'l (min) (min) (min) (min) (min) (min) (min) (min) (min) (min)

9 IB 0 LH 16.21 17.11 21.71 10.09 5.68 17.86 7.14 3.20 2.88 0.00 0.00 n/a 4.80 -0.47 5.27 4.23 9 IB 1 SH 61.62 62.05 32.72 18.07 4.69 22.30 8.92 6.59 6.44 7.56 5.25 5.18 0.00 0.00 5.61 9.45 9 IB 2 LH 32.16 32.28 28.95 18.21 4.11 21.57 8.63 4.18 4.03 0.00 0.00 n/a 3.34 -0.95 4.29 5.91 9 IB 3 SH 30.61 30.28 31.35 15.52 3.97 24.78 9.91 6.35 6.25 12.63 5.49 5.46 0.00 0.00 7.51 9.17 9 IB 4 SH 26.54 26.20 29.67 13.67 4.69 23.95 9.58 6.85 6.74 9.81 5.39 5.34 0.00 0.00 6.38 9.88 9 IB 5 LH 22.05 22.13 27.71 13.87 5.84 22.39 8.96 4.98 4.76 0.00 0.00 n/a 3.17 -0.78 3.95 6.98 9 IB 6 LH 33.59 33.96 32.63 19.91 2.78 24.69 9.88 7.08 7.02 0.00 0.00 n/a 1.92 -1.70 3.62 10.30 9 IB 7 LH 30.44 30.89 26.93 18.15 3.72 19.73 7.89 3.36 3.20 0.00 0.00 n/a 4.27 -0.84 5.11 4.69 9 IB 8 LH 41.11 40.94 30.53 18.15 3.36 22.24 8.90 4.49 4.37 0.00 0.00 n/a 4.55 -1.24 5.79 6.41 9 IB 9 LH 18.77 18.77 23.71 12.37 4.11 19.09 7.64 2.36 2.10 0.00 0.00 n/a 5.44 -0.31 5.75 3.08 9 OB 0 LH 12.29 16.63 20.26 10.58 4.48 16.60 6.64 3.06 2.85 0.00 0.00 n/a 4.64 -1.26 5.91 4.18 9 OB 1 LH 18.16 22.20 21.81 9.53 3.12 17.79 7.12 3.18 3.06 0.00 0.00 n/a 2.43 -0.16 2.58 4.49 9 OB 2 LH 18.44 22.16 25.52 12.77 3.98 20.73 8.29 5.03 4.92 0.00 0.00 n/a 2.10 -2.74 4.83 7.22 9 OB 3 LH 26.82 35.19 29.75 16.99 3.59 22.95 9.18 5.35 5.26 0.00 0.00 n/a 3.68 -1.77 5.45 7.71 9 OB 4 SH 43.19 54.95 31.95 19.43 3.30 22.78 9.11 4.87 4.76 8.10 4.86 4.82 0.00 0.00 5.51 6.98 9 OB 5 LH 33.81 42.44 26.31 17.75 3.92 18.60 7.44 4.27 4.13 0.00 0.00 n/a 4.92 -1.41 6.33 6.05 9 OB 6 LH 13.42 16.64 23.55 12.50 3.71 19.36 7.74 3.59 3.47 0.00 0.00 n/a 1.72 -1.33 3.05 5.09 9 OB 7 LH 14.35 16.75 21.12 11.67 3.36 17.05 6.82 3.05 2.92 0.00 0.00 n/a 7.08 -1.39 8.47 4.29 9 OB 8 LH 23.73 33.93 25.45 15.97 3.39 19.15 7.66 3.34 3.20 0.00 0.00 n/a 6.37 -0.99 7.36 4.70

81 9 OB 9 LH 15.08 21.09 22.31 13.82 3.68 17.54 7.02 3.76 3.63 0.00 0.00 n/a 5.25 -1.30 6.54 5.33

Table 42 Traffic Congestion Impact on Route 9, MBTA, Boston

Dir period hdwy trips/yr Ave.(ons/trip) DelRT DelRecov DelTT DelWait DelPot'l Total class (min) (min) (min) (min) (min) IB 0Low_Traffic Long_Headway 4380 17.9 0.0 0.0 0.0 0.0 0.0 IB 1AM Peak Short_Headway 5566 63.3 4.4 5.6 1.8 1.7 4.2 IB 2Midday Base Long_Headway 3542 33.1 3.7 1.8 1.5 0.0 1.3 IB 3Midday School Short_Headway 3289 31.9 6.9 5.4 2.8 1.1 4.0 IB 4PM Peak Short_Headway 4048 27.6 6.1 6.1 2.4 1.4 4.6 IB 5Shoulder/Eve. Long_Headway 3542 22.7 4.5 2.9 1.8 0.0 2.2 IB 6Sat_Morning Long_Headway 616 34.5 6.8 6.7 2.7 0.0 5.0 IB 7SUN_Morning Long_Headway 448 31.2 1.9 0.5 0.7 0.0 0.4 IB 8Afternoon_WKEND Long_Headway 1456 41.8 4.4 2.3 1.8 0.5 1.8 IB 9Evening _WKEND Long_Headway 952 19.5 1.2 0.0 0.5 0.5 0.0 OB 0Low_Traffic Long_Headway 3874 17.6 0.0 0.0 0.0 0.0 0.0 OB 1AM Peak Long_Headway 4048 23.7 1.2 0.3 0.5 0.0 0.3 OB 2Midday Base Long_Headway 4301 22.8 4.1 3.3 1.6 0.0 2.5 OB 3Midday School Long_Headway 3036 36.0 6.3 3.8 2.5 0.0 2.9 OB 4PM Peak Short_Headway 4807 57.0 6.2 3.0 2.5 1.4 2.3 OB 5Shoulder/Eve. Long_Headway 3795 44.8 2.0 2.0 0.8 0.4 1.5 OB 6Sat_Morning Long_Headway 616 16.8 2.8 1.0 1.1 0.0 0.7 OB 7SUN_Morning Long_Headway 392 17.1 0.4 0.1 0.2 2.6 0.1 OB 8Afternoon_WKEND Long_Headway 1456 35.8 2.5 0.6 1.0 1.5 0.4 OB 9Evening _WKEND Long_Headway 952 22.5 0.9 1.2 0.4 0.6 0.9 Total h/yr, INBOUND 1,898.1 1,624.7 28,209.1 15,341.9 49,207.2 Total h/yr, OUTBOUND 1,425.3 864.2 22,857.1 9,257.0 25,106.3 Total h/yr 3,323.4 2,488.8 51,066.2 24,598.9 74,313.5 unit cost ($/h) 108.4 108.4 12.0 18.0 9.0

Impact ($/yr) 360,261 269,777 612,794 442,781 668,822 2,354,448 82

Table 43 Route 9 variables and corresponding adjustments ( using scheduled running time)

Rt. Dir. Period RT Stdv(RT) Ave_Pax N_STOP Stop_time Net T.T. Delay /trip TT.Dealy min min PAX min min min min

9 IB 0Low_Traffic 20.76 1.92 17.93 21.25 6.85 13.91 0.00 0.00 9 IB 1AM Peak 38.50 3.61 63.34 30.48 13.73 24.77 10.86 4.35 9 IB 2Midday Base 30.00 0.00 33.14 27.35 9.86 20.14 6.23 2.49 9 IB 3Midday School 30.00 0.00 31.86 27.03 9.65 20.35 6.44 2.57 9 IB 4PM Peak 34.00 0.00 27.63 25.79 8.92 25.08 11.17 4.47 9 IB 5Shoulder/Eve. 25.70 2.93 22.69 23.83 7.95 17.74 3.84 1.53 9 IB 6Sat_Morning 29.45 1.63 34.55 27.66 10.08 19.37 5.46 2.19 9 IB 7SUN_Morning 24.75 3.41 31.18 26.85 9.54 15.21 1.30 0.52 9 IB 8Afternoon_WKEND 29.04 0.64 41.79 28.91 11.13 17.91 4.01 1.60 9 IB 9Evening _WKEND 24.53 1.52 19.49 22.18 7.23 17.30 3.39 1.36 9 OB 0Low_Traffic 17.80 1.99 17.63 20.41 6.62 11.17 0.00 0.00 9 OB 1AM Peak 19.44 1.21 23.66 23.33 7.93 11.50 0.33 0.13 9 OB 2Midday Base 21.18 1.88 22.85 23.00 7.77 13.40 2.23 0.89 9 OB 3Midday School 26.00 2.09 36.05 26.59 9.98 16.02 4.84 1.94 9 OB 4PM Peak 30.32 1.80 57.03 28.43 12.59 17.72 6.55 2.62 9 OB 5Shoulder/Eve. 22.87 2.97 44.76 27.68 11.14 11.72 0.55 0.22 9 OB 6Sat_Morning 20.73 2.10 16.82 19.91 6.42 14.31 3.13 1.25 9 OB 7SUN_Morning 18.86 1.95 17.08 20.07 6.49 12.37 1.20 0.48 9 OB 8Afternoon_WKEND 23.19 0.64 35.81 26.55 9.95 13.24 2.07 0.83 9 OB 9Evening _WKEND 19.24 1.05 22.49 22.85 7.70 11.53 0.36 0.14

Table 44 Traffic Congestion Impact on Route 9(using scheduled running time data)

Rt. Dir. Period Delay /trip Trip /yr Operation Cost Pax_delay (min) Total pax_yr Delay cost min/trip $/yr min/trip pax/yr $/yr 9 IB 0Low_Traffic - 4,380 - - 78,530 - 9 IB 1AM Peak 10.9 5,566 109,244 4.3 352,537 306,386 9 IB 2Midday Base 6.2 3,542 39,873 2.5 117,373 58,507 9 IB 3Midday School 6.4 3,289 38,252 2.6 104,780 53,961 9 IB 4PM Peak 11.2 4,048 81,672 4.5 111,864 99,939 9 IB 5Shoulder/Eve. 3.8 3,542 24,553 1.5 80,353 24,664 9 IB 6Sat_Morning 5.5 616 6,082 2.2 21,280 9,303 9 IB 7SUN_Morning 1.3 448 1,052 0.5 13,969 1,452 9 IB 8Afternoon_WKEND 4.0 1,456 10,536 1.6 60,841 19,495 9 IB 9Evening _WKEND 3.4 952 5,827 1.4 18,554 5,029 9 OB 0Low_Traffic - 3,874 - - 68,316 - 9 OB 1AM Peak 0.3 4,048 2,406 0.1 95,773 2,520 9 OB 2Midday Base 2.2 4,301 17,320 0.9 98,266 17,522 9 OB 3Midday School 4.8 3,036 26,557 1.9 109,436 42,388 9 OB 4PM Peak 6.5 4,807 56,865 2.6 274,163 143,613 9 OB 5Shoulder/Eve. 0.5 3,795 3,761 0.2 169,870 7,454 9 OB 6Sat_Morning 3.1 616 3,485 1.3 10,360 2,595 9 OB 7SUN_Morning 1.2 392 847 0.5 6,695 641 9 OB 8Afternoon_WKEND 2.1 1,456 5,439 0.8 52,139 8,625 9 OB 9Evening _WKEND 0.4 952 618 0.1 21,413 616 Total IB Inbound-Total 317,091 578,738 OB The route _Total 117,298 225,975 Total 55,116 434,389 1,866,512 804,713 84

 Route 89 Route 89 (variant 89_2)runs between Davis Square and Sullivan Station. Route 89 (variant 89_) runs between Clarendon Hill and Sullivan Station.

**Route (89_)

Table 45 Route 89_ variables and corresponding adjustments (using AVL data)

Rt. Dir. Period hdwy netOns netOffs RT nStop S(nStop) AdjRT AdjTT SFsch(RT) AdjSFsch(RT) (H) S(H) AdjS(H) DepDev DepDev0.02 Wait AdjPot'l (min) (min) (min) (min) (min) (min) (min) (min) (min) (min)

89 IB 0 LH 12.53 12.86 12.79 9.94 2.80 9.38 3.75 2.90 2.80 n/a n/a n/a 0.90 -0.59 1.48 4.11 89 IB 1 LH 33.14 32.13 25.34 15.84 4.13 18.44 7.38 7.45 7.36 n/a n/a n/a 3.89 -1.18 5.07 10.80 89 IB 2 LH 28.33 24.88 20.14 15.09 3.02 14.00 5.60 2.94 2.79 n/a n/a n/a 1.85 -1.93 3.79 4.10 89 IB 3 LH 20.03 18.85 21.39 14.12 3.57 16.27 6.51 4.41 4.31 n/a n/a n/a 2.20 -1.68 3.88 6.32 89 IB 4 LH 11.62 15.18 22.72 12.33 3.81 18.74 7.50 5.32 5.23 n/a n/a n/a 4.81 -0.98 5.78 7.67 89 IB 5 LH 12.18 15.86 19.41 12.45 4.08 15.34 6.14 3.50 3.35 n/a n/a n/a 5.78 -0.34 6.12 4.91 89 IB 6 LH 25.65 18.80 21.40 13.25 3.11 16.08 6.43 4.79 4.71 n/a n/a n/a 2.36 -1.05 3.41 6.91 89 IB 7 LH 21.96 9.89 18.93 15.46 2.67 13.63 5.45 3.27 3.18 n/a n/a n/a 4.74 -0.20 4.94 4.66 89 IB 8 LH 19.31 14.68 21.41 15.84 3.47 16.06 6.42 4.18 4.08 n/a n/a n/a 7.18 0.08 7.10 5.98 89 IB 9 LH 10.65 8.21 17.64 12.48 3.00 13.91 5.56 3.42 3.33 n/a n/a n/a 4.01 -0.12 4.13 4.88 89 OB 0 LH 5.70 13.60 16.68 11.26 2.61 13.43 5.37 3.11 3.04 n/a n/a n/a 5.51 1.14 4.37 4.46 89 OB 1 LH 17.41 22.33 21.89 15.32 3.75 16.57 6.63 5.61 5.52 n/a n/a n/a 6.82 0.06 6.76 8.10 89 OB 2 LH 17.00 29.19 20.24 16.57 3.32 14.44 5.78 3.86 3.76 n/a n/a n/a 4.07 -0.54 4.61 5.51 89 OB 3 LH 15.21 29.32 21.87 15.66 3.67 16.41 6.56 2.22 2.01 n/a n/a n/a 4.17 -0.24 4.40 2.95 89 OB 4 LH 19.11 31.33 23.97 15.92 4.48 18.10 7.24 3.92 3.75 n/a n/a n/a 3.30 -2.93 6.24 5.50 89 OB 5 LH 10.59 21.86 18.98 14.51 4.09 14.36 5.74 2.76 2.57 n/a n/a n/a 5.55 -0.62 6.17 3.77 89 OB 6 LH 15.53 24.79 19.99 16.35 2.67 14.48 5.79 3.48 3.40 n/a n/a n/a 4.84 1.18 3.66 4.99 89 OB 7 LH 14.56 19.71 17.79 14.50 3.34 12.95 5.18 2.30 2.13 n/a n/a n/a 5.76 0.46 5.30 3.12 89 OB 8 LH 14.53 28.14 21.31 17.20 3.43 15.57 6.23 4.35 4.25 n/a n/a n/a 7.26 0.93 6.34 6.24 89 OB 9 LH 12.57 27.71 18.96 14.76 3.25 13.95 5.58 4.10 4.02 n/a n/a n/a 4.76 0.92 3.84 5.89

Table 46 Traffic Congestion Impact on Route 89_, MBTA, Boston

Dir period hdwy trips/yr Ave.(ons/trip ) DelRT DelRecov DelTT DelWait DelPot'l Total class (min) (min) (min) (min) (min)

IB 0Low_Traffic Long_Headway 1516 17.1 0.0 0.0 0.0 0.0 0.0

IB 1AM Peak Long_Headway 1771 40.1 9.1 7.3 3.6 3.6 5.4

IB 2Midday Base Long_Headway 1265 35.8 4.6 0.0 1.8 2.3 0.0

IB 3Midday School Long_Headway 1265 27.2 6.9 2.4 2.8 2.4 1.8

IB 4PM Peak Long_Headway 1771 18.9 9.4 3.9 3.7 4.3 2.9

IB 5Shoulder/Eve. Long_Headway 1265 17.8 6.0 0.8 2.4 4.6 0.6

IB 6Sat_Morning Long_Headway 168 33.3 6.7 3.0 2.7 1.9 2.3

IB 7SUN_Morning Long_Headway 224 27.6 4.2 0.6 1.7 3.5 0.4

IB 8Afternoon_WKEND Long_Headway 560 25.0 6.7 2.0 2.7 5.6 1.5

IB 9Evening _WKEND Long_Headway 280 13.5 4.5 0.8 1.8 2.6 0.6

OB 0Low_Traffic Long_Headway 1263 14.4 0.0 0.0 0.0 0.0 0.0

OB 1AM Peak Long_Headway 1518 23.2 3.1 4.0 1.3 2.4 3.0

OB 2Midday Base Long_Headway 1012 31.7 1.0 1.1 0.4 0.2 0.9

OB 3Midday School Long_Headway 1518 31.4 3.0 0.0 1.2 0.0 0.0

OB 4PM Peak Long_Headway 1771 33.8 4.7 1.1 1.9 1.9 0.8

OB 5Shoulder/Eve. Long_Headway 759 22.1 0.9 0.0 0.4 1.8 0.0

OB 6Sat_Morning Long_Headway 168 26.0 1.0 0.6 0.4 0.0 0.4

OB 7SUN_Morning Long_Headway 224 23.9 0.0 0.0 0.0 0.9 0.0

OB 8Afternoon_WKEND Long_Headway 616 31.4 2.1 1.9 0.9 2.0 1.4

OB 9Evening _WKEND Long_Headway 224 33.0 0.5 1.6 0.2 0.0 1.2

Total h/yr, INBOUND 1,030.1 430.1 11,420.1 13,521.4 9,992.2

Total h/yr, OUTBOUND 348.0 179.2 4,193.1 4,653.6 3,667.7

Total h/yr 1,378.1 609.3 15,613.1 18,175.0 13,659.8

unit cost ($/h) 108.4 108.4 12.0 18.0 9.0

Impact ($/yr) 149,382 66,047 187,358 327,149 122,939 916,885

Table 47 Route 89 variables and corresponding adjustments ( using scheduled running time)

Rt. Dir. Period RT Stdv(RT) Ave_Pax N_STOP Stop_time Net T.T. Delay /trip TT.Dealy min min PAX min min min min 89.0 IB 0Low_Traffic 10.28 0.69 17.12 19.76 6.42 3.86 0.00 0.00 89.0 IB 1AM Peak 18.57 1.51 40.15 26.41 10.37 8.20 4.35 1.74 89.0 IB 2Midday Base 18.40 0.55 35.80 25.83 9.78 8.62 4.76 1.90 89.0 IB 3Midday School 18.00 0.00 27.20 23.99 8.46 9.54 5.69 2.27 89.0 IB 4PM Peak 18.57 0.53 18.93 20.76 6.84 11.73 7.87 3.15 89.0 IB 5Shoulder/Eve. 16.40 2.51 17.79 20.14 6.58 9.82 5.96 2.39 89.0 IB 6Sat_Morning 17.33 1.15 33.33 25.41 9.43 7.91 4.05 1.62 89.0 IB 7SUN_Morning 16.00 1.15 27.60 24.10 8.52 7.48 3.62 1.45 89.0 IB 8Afternoon_WKEND 18.20 0.27 25.05 23.32 8.08 10.12 6.27 2.51 89.0 IB 9Evening _WKEND 15.20 0.00 13.50 17.32 5.47 9.73 5.87 2.35 89.0 OB 0Low_Traffic 14.08 2.79 14.38 18.74 5.89 8.19 0.00 0.00 89.0 OB 1AM Peak 16.67 2.07 23.22 24.07 8.06 8.60 0.41 0.17 89.0 OB 2Midday Base 18.00 0.00 31.70 26.99 9.63 8.37 0.18 0.07 89.0 OB 3Midday School 20.50 2.74 31.37 26.90 9.57 10.93 2.74 1.09 89.0 OB 4PM Peak 22.14 0.90 33.76 27.49 9.96 12.18 3.99 1.60 89.0 OB 5Shoulder/Eve. 17.33 3.51 22.12 23.56 7.83 9.50 1.31 0.53 89.0 OB 6Sat_Morning 16.67 1.53 26.00 25.21 8.62 8.05 -0.14 -0.06 89.0 OB 7SUN_Morning 16.00 2.31 23.88 24.36 8.20 7.80 -0.39 -0.16 89.0 OB 8Afternoon_WKEND 18.00 0.00 31.39 26.91 9.58 8.42 0.23 0.09 89.0 OB 9Evening _WKEND 15.25 0.87 32.95 27.30 9.83 5.42 -2.77 -1.11

Table 48 Traffic Congestion Impact on Route 89(using scheduled running time data)

Rt. Dir. Period Delay /trip Trip /yr Operation Pax_delay Total pax_yr Delay cost Cost (min) min/trip $/yr min/trip pax/yr $/yr 89 IB 0Low_Traffic 0.00 1516.00 - 0.00 25957.06 0.00 89 IB 1AM Peak 4.35 1771.00 13,904 1.74 71105.65 24718.78 89 IB 2Midday Base 4.76 1265.00 10,881 1.90 45287.00 17249.61 89 IB 3Midday School 5.69 1265.00 12,994 2.27 34408.00 15650.08 89 IB 4PM Peak 7.87 1771.00 25,193 3.15 33522.50 21116.04 89 IB 5Shoulder/Eve. 5.96 1265.00 13,632 2.39 22504.35 10738.47 89 IB 6Sat_Morning 4.05 168.00 1,229 1.62 5598.60 1813.83 89 IB 7SUN_Morning 3.62 224.00 1,464 1.45 6182.40 1789.16 89 IB 8Afternoon_WKEND 6.27 560.00 6,339 2.51 14025.67 7030.37 89 IB 9Evening _WKEND 5.87 280.00 2,970 2.35 3780.00 1775.54 89 OB 0Low_Traffic 0.00 1263.00 - 0.00 18155.63 0.00 89 OB 1AM Peak 0.41 1518.00 1,135 0.17 35242.90 1167.24 89 OB 2Midday Base 0.18 1012.00 331 0.07 32080.40 465.23 89 OB 3Midday School 2.74 1518.00 7,504 1.09 47614.60 10423.08 89 OB 4PM Peak 3.99 1771.00 12,778 1.60 59783.90 19100.94 89 OB 5Shoulder/Eve. 1.31 759.00 1,803 0.53 16786.55 1765.28 89 OB 6Sat_Morning 0.00 168.00 - 0.00 4368.00 0.00 89 OB 7SUN_Morning 0.00 224.00 - 0.00 5348.00 0.00 89 OB 8Afternoon_WKEND 0.23 616.00 259 0.09 19335.87 359.72 89 OB 9Evening _WKEND 0.00 224.00 - 0.00 7380.80 0.00 IB Inbound-Total 88,606 101,882 OB The route _Total 23,811 33,281 Total 19,158 112,417 508,468 135,163

**Route (89_2)

Table 49 Route 89_2 variables and corresponding adjustments (using AVL data)

Rt. Dir. Period hdwy netOns netOffs RT nStop S(nStop) AdjRT AdjTT SFsch(RT) AdjSFsch(RT) (H) S(H) AdjS(H) DepDev DepDev0.02 Wait AdjPot'l (min) (min) (min) (min) (min) (min) (min) (min) (min) (min)

89.2 IB 0 LH 17.38 18.43 14.01 12.48 3.87 9.47 3.79 1.80 1.49 n/a n/a n/a 1.53 -0.86 2.39 2.19 89.2 IB 1 LH 28.88 28.00 21.73 13.88 3.45 15.74 6.29 6.30 6.22 n/a n/a n/a 2.08 -2.02 4.10 9.12 89.2 IB 2 LH 26.69 23.44 18.10 14.92 2.94 12.16 4.87 2.72 2.58 n/a n/a n/a 2.44 -1.87 4.32 3.78 89.2 IB 3 LH 27.45 25.83 18.74 15.88 2.68 12.45 4.98 3.21 3.10 n/a n/a n/a 1.96 -1.60 3.55 4.55 89.2 IB 4 LH 23.56 30.78 19.56 16.07 3.06 13.35 5.34 4.29 4.20 n/a n/a n/a 4.57 -0.65 5.23 6.16 89.2 IB 5 LH 25.51 33.22 16.78 15.08 2.73 10.59 4.24 3.34 3.23 n/a n/a n/a 4.12 -1.40 5.53 4.74 89.2 IB 6 LH 17.90 13.12 17.35 13.00 3.07 12.82 5.13 3.21 3.09 n/a n/a n/a 2.32 -2.27 4.58 4.54 89.2 IB 8 LH 27.02 26.04 19.01 15.11 2.81 12.93 5.17 2.10 1.92 n/a n/a n/a 6.56 -1.17 7.73 2.82 89.2 IB 9 LH 17.78 24.31 16.51 14.50 2.81 11.29 4.52 3.81 3.73 n/a n/a n/a 2.18 -1.27 3.45 5.47 89.2 OB 0 LH 11.85 30.32 14.90 12.32 3.86 10.44 4.18 2.54 2.34 n/a n/a n/a 3.82 -2.45 6.26 3.44 89.2 OB 1 LH 31.49 41.31 20.67 16.44 3.28 13.47 5.39 6.42 6.35 n/a n/a n/a 5.45 -0.37 5.81 9.32 89.2 OB 2 LH 21.76 40.23 19.32 16.26 2.60 12.90 5.16 5.02 4.96 n/a n/a n/a 5.30 -1.01 6.31 7.28 89.2 OB 3 LH 18.15 37.19 19.34 15.94 2.65 13.36 5.34 4.83 4.77 n/a n/a n/a 3.38 -2.78 6.16 7.00 89.2 OB 4 LH 23.39 41.11 20.66 15.40 4.25 14.29 5.72 5.58 5.47 n/a n/a n/a 2.99 -5.29 8.28 8.02 89.2 OB 5 LH 22.03 45.26 18.00 15.72 3.29 11.54 4.61 4.50 4.41 n/a n/a n/a 6.33 -0.11 6.44 6.46 89.2 OB 6 LH 15.76 26.38 18.46 14.53 2.64 13.32 5.33 3.01 2.92 n/a n/a n/a 4.97 0.15 4.82 4.28 89.2 OB 8 LH 15.61 30.01 20.04 17.13 2.58 14.19 5.67 3.89 3.82 n/a n/a n/a 6.87 1.33 5.54 5.60 89.2 OB 9 LH 17.73 32.97 18.28 15.45 1.79 12.57 5.03 1.80 1.69 n/a n/a n/a 4.02 0.04 3.98 2.48 89

Table 50 Traffic Congestion Impact on Route 89_2, MBTA, Boston

Dir period hdwy trips/yr Ave.(ons/t rip) DelRT DelRecov DelTT DelWait DelPot'l Total class (min) (min) (min) (min) (min)

IB 0Low_Traffic Long_Headway 1348 26.1 0.0 0.0 0.0 0.0 0.0

IB 1AM Peak Long_Headway 1518 35.0 6.3 7.4 2.5 1.7 5.5

IB 2Midday Base Long_Headway 1012 33.7 2.7 1.6 1.1 1.9 1.2

IB 3Midday School Long_Headway 1012 37.3 3.0 2.5 1.2 1.2 1.8

IB 4PM Peak Long_Headway 2024 38.4 3.9 4.2 1.5 2.8 3.1

IB 5Shoulder/Eve. Long_Headway 1518 37.3 1.1 2.7 0.4 3.1 2.0

IB 6Sat_Morning Long_Headway 168 23.3 3.3 2.4 1.3 2.2 1.8

IB 8Afternoon_WKEND Long_Headway 336 39.9 3.5 0.6 1.4 5.3 0.5

IB 9Evening _WKEND Long_Headway 224 26.5 1.8 3.5 0.7 1.1 2.6

OB 0Low_Traffic Long_Headway 1545 30.5 0.0 0.0 0.0 0.0 0.0

OB 1AM Peak Long_Headway 1518 42.0 3.0 6.4 1.2 0.0 4.8

OB 2Midday Base Long_Headway 1012 40.6 2.5 4.2 1.0 0.0 3.1

OB 3Midday School Long_Headway 1012 37.4 2.9 3.9 1.2 0.0 2.9

OB 4PM Peak Long_Headway 2024 41.3 3.9 4.9 1.5 2.0 3.7

OB 5Shoulder/Eve. Long_Headway 1265 46.0 1.1 3.3 0.4 0.2 2.4

OB 6Sat_Morning Long_Headway 224 26.4 2.9 0.9 1.2 0.0 0.7

OB 8Afternoon_WKEND Long_Headway 280 30.3 3.7 2.4 1.5 0.0 1.8

OB 9Evening _WKEND Long_Headway 280 33.1 2.1 0.0 0.9 0.0 0.0

Total h/yr, INBOUND 448.7 488.5 6,475.2 11,417.8 13,207.4

Total h/yr, OUTBOUND 358.9 545.4 5,733.4 3,017.2 16,868.9

Total h/yr 807.5 1,033.9 12,208.6 14,435.0 30,076.3

unit cost ($/h) 108.4 108.4 12.0 18.0 9.0

Impact ($/yr) 87,538 112,073 146,503 259,829 270,687 914,141

Table 51 Route 89.2 variables and corresponding adjustments ( using scheduled running time)

Rt. Dir. Period RT Stdv(RT) Ave_Pax N_STOP Stop_time Net T.T. Delay /trip TT.Dealy min min PAX min min min min 89.2 IB 0Low_Traffic 13.19 0.59 26.13 21.28 7.71 5.48 0.00 0.00 89.2 IB 1AM Peak 16.83 0.41 34.98 22.70 8.96 7.87 2.39 0.96 89.2 IB 2Midday Base 15.75 1.50 33.73 22.56 8.80 6.95 1.47 0.59 89.2 IB 3Midday School 16.00 1.15 37.28 22.93 9.25 6.75 1.27 0.51 89.2 IB 4PM Peak 16.50 0.93 38.37 23.02 9.39 7.11 1.63 0.65 89.2 IB 5Shoulder/Eve. 14.17 0.75 37.27 22.92 9.25 4.92 -0.57 -0.23 89.2 IB 6Sat_Morning 15.00 0.00 23.25 20.54 7.24 7.76 2.28 0.91 89.2 IB 8Afternoon_WKEND 17.50 2.26 39.90 23.14 9.57 7.93 2.44 0.98 89.2 IB 9Evening _WKEND 14.00 0.00 26.47 21.36 7.76 6.24 0.76 0.30 89.2 OB 0Low_Traffic 16.97 2.42 30.49 25.46 9.14 7.82 0.00 0.00 89.2 OB 1AM Peak 26.67 0.82 41.99 27.40 10.79 15.88 8.05 3.22 89.2 OB 2Midday Base 24.00 1.15 40.59 27.23 10.61 13.39 5.57 2.23 89.2 OB 3Midday School 24.00 2.00 37.44 26.81 10.18 13.82 6.00 2.40 89.2 OB 4PM Peak 25.88 2.03 41.33 27.32 10.71 15.17 7.35 2.94 89.2 OB 5Shoulder/Eve. 22.20 2.68 46.01 27.79 11.30 10.90 3.08 1.23 89.2 OB 6Sat_Morning 17.00 0.00 26.38 24.30 8.44 8.56 0.73 0.29 89.2 OB 8Afternoon_WKEND 17.00 0.00 30.30 25.41 9.11 7.89 0.06 0.03 89.2 OB 9Evening _WKEND 19.00 2.74 33.05 26.03 9.54 9.46 1.63 0.65

Table 52 Traffic Congestion Impact on Route 89.2 (using scheduled running time data)

Rt. Dir. Period Delay /trip Trip /yr Operation Cost Pax_delay (min) Total pax_yr Delay cost min/trip $/yr min/trip pax/yr $/yr

89.2 IB 0Low_Traffic 0.00 1348.00 0 0.00 35229.46 0.00 89.2 IB 1AM Peak 2.39 1518.00 6556 0.96 53104.70 10155.45 89.2 IB 2Midday Base 1.47 1012.00 2690 0.59 34129.70 4017.29 89.2 IB 3Midday School 1.27 1012.00 2315 0.51 37722.30 3821.71 89.2 IB 4PM Peak 1.63 2024.00 5964 0.65 77658.35 10132.95 89.2 IB 5Shoulder/Eve. 0.00 1518.00 0 0.00 56570.80 0.00 89.2 IB 6Sat_Morning 2.28 168.00 692 0.91 3906.00 712.56 89.2 IB 8Afternoon_WKEND 2.44 336.00 1484 0.98 13406.40 2621.93 89.2 IB 9Evening _WKEND 0.76 224.00 306 0.30 5929.28 358.28 89.2 OB 0Low_Traffic 0.00 1545.00 0 0.00 47109.63 0.00 89.2 OB 1AM Peak 8.05 1518.00 22083 3.22 63743.35 41061.85 89.2 OB 2Midday Base 5.57 1012.00 10183 2.23 41074.55 18300.73 89.2 OB 3Midday School 6.00 1012.00 10964 2.40 37886.75 18175.21 89.2 OB 4PM Peak 7.35 2024.00 26864 2.94 83654.45 49165.55 89.2 OB 5Shoulder/Eve. 3.08 1265.00 7034 1.23 58202.65 14330.99 89.2 OB 6Sat_Morning 0.73 224.00 297 0.29 5908.00 346.59 89.2 OB 8Afternoon_WKEND 0.06 280.00 33 0.03 8484.00 43.63 89.2 OB 9Evening _WKEND 1.63 280.00 826 0.65 9254.00 1209.33

IB Inbound-Total 20,007 31,820

OB The route _Total 78,284 142,634

Total 18,320 98,291 672,974 174,454