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Using AVL Data to Measure the Impact of Traffic
Congestion on Bus Passenger and Operating Cost
A Thesis Presented
By
Ahmed Talat M. Halawani
to
The Department of Civil and Environmental Engineering
in partial fulfillment of the requirements
for the degree of
Master of Science
in
Civil Engineering
in the field of
Transportation Engineering
Northeastern University
Boston, Massachusetts
December, 2014
ii
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.
iv
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.
v
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
vi
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
ix
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
1
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.
2
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.
3
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.
5
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].
8
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.
9
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.
10
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
11
(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.
12
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.)
Time-point Order Order of time-point within HalfTrip (1
st time-point of HalfTrip would be
1, etc.)
Scheduled Scheduled time
Arrival Actual Arrival time to the Time-point
Departure Actual departure time
Earliness Deviation from departure time (s), in which positive is early and
negative is late.
Scheduled Headway
Scheduled leading headway (seconds)
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.
13
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
STOP_OFFSET It is a bus’s relative location in an ordered list of all stops for the 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
LONG_FIELD_3
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 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
14
“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.
15
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.
16
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 )
17
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
18
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).
19
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.
20
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) 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.
Using these results, dwell time will be estimated using equation (5.1)
Dwell Time = 1.63 + Tons * 4.35+ Toffs *1.87 (5.1)
Figure 1 the multiple regression model result
21
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.
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
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.
22
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).
𝐷𝑎𝑐𝑐 = 𝑡𝑎𝑐𝑐 − 𝑡𝑢𝑛𝑎𝑐𝑐 =V
𝑎𝑎𝑐𝑐 –
V
2 𝑎𝑎𝑐𝑐=
V
2 𝑎𝑎𝑐𝑐 (5.3)
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
23
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.
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.
Interval Day & Period
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
8Afternoon_WKEND 12:00 PM- 5:59 PM 1:30 PM – 3:59 PM 3Midday School
4:00 PM – 6:29 PM 4PM Peak
9Evening _WKEND 6:00 PM – 9:59 PM 6:30 AM – 9:59 PM 5Shoulder/Eve.
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.
24
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).
25
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)
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)
Negative value means bus consumed more time at the low traffic period than
the other periods, which indicates poor operating management for a route.
For our case study, following Furth (1998), a passenger trip on average covers 40 %
of the route length [16].
26
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
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.
By “Variability at the trip level”, we refer to the variability in the running time
between first and last stop
27
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.
VFsch
(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
V(𝑅𝑇)𝑃 =1
n − 1 ∑ ∑ (tipj − tp̿)
ij
2 (5.8)
28
The variation in the scheduled running time for a period is
𝑉(𝑆)𝑝 =1
n − 1 ∑ ∑ (Sip − S̅p)
ij
2 (5.9)
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
VFsch(p) =1
n−1 ∑ (ti − si)
i 2 (5.10)
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
𝑉𝐹𝑆𝑐ℎ(𝑡) = 𝐸(𝑡𝑖 − 𝑠𝑖)2 = 𝐸{[(𝑡𝑖 − 𝜇𝑇) − (𝑠𝑖 − 𝜇𝑆) + (𝜇𝑇 − 𝜇𝑆)]2}
= 𝐸(𝑡𝑖 − 𝜇𝑇)2 + 𝐸(𝑠𝑖 − 𝜇𝑆)2 − 2𝐸[(𝑡𝑖 − 𝜇𝑇)(𝑠𝑖 − 𝜇𝑆)] (5.11)
(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
𝑉𝐹𝑆𝑐ℎ(𝑅𝑇)𝑃 = 𝑉(𝑅𝑇)𝑃 + 𝑉(𝑆)𝑃 + (S̅P − t̿p)2
− 2𝑟𝑡𝑠 √𝑉(𝑡) √𝑉(𝑠) (5.12)
29
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,
Vserve(RT)p = (OnTime + OffTime)2 * (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.
30
Stopping Effect, Vstops(RT)p, is the portion of the running time variability due to
variability in the number of stops made per trips .
Vstops(RT)p = V(n_Stop)p * (LostTime)2 (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).
31
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)
† It is about 95 percentile (with assuming random distribution)
DelRecp = 1.64 {√VFSch(RT)p – √VFSch(RT)p − 𝑉𝑒,𝑝} (5.18)
32
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).
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
𝐸[𝑊] =ℎ
2(1 +
𝑉(ℎ)
ℎ2) (5.19)
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.
33
The part that stems from variability is excess waiting time, given by ℎ
2∗ (
𝑉(ℎ)
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
DelWaitp = 𝐻
2∗
AdjV(H)p − 1
𝐻2 (5.21)
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
35
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 𝐴𝑟𝑟95𝑝𝑐𝑡 − 𝐴𝑟𝑟̅̅ ̅̅ ̅.
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)
36
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.
37
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
Incr
emen
tal
cost
du
e to
incr
ease
d
(Ave
rage
del
ay)
DelRTp*tripsp* $108.39
0.4 DelRTp * (Ons/ tripp) * tripp *$12
Incr
emen
tal
cost
du
e to
incr
ease
d
(Vari
abil
ity)
DelRecp*tripsp* $108.39
(DelWaitp * 1.5 + 0.75 DelRecp*0.75)*
(Ons/ tripp) * tripp *$12
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.
38
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 ∗ 𝑂𝑛𝑠/𝑡𝑟𝑖𝑝
𝑁 (5.26)
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:
𝑃(𝑠𝑡𝑜𝑝) = 1 − 𝑒−𝑚 (5.27)
Thus, the expected number of stops made in a trip is
𝑆𝑡𝑜𝑝𝑠 𝑀𝑎𝑑𝑒 = 𝑁 ∗ (1 − 𝑒−𝑚) (5.28)
39
Figure 2 Demonstration to Poisson passenger arrival with 30 stops for a route
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.
0
5
10
15
20
25
30
35
0 10 20 30 40 50 60St
op
s M
ade
Ons/trip
40
Figure 3 route 1 [18]
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).
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).
41
Table 7 trips throughout a year for Route 1 , MBTA
Dir
ecti
on
Per
iod
Da
y
Sch
edu
led
Tri
p/d
ay
Sch
edu
led
Tri
p/y
ear
Sch
edu
led
Tri
ps/
yea
r
Sh
are
of
yea
r’s
trip
s/p
erio
d
Sh
are
of
yea
r’s
trip
s
IB 0Low_Traffic
SAT 23 1288
18%
SUN 17 952
13%
WKDY 20 5060
69%
Total - - 7300 100% 20%
IB 8Afternoon_WKEND
SAT 37 2072
65%
SUN 20 1120
35%
Total - - 3192 100% 9%
IB 9Evening _WKEND
SAT 18 1008
58%
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%
OB 0Low_Traffic
SAT 26 1456
18%
SUN 16 896
11%
WKDY 23 5819
71%
Total - - 8171 100% 22%
OB 8Afternoon_WKEND
SAT 37 2072
65%
SUN 20 1120
35%
Total - - 3192 100% 8%
OB 9Evening _WKEND
SAT 18 1008
58%
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
43
Table 9 Route 1 variables and corresponding adjustments (using AVL data)
3 Negative sign indicate early departure
Rt.
Dir
.
Per
iod
hd
wy
net
On
s
net
Off
s
RT
nS
top
S(n
Sto
p)
Ad
jRT
Ad
jTT
SF
sch
(RT
)
Ad
jSF
sch
(RT
)
(H)
S(H
)
Ad
jS(H
)
Dep
Dev
Dep
Dev
0.0
23
Wait
Ad
jPot'
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
44
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)
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.
In the case, when the traffic congestion impact values were negative, values were set
to be equal zero.
45
Table 10 Traffic Congestion Impact on Route 1, MBTA, Boston. D
ir
per
iod
hd
wy
trip
s/yr
Ave
(on
s/tr
ip)
Del
RT
(min
)
Del
Rec
ov
(min
)
Del
TT
(min
)
Del
Wait
(min
)
Del
Pot'
l
(min
)
Tota
l
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 ($/h)
108.4 108.4 12.0 18.0 9.0
Impact ($/yr)
800,977 413,699 2,297,076 2,045,403 1,692,202 7,249,376
46
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
.
Per
iod
RT
Std
v(R
T)
Av
e_P
ax
N_
ST
OP
Sto
p_
tim
e
Net
T.T
.
Del
ay
/tr
ip
TT
.Dea
ly
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
47
Table 12 Traffic Congestion Impact on Route 1(using scheduled running time data)
Dir
.
Per
iod
Del
ay
/tr
ip
Tri
p /
yr
Op
era
tio
n
Co
st
Pa
x_
del
ay
(min
)
Est
i. P
ax
Del
ay
co
st
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.
48
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
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).
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
99 Regular route Boston Regional Medical Center -Wellington
Station
49
Table 14 Summary Annual Impact of Traffic Congestion on the chosen routes
Impact due to lower average speed
Variability impact Total Impact due to lower 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.
50
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)
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.
Figure 5 Travel congestion impact per Veh-hr
0
20
40
60
80
100
120
140
160
1 23 28 39_3 66-6 77 89 99-7 9
Traffic congestion cost /Veh-hr
for operating cost for passengers
1.49 1.40 1.53 1.50
1.73
1.40
1.11
0.61
0.92
-
0.30
0.60
0.90
1.20
1.50
1.80
2.10
1.00 23.00 28.00 39_3 66-6 77.00 89.00 99-7 9.00
$/P
ax
Route
Average passenger impact of traffic congestion
Figure 4 the estimated impact cost of traffic congestion on passenger
51
Table 15 Traffic congestion impact per Veh-hr
Traffic congestion cost /Veh-hr
ROUTE Annual
revenue hours
Pax/yr for operating
cost
for passengers $/pax-
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).
Figure 6 Comparing Measuring travel congestion impact on buses due to lower average speed
using AVL data & Scheduled running time data
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
0 1,000,000 2,000,000 3,000,000 4,000,000
Usi
ng
Sch
edu
le r
un
nin
g ti
me
dat
a
Using AVL Data
impact on Pax Travel cost impact on Operating cost 45 degree line
52
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.
53
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2. Moynihan, D.P. “The Politics and Economics of Regional Growth.” The public
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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.
54
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
55
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
56
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]
57
Table 17 Route 23 variables and corresponding adjustments (using AVL data)
Rt.
Dir
.
Pe
rio
d
hd
wy
ne
tOn
s
ne
tOff
s
RT
nSt
op
S(n
Sto
p)
Ad
jRT
Ad
jTT
SFsc
h(R
T)
Ad
jSFs
ch(R
T)
(H)
S(H
)
Ad
jS(H
)
De
pD
ev
De
pD
ev0
.02
Wai
t
Ad
jPo
t'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
58
Table 18 Traffic Congestion Impact on Route 23 (Using AVL Data)
Dir
pe
rio
d
hd
wy
trip
s/yr
Ave
.(o
ns/
trip
) De
lRT
De
lRe
cov
De
lTT
De
lWai
t
De
lPo
t'l
Tota
l
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
59
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
60
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
Cost
Pax_delay (min) Total pax_yr Delay 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
61
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
.
Pe
rio
d
hd
wy
ne
tOn
s
ne
tOff
s
RT
nSt
op
S(n
Sto
p)
Ad
jRT
Ad
jTT
SFsc
h(R
T)
Ad
jSFs
ch(R
T)
(H)
S(H
)
Ad
jS(H
)
De
pD
ev
De
pD
ev0
.02
Wai
t
Ad
jPo
t'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
62
Table 22 Traffic Congestion Impact on Route 66 (Using AVL Data).
Dir
per
iod
hd
wy
trip
s/y
r
Av
e.(o
ns/
trip
)
Del
RT
Del
Reco
v
Del
TT
Del
Wa
it
Del
Po
t'l
To
tal
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
63
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
64
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
65
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
.
Per
iod
hd
wy
net
On
s
net
Off
s
RT
nS
top
S(n
Sto
p)
Ad
jRT
Ad
jTT
SF
sch
(RT
)
Ad
jSF
sch
(RT
)
(H)
S(H
)
Ad
jS(H
)
Dep
Dev
Dep
Dev
0.0
2
Wa
it
Ad
jPo
t'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
66
Table 26 Traffic Congestion Impact on Route 77 (Using AVL Data). D
ir
pe
rio
d
hd
wy
trip
s/yr
Ave
.(o
ns/
trip
)
De
lRT
De
lRe
cov
De
lTT
De
lWai
t
De
lPo
t'l
Tota
l
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
Impact ($/yr)
659,692 224,746 1,057,885 1,625,373 490,594 4,340,972
67
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
68
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
69
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
.
Pe
rio
d
hd
wy
ne
tOn
s
ne
tOff
s
RT
nSt
op
S(n
Sto
p)
Ad
jRT
Ad
jTT
SFsc
h(R
T)
Ad
jSFs
ch(R
T)
(H)
S(H
)
Ad
jS(H
)
De
pD
ev
De
pD
ev0
.02
Wai
t
Ad
jPo
t'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
70
Table 30 Traffic Congestion Impact on Route 28 (Using AVL Data).
Dir
pe
rio
d
hd
wy
trip
s/yr
Ave
.(o
ns/
trip
)
De
lRT
De
lRe
cov
De
lTT
De
lWai
t
De
lPo
t'l
Tota
l
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
71
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
72
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
73
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
.
Pe
rio
d
hd
wy
ne
tOn
s
ne
tOff
s
RT
nSt
op
S(n
Sto
p)
Ad
jRT
Ad
jTT
SFsc
h(R
T)
Ad
jSFs
ch(R
T)
(H)
S(H
)
Ad
jS(H
)
De
pD
ev
De
pD
ev0
.02
Wai
t
Ad
jPo
t'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
74
Table 34 Traffic Congestion Impact on Route 39 (Using AVL Data).
Dir
pe
rio
d
hd
wy
trip
s/yr
Ave
.(o
ns/
trip
)
De
lRT
De
lRe
cov
De
lTT
De
lWai
t
De
lPo
t'l
Tota
l
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
unit cost ($/h)
108.4 108.4 12.0 18.0 9.0
Impact ($/yr)
971,377 357,917 2,653,572 1,936,626 1,446,674 7,782,408
75
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
76
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
77
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
.
Pe
rio
d
hd
wy
ne
tOn
s
ne
tOff
s
RT
nSt
op
S(n
Sto
p)
Ad
jRT
Ad
jTT
SFsc
h(R
T)
Ad
jSFs
ch(R
T)
(H)
S(H
)
Ad
jS(H
)
De
pD
ev
De
pD
ev0
.02
Wai
t
Ad
jPo
t'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
78
Table 38 Traffic Congestion Impact on Route 99 (Using AVL Data). D
ir
pe
rio
d
hd
wy
trip
s/yr
Ave
.(o
ns/
trip
)
De
lRT
De
lRe
cov
De
lTT
De
lWai
t
De
lPo
t'l
Tota
l
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
79
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
80
Table 40 Traffic Congestion Impact on Route 99(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
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
81
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
.
Pe
rio
d
hd
wy
ne
tOn
s
ne
tOff
s
RT
nSt
op
S(n
Sto
p)
Ad
jRT
Ad
jTT
SFsc
h(R
T)
Ad
jSFs
ch(R
T)
(H)
S(H
)
Ad
jS(H
)
De
pD
ev
De
pD
ev0
.02
Wai
t
Ad
jPo
t'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
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
82
Table 42 Traffic Congestion Impact on Route 9, MBTA, Boston
Dir
pe
rio
d
hd
wy
trip
s/yr
Ave
.(o
ns/
trip
)
De
lRT
De
lRe
cov
De
lTT
De
lWai
t
De
lPo
t'l
Tota
l
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
83
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
84
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
85
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
.
Pe
rio
d
hd
wy
ne
tOn
s
ne
tOff
s
RT
nSt
op
S(n
Sto
p)
Ad
jRT
Ad
jTT
SFsc
h(R
T)
Ad
jSFs
ch(R
T)
(H)
S(H
)
Ad
jS(H
)
De
pD
ev
De
pD
ev0
.02
Wai
t
Ad
jPo
t'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
86
Table 46 Traffic Congestion Impact on Route 89_, MBTA, Boston
Dir
pe
rio
d
hd
wy
trip
s/yr
Ave
.(o
ns/
trip
) De
lRT
De
lRe
cov
De
lTT
De
lWai
t
De
lPo
t'l
Tota
l
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
87
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
88
Table 48 Traffic Congestion Impact on Route 89(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 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
89
**Route (89_2)
Table 49 Route 89_2 variables and corresponding adjustments (using AVL data)
Rt.
Dir
.
Pe
rio
d
hd
wy
ne
tOn
s
ne
tOff
s
RT
nSt
op
S(n
Sto
p)
Ad
jRT
Ad
jTT
SFsc
h(R
T)
Ad
jSFs
ch(R
T)
(H)
S(H
)
Ad
jS(H
)
De
pD
ev
De
pD
ev0
.02
Wai
t
Ad
jPo
t'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
90
Table 50 Traffic Congestion Impact on Route 89_2, MBTA, Boston
Dir
pe
rio
d
hd
wy
trip
s/yr
Ave
.(o
ns/
t
rip
)
De
lRT
De
lRe
cov
De
lTT
De
lWai
t
De
lPo
t'l
Tota
l
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
91
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
92
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
