Identifying Opportunities to Alleviate Bus Delay

 

 

Project Manager

Jonathan Belcher

 

Project Principal

Annette Demchur

 

Data Analyst

Nicholas Hart

 

Graphics

Nicholas Hart

 

Cover Design

Kim DeLauri

 

 

The preparation of this document was supported

by the Federal Transit Administration through

MPO §5303 Contracts #84080 and #91027.

                                       

Central Transportation Planning Staff

Directed by the Boston Region Metropolitan

Planning Organization. The MPO is composed of

state and regional agencies and authorities, and

local governments.

 

 

December 2016

 

To request additional copies of this document or copies in an accessible format, contact:

 

Central Transportation Planning Staff

State Transportation Building

Ten Park Plaza, Suite 2150

Boston, Massachusetts 02116

 

(857) 702-3700

(617) 570-9192 (fax)

TTY - (617) 570-9193

 

ctps@ctps.org

ctps.org

 

Report Map Page
 

Abstract

 

Reliable bus transportation offers mobility, access, and livability benefits to residents throughout the region. However, delays in bus service diminish these benefits. This study seeks to identify opportunities to alleviate existing delays in bus service. To this end, MPO staff 1) examined factors that lead to increased dwell time at bus stops. This included estimating the amount of time contributed by passengers’ use of each fare payment type, as well as the time added by boardings of baby carriages, wheeled mobility devices, and portable shopping carts—along with any delay caused by the on-board presence of such items. In addition, staff 2) assessed delay caused by operational and scheduling practices—such as, the scheduling of interlines, scheduling of deadhead movements, on-time performance of garage pull-outs, scheduling of supplemental bus trips, scheduling of bus operator swing-ons, and non-utilization of early pull-up opportunity at trip origins.


 

 

Table of Contents                                                                               

Abstract

Chapter 1—Executive Summary

Chapter 2—Investigating Factors that Lead to Increased Dwell Time

2.1........ Data Collection—MBTA Routes 116 and 117

2.2........ Estimating a Dwell Time Regression Model

Chapter 3—Assessing Operational and Scheduling Improvements to Alleviate
        Bus Delay

3.1........ Interlines

3.2........ Deadhead Movements

3.3........ Garage Pull-Outs

3.4........ Supplemental MBTA Bus Trips

3.5........ Bus Operator Swing-Ons

3.6........ Early Pull-Up Opportunity

 

 

TABLES                                                                                                  

Table 1 Summary of Bus Routes 116 and 117 Boarding Observations

Table 2 Summary of Bus Routes 116 and 117 Load-at-Stop Observations

Table 3 Variables Tested for the Estimation of a Dwell Time Regression Model

Table 4 Coefficients of the Dwell Time Regression Model

Table 5 Estimation of Total Dwell Time and Run Time Contribution

Table 6 Run-Time Variability by Route—Charlestown District, Fall 2015

Table 7 Run-Time Variability by Trip End Point—Charlestown District, Fall 2015

Table 8 Run-Time Variability by Trip End Time—Charlestown District, Fall 2015

Table 9 Trips Leading into Non-Deadhead Interlines that Do Not Pass the Run-Time
 Variability Threshold—Charlestown District, Fall 2015

Table 10 Trips Leading into Deadhead Interlines that Do Not Pass the Run-Time
   Variability Threshold—Charlestown District, Fall 2015

Table 11 Deadhead Movements with Median Run Times Greater than  Scheduled
   Run Times, Spring 2016

Table 12 Summary of Departure Lateness from Garage, Spring 2016

Table 13 Summary of First Trip Arrival Lateness of Pull-Outs that Depart more than
   Three Minutes Late from the Garage, Spring 2016

Table 14 Summary of Early Pull-Up Opportunity by Route

Table 15 Summary of Early Pull-Up Opportunity—All Observations

 

 

FIGURES                                                                                                 

Figure 1 Observed and Fitted Dwell Time

Figure 2 Estimated Delay from Pay Cash and Add Value Transactions by Trip

Figure 3 Estimated Delay from Baby Carriage Boardings and Alightings by Trip

Figure 4 Estimated Delay from Pay Cash and Add Value Transactions and  Baby
  Carriage Boardings and Alightings by Trip

Figure 5 Departure Lateness from Garage—Systemwide Observations,
  Spring 2016

Figure 6 Procedure for Assessing Swing-On Delay

Figure 7 Procedure for Assessing Early Pull-Up Opportunity

 

 

APPENDICES                                                                                           

 

 

 

 


 

Chapter 1—Executive Summary

 

Reliable bus transportation offers mobility, access, and livability benefits to residents throughout the region. However, delays in bus service diminish these benefits. Upgrades to infrastructure, such as more efficient fare-collection equipment, as well as operational improvements, such as increasing the effectiveness of scheduling practices, could decrease bus delay, which in turn would improve on-time performance, reduce travel times for transit passengers, and make transit a more attractive travel mode. Using a variety of data sources, the Central Transportation Planning Staff (CTPS) to the Boston Region Metropolitan Planning Organization (MPO) analyzed these factors, and developed solutions to address the specific causes of delay.

 

Chapter 2 of this report investigates factors that lead to increased dwell time at bus stops by estimating the amount of time contributed by using each fare payment type, and by the boardings and on-board presence of baby carriages, wheeled mobility devices, and portable shopping carts, using observations from a sample of Route 116 and 117 trips. The results from the regression analysis estimate that the baseline boarding time (for a passenger paying with a CharlieCard or CharlieTicket) is about 3.3 seconds. An additional 9.4 seconds is added (12.7 seconds of total boarding time) if the passenger pays with cash, and an additional 6.3 seconds is added (9.6 seconds of total boarding time) if the passenger adds value to their CharlieCard. These results suggest that overall dwell time for the observed trips would be reduced by an estimated 11.3 percent if cash transactions were not permitted and by an approximate 2.6 percent if baby carriages had not boarded. Dwell time accounted for 14.4 percent of total run time. Therefore, staff estimate that total run time for the observed trips would be reduced by only 1.6 percent if cash transactions were not permitted and by 0.4 percent if baby carriages had not boarded.

 

Chapter 3 of this report assesses delay caused by a series of operational and scheduling practices; the scheduling of interlines, scheduling of deadhead movements, on-time performance of garage pull-outs, scheduling of supplemental trips, scheduling of bus operator swing-ons, and non-utilization of early pull-up opportunity at trip origins.

 

In analyzing interlines and deadhead movements, CTPS found that 193 of the 364 scheduled weekday interlines from the Charlestown bus district might be considered for de-interlining based on their run time variability observed in fall 2015. In addition, 41 of the 437 scheduled weekday deadhead movements systemwide had median run times greater than their scheduled run times in spring 2016.

 

In analyzing on-time performance of garage pull-outs, CTPS found that in spring 2016, 31 percent of pull-out trips departed more than three minutes late from the garage; and of these trips, 70 percent of them arrived at the start point of their first vehicle-revenue trip after the scheduled departure time, with an average arrival lateness of 3.9 minutes per trip.

 

For the analysis of supplemental trips, CTPS provided summaries of origin departure lateness, end-point arrival lateness, and run times, and identified how each trip might be scheduled more effectively. It is important that these trips run as scheduled, as many of them start a string of trips that provide service throughout the PM peak period.

 

In analyzing bus operator swing-ons, CTPS estimated that swing-ons added an extra 59 seconds to each trip where a swing-on occurred. Staff also found that 24 percent of scheduled swing-ons occurred on trips where the bus arrived at the swing-on location already behind schedule.

 

In analyzing early pull-up opportunity at trip origins, CTPS found that earlier pull-ups at origins would not contribute to a significant reduction in departure lateness on the routes that staff studied. Of the 25,169 minutes of observed departure lateness, staff estimated that only 1,441 minutes (5.7 percent) would have been saved if buses, that had the opportunity to do so, had pulled up earlier.

 


 

Chapter 2—Investigating Factors that Lead to Increased Dwell Time

 

2.1      Data Collection—MBTA Routes 116 and 117

MPO field staff (CTPS) conducted manual observations of baby carriage boardings, wheeled mobility device boardings, and portable shopping cart boardings on sample trips of MBTA bus Routes 116 and 117 from January 12, 2016 to January 21, 2016. These observations were paired with dwell times, boarding and alighting counts from on-board Automatic Passenger Counters (APCs), and counts by fare transaction type from the MBTA Automatic Fare Collection (AFC) database. A total of 97 trips of usable data was collected from 23 Route 116 inbound trips, 24 Route 116 outbound trips, 23 Route 117 inbound trips, and 27 Route 117 outbound trips. Appendix A provides a summary of boarding observations for each trip; Table 1 displays a summary of all boarding observations; and Table 2 displays a summary of all load-at-stop observations.

 

Table 1
Summary of Bus Routes 116 and 117 Boarding Observations

(Sampled Trips from January 12, 2016 to January 21, 2016)

Boarding Type

Total Observations

Percentage of AFC Transactions

CharlieCard

2,253

72.6%

CharlieTicket

673

21.7%

Add-Value

265

8.5%

Pay Cash

178

5.7%

Baby Carriage

42

1.4%

Shopping Cart

7

0.2%

Wheeled Mobility Device

3

0.1%

AFC = Automatic fare collection.

Note: Counts do not include activity at first and last stop of a trip. Add-value transactions are also counted as CharlieCard transactions.

 

Table 2
Summary of Bus Routes 116 and 117 Load-at-Stop Observations

(Sampled Trips from January 12, 2016 to January 21, 2016)

Load Type

Total Observations

Percentage of Stops

One Baby Carriage on-board

344

11.5%

Two Baby Carriages on-board

89

3.0%

Three Baby Carriages on-board

10

0.3%

One Shopping Cart on-board

95

3.2%

One Wheeled Mobility Device on-board

32

1.1%

Note: Counts do not include activity at first and last stop of a trip.


 

2.2      Estimating a Dwell Time Regression Model

Using the APC data, AFC data, and data collected from manual observations on sample trips of MBTA Routes 116 and 117, CTPS conducted a regression analysis to estimate the amount of dwell time contributed by a set of variables. Table 3 displays a list of variables that were tested.

 

Table 3
Variables Tested for the Estimation of a Dwell Time Regression Model

Variable

Definition

Range of Observed Values

APC Dwell

Dwell time in seconds, calculated from APC data as the amount of time between the doors opening and doors closing

3 to 152

AFC Ons

Number of boardings, calculated as the number of AFC transactions

0 to 18

AFC Ticket

Number of CharlieTicket transactions

0 to 5

AFC Cash

Number of cash only transactions

0 to 5

AFC Add-Value

Number of add value to CharlieCard transactions

0 to 4

AFC Noninteraction

Number of APC boardings minus the number of AFC transactions, only when more APC boardings than AFC transactions are recorded

0 to 7

Load Over 30

Number of additional passengers greater than 30 on-board the bus

0 to 36

APC Offs Front

Number of front door alightings

0 to 20

Extra Rear

Extra rear door activity calculated as the number of rear door offs minus the total number of AFC transactions, AFC noninteraction, and front door offs

0 to 10

Carriage Ons

Number of baby carriage boardings

0 to 2

Carriage Offs

Number of baby carriage alightings

0 to 2

Cart Ons

Number of portable shopping cart boardings

0 to 1

Cart Offs

Number of portable shopping cart alightings

0 to 1

Carriage On-board

Number of baby carriages on-board the bus when it leaves the stop

0 to 3

Cart On-board

Number of personal shopping carts on-board the bus when it leaves the stop

0 to 1

WMD On-board

Number of wheeled mobility devices on-board the bus when it leaves the stop

0 to 1

AFC = Automatic fare collection. APC = Automatic passenger counters. WMD = Wheeled mobility devices.

 

CTPS estimated an ordinary least squares linear regression model1 . The first and last stop of each trip and stops that included a wheeled mobility device boarding or alighting were removed from the regression analysis, as bus doors are often left open for a longer period of time than is required to board and alight passengers at the first and last stop, and the procedure to board or alight a wheeled mobility device may alter the typical manner in which additional customers at the stop board and alight. The functional form of the model is shown below, and its coefficients are shown in Table 4.

 

 

1 Ordinary least squares is a method for estimating the unknown parameters in a linear regression model.

 

Table 4
Coefficients of the Dwell Time Regression Model

Variable

Estimate

Standard Error

p-value

Intercept

3.70

0.35

< 2.0E-16

AFC Ons

3.32

0.14

< 2.0E-16

AFC Cash

9.38

0.64

< 2.0E-16

AFC Add-Value

6.29

0.59

< 2.0E-16

AFC Noninteraction

3.23

0.31

< 2.0E-16

Load Over 30

0.30

0.06

1.7E-07

APC Offs Front

2.82

0.17

< 2.0E-16

Extra Rear

1.34

0.25

1.2E-07

Carriage Ons

9.63

1.45

4.1E-11

Carriage Offs

7.18

1.32

6.1E-08

AFC = Automatic fare collection. APC = Automatic passenger counters.

Note: Multiple R-squared = 0.641, degrees of freedom = 1,785.

 

The results from the regression analysis estimate that the baseline boarding time (for a passenger paying with a CharlieCard or CharlieTicket) is about 3.3 seconds. An additional 9.4 seconds is added (12.7 seconds of total boarding time) if the passenger pays with cash, and an additional 6.3 seconds is added (9.6 seconds of total boarding time) if the passenger adds value to their CharlieCard. When the variable representing the number of CharlieTicket transactions was included in the model, it was well outside of a five percent confidence level, suggesting that CharlieCard and CharlieTicket transactions take a similar amount of time to complete. Furthermore, the model estimates that dwell times increase as passenger loads increase, once loads are greater than 30 passengers2 , at a rate of about 0.3 seconds per additional passenger, and that baby carriages contribute 9.6 seconds when boarding and 7.2 seconds when alighting.

 

2 CTPS chose a load of 30 passengers to represent the point at which standees might typically start to form on-board a bus, thus impeding boarding and alighting passengers. However, a regression tree analysis, and the testing of multiple load variables at different break points did not find any one break point to be conclusively significant over another. CTPS hypothesize that this may be a result of low sampling at certain ranges of passenger load.

 

 

Figure 1 displays the relationship between observed dwell time and fitted dwell time. From Figure 1 it appears that the model tends to underestimate when observed dwell times are long. This is because observations with long dwell times are more likely to include additional delay factors that were not specified in the model.

Figure 1
Observed and Fitted Dwell Time

(Sampled Trips from Bus Routes 116 and 117,

January 12, 2016 to January 21, 2016)

 

Figure 1 is a non-accessible scatter chart. This figure displays the relationship between observed dwell time and fitted dwell time of sampled trips from bus Routes 116 and 117 between January 12, 2016 and January 21, 2016. From Figure 1 it appears that the model tends to underestimate when observed dwell times are long. This is because observations with long dwell times are more likely to include additional delay factors that were not specified in the model.

 

 

The coefficients estimated by the dwell time regression model were applied to the dataset to estimate the total contribution to dwell time and run time3 for each variable, shown in Table 54 . The results estimate that overall dwell time for the observed trips would be reduced by 11.4 percent if cash transactions (pay cash and add-value) were not permitted and by 1.5 percent if baby carriages had not boarded. Dwell time accounted for 15.6 percent of total run time. Therefore, it is estimated that total run time for the observed trips would be reduced by 1.8 percent if cash transactions were not permitted and by 0.2 percent if baby carriages had not boarded.

 

Table 5
Estimation of Total Dwell Time and Run Time Contribution

(Sampled Trips from Bus Routes 116 and 117,
January 12, 2016 to January 21, 2016)

 

Total Dwell Contribution (seconds)

Percentage of Total Dwell Time

Percentage of Total Run Time

Intercept

6,763

21.3%

3.3%

AFC Ons

12,685

40.0%

6.2%

AFC Cash

1,762

5.6%

0.9%

AFC Add-Value

1,834

5.8%

0.9%

AFC Noninteraction

1,798

5.7%

0.9%

Load Over 30

842

2.7%

0.4%

APC Offs Front

4,483

14.1%

2.2%

Extra Rear

709

2.2%

0.3%

Carriage Ons

477

1.5%

0.2%

Carriage Offs

352

1.1%

0.2%

AFC = Automatic fare collection. APC = Automatic passenger counters.

Note: Three boardings and three alightings of wheeled mobility devices were observed. The combined dwell time for these observations was 502 seconds, accounting for 1.6 percent of total observed dwell time (0.2 percent of total run time).

 

The relatively small impact of cash transactions on total run time suggests that fare system upgrades that eliminate on-board cash transactions may improve reliability for specific trips or during certain time periods that experience relatively high cash usage. However, these upgrades likely would not reduce run times on the observed trips to a degree that allows service to be scheduled more frequently with the same amount of resources (unless significant time savings are achieved through all-door boarding). Figure 2 shows that the estimated amount of time added by pay cash and add value transactions was less than two minutes for all observed trips, with the largest estimated amount of time added for a single trip being 2.0 minutes (4.7 percent of that trip’s total run time). Figure 2 also shows that trips with relatively larger estimated amounts of time added because of pay cash and add value transactions tended to occur during the midday period of service, with less of an effect during the AM peak and PM peak periods of service.

 

Figure 3 shows that the estimated amount of time added by baby carriage boardings and alightings was less than one minute for all observed trips, with the largest estimated amount of time added for a single trip being 0.8 minutes (1.2 percent of that trip’s total run time). Figure 3 also shows that, as with trips that have relatively larger estimated amounts of time added because of pay cash and add value transactions, trips with relatively larger amounts of time added because of baby carriage boardings and alightings tended to occur during the midday period of service, with less of an effect during the AM peak and PM peak periods of service. Figure 4 shows that the estimated amount of time added by pay cash and add value transactions and baby carriage boardings and alightings combined was less than 2.5 minutes for all observed trips, with the largest estimated amount of time added for a single trip being 2.3 minutes (5.4 percent of that trip’s total run time).

 

The variables representing shopping cart boardings and alightings, baby carriages on-board, shopping carts on-board, and wheeled mobility devices on-board were not significant at a five percent confidence level when included in the model; however, this does not necessarily mean that these variables are not significant contributors to dwell time in the real world. It is possible that the dataset did not contain enough observations of these variables to estimate their effects5. Because the on-board presence of baby carriages, shopping carts, and wheeled mobility devices were not significant contributors to the model, no conclusions on their effect on dwell time can be drawn, and it remains unclear whether a seating arrangement designed to provide more room for these devices might produce an overall decrease in dwell time.

 

The results of this analysis are contingent on the observations of only two bus routes, and a short observation period of seven weekdays6. Therefore, these results are not meant to reflect the MBTA system as a whole, as the effect and rate at which the variables in the model occur likely would vary geographically and temporally.

 

3 Total run time for the trips that were observed may have been increased by buses waiting for the Andrew McArdle Bridge to reopen when allowing commercial maritime vessels to pass.

4 Activity at the first stop of each trip was included if it occurred after the trip’s scheduled departure time, and excluded if the trip left before or at its scheduled departure time.

5 An analysis of variance was performed between the chosen model and a set of nested models containing each of the variables that were excluded. A nested model containing both the Cart Ons and Cart Offs variables was shown to have a significant reduction in the residual sum of squares at a five percent confidence level, and a nested model containing the Carriage On-board variable was shown to have a significant reduction in the residual sum of squares at a ten percent confidence level.

6 Observations from 97 bus trips were used for this analysis. The MBTA runs approximately 4 million bus trips annually.

 


Figure 2
Estimated Delay from Pay Cash and Add Value Transactions by Trip

(Sampled Trips from Bus Routes 116 and 117,

January 12, 2016 to January 21, 2016)

 

Figure 2 is a non-accessible scatter chart. This figure shows that the estimated amount of time added by pay cash and add value transactions was less than two minutes for all observed trips, with the largest estimated amount of time added for a single trip being 2.0 minutes (4.7 percent of that trip’s total run time). Figure 2 also shows that trips with relatively larger estimated amounts of time added because of pay cash and add value transactions tended to occur during the midday period of service, with less of an effect during the AM peak and PM peak periods of service.

 

Figure 2 is a non-accessible scatter chart. This figure shows that the estimated amount of time added by pay cash and add value transactions was less than two minutes for all observed trips, with the largest estimated amount of time added for a single trip being 2.0 minutes (4.7 percent of that trip’s total run time). Figure 2 also shows that trips with relatively larger estimated amounts of time added because of pay cash and add value transactions tended to occur during the midday period of service, with less of an effect during the AM peak and PM peak periods of service.

 

Figure 2 is a non-accessible scatter chart. This figure shows that the estimated amount of time added by pay cash and add value transactions was less than two minutes for all observed trips, with the largest estimated amount of time added for a single trip being 2.0 minutes (4.7 percent of that trip’s total run time). Figure 2 also shows that trips with relatively larger estimated amounts of time added because of pay cash and add value transactions tended to occur during the midday period of service, with less of an effect during the AM peak and PM peak periods of service.

Figure 2 is a non-accessible scatter chart. This figure shows that the estimated amount of time added by pay cash and add value transactions was less than two minutes for all observed trips, with the largest estimated amount of time added for a single trip being 2.0 minutes (4.7 percent of that trip’s total run time). Figure 2 also shows that trips with relatively larger estimated amounts of time added because of pay cash and add value transactions tended to occur during the midday period of service, with less of an effect during the AM peak and PM peak periods of service.

 

Figure 3
Estimated Delay from
Baby Carriage Boardings and Alightings by Trip
(Sampled Trips from Bus Routes 116 and 117,
January 12, 2016 to January 21, 2016)

 

Figure 3 is a non-accessible scatter chart. It shows that the estimated amount of time added by baby carriage boardings and alightings was less than one minute for all observed trips, with the largest estimated amount of time added for a single trip being 0.8 minutes (1.2 percent of that trip’s total run time). Figure 3 also shows that, as with trips that have relatively larger estimated amounts of time added because of pay cash and add value transactions, trips with relatively larger amounts of time added because of baby carriage boardings and alightings tended to occur during the midday period of service, with less of an effect during the AM peak and PM peak periods of service.


 

Figure 3 is a non-accessible scatter chart. It shows that the estimated amount of time added by baby carriage boardings and alightings was less than one minute for all observed trips, with the largest estimated amount of time added for a single trip being 0.8 minutes (1.2 percent of that trip’s total run time). Figure 3 also shows that, as with trips that have relatively larger estimated amounts of time added because of pay cash and add value transactions, trips with relatively larger amounts of time added because of baby carriage boardings and alightings tended to occur during the midday period of service, with less of an effect during the AM peak and PM peak periods of service.

 

Figure 3 is a non-accessible scatter chart. It shows that the estimated amount of time added by baby carriage boardings and alightings was less than one minute for all observed trips, with the largest estimated amount of time added for a single trip being 0.8 minutes (1.2 percent of that trip’s total run time). Figure 3 also shows that, as with trips that have relatively larger estimated amounts of time added because of pay cash and add value transactions, trips with relatively larger amounts of time added because of baby carriage boardings and alightings tended to occur during the midday period of service, with less of an effect during the AM peak and PM peak periods of service.

 

Figure 3 is a non-accessible scatter chart. It shows that the estimated amount of time added by baby carriage boardings and alightings was less than one minute for all observed trips, with the largest estimated amount of time added for a single trip being 0.8 minutes (1.2 percent of that trip’s total run time). Figure 3 also shows that, as with trips that have relatively larger estimated amounts of time added because of pay cash and add value transactions, trips with relatively larger amounts of time added because of baby carriage boardings and alightings tended to occur during the midday period of service, with less of an effect during the AM peak and PM peak periods of service.

 

Figure 4
Estimated Delay from Pay Cash and Add Value Transactions and
Baby Carriage Boardings and Alightings by Trip

(Sampled Trips from Bus Routes 116 and 117,
January 12, 2016 to January 21, 2016)

 

 

Figure 4 shows that the estimated amount of time added by pay cash and add value transactions and baby carriage boardings and alightings combined was less than 2.5 minutes for all observed trips, with the largest estimated amount of time added for a single trip being 2.3 minutes (5.4 percent of that trip’s total run time).

 

Figure 4 shows that the estimated amount of time added by pay cash and add value transactions and baby carriage boardings and alightings combined was less than 2.5 minutes for all observed trips, with the largest estimated amount of time added for a single trip being 2.3 minutes (5.4 percent of that trip’s total run time).

 

Figure 4 shows that the estimated amount of time added by pay cash and add value transactions and baby carriage boardings and alightings combined was less than 2.5 minutes for all observed trips, with the largest estimated amount of time added for a single trip being 2.3 minutes (5.4 percent of that trip’s total run time).

 

Figure 4 shows that the estimated amount of time added by pay cash and add value transactions and baby carriage boardings and alightings combined was less than 2.5 minutes for all observed trips, with the largest estimated amount of time added for a single trip being 2.3 minutes (5.4 percent of that trip’s total run time).

 

Chapter 3—Assessing Operational and Scheduling Improvements to Alleviate Bus Delay

 

CTPS assessed areas where improvements to operational and scheduling practices can be made to alleviate bus delay, including (3.1) interlines, (3.2) deadhead movements, (3.3) garage pull-outs, (3.4) supplemental trips, (3.5) bus operator swing-ons, and (3.6) early pull-up opportunity.

 

3.1      Interlines

An interline is when a vehicle is scheduled to complete a trip on one route and conduct its next trip on another route, either between two routes that share a common start/end point or through a deadhead movement. The benefit of interlining is that it allows resources to be utilized more efficiently, with less vehicle movements to and from the garage. A negative effect of interlining is the possibility that a route/corridor with normally reliable service can take on the delay from a route/corridor with poor on-time performance. From a customer-experience perspective, it is generally preferred that customers on one route/corridor are not negatively impacted by recurring delay from another route/corridor, and that routes with poor reliability are self-contained.

 

The MBTA uses the Horaires et Assignments pour Systems de Transport Urban et Semi-Urban (HASTUS) scheduling software to automatically block (form the string of trips that is assigned to an individual bus) and assign runs (the string of trips that is assigned to an individual operator) to build bus schedules for each bus district. The automatic blocking capabilities of HASTUS provide an opportunity to automatically schedule interlines into each garage’s schedule. CTPS was tasked with assessing run-time variability of trips to provide a list of suggested trips that should not be coded for interlining (assuming that proper run times, recovery times, and other factors are programmed into HASTUS for each trip). CTPS assessed the run-time variability of trips from the Charlestown bus district using fall 2015 Automatic Vehicle Location (AVL) data provided by the MBTA.

 

Staff utilized two thresholds to assess run-time variability; the 80 percentile run time compared to 120 percent of the scheduled run time if a trip is to be interlined at its end point, and the 90 percentile run time compared to 120 percent of the scheduled run time if a trip is to be interlined with a deadhead movement, to account for additional variability in the deadhead run time. Trips that do not pass these thresholds are not meant to form a definitive list of trips that should not be interlined, as there are scheduling techniques that may reduce the impact of run-time variability, such as increasing the scheduled recovery time. At most, the summary of trips based on these thresholds should be used to identify trips, routes, locations, and times of day where extra attention might be warranted.

 

Table 6 provides a summary of run-time variability by route; Table 7 provides a summary of run-time variability by trip end point; and Table 8 provides a summary of run-time variability by trip end time for trips from the Charlestown bus district.

 

Table 6
Run-Time Variability by Route—Charlestown District, Fall 2015

Route

Dir.

Scheduled Weekday Trips

Number of Trips with 80 Percentile Run Time Greater than 120 Percent of Scheduled Run Time

Number of Trips with 90 Percentile Run Time Greater than 120 Percent of Scheduled Run Time

Percentage of Trips with 80 Percentile Run Time Greater than 120 Percent of Scheduled Run Time

Percentage of Trips with 90 Percentile Run Time Greater than 120 Percent of Scheduled Run Time

89

In

33

18

25

55%

76%

89

Out

28

14

18

50%

64%

89-2

In

32

27

29

84%

91%

89-2

Out

32

2

3

6%

9%

89-4

Out

1

0

0

0%

0%

90

In

19

1

3

5%

16%

90

Out

19

2

8

11%

42%

91

In

36

24

30

67%

83%

91

Out

35

27

32

77%

91%

91-7

In

6

3

5

50%

83%

91-7

Out

7

4

4

57%

57%

92-1

In

15

3

8

20%

53%

92-1

Out

13

6

9

46%

69%

92-3

In

22

13

16

59%

73%

92-3

Out

24

11

16

46%

67%

92-7

In

3

0

1

0%

33%

92-7

Out

2

0

1

0%

50%

93

Out

2

1

1

50%

50%

93-1

In

56

28

41

50%

73%

93-1

Out

55

24

42

44%

76%

93-2

In

12

9

12

75%

100%

93-2

Out

13

12

13

92%

100%

93-7

In

8

1

3

13%

38%

93-7

Out

8

1

5

13%

63%

94-1

In

11

2

3

18%

27%

94-1

Out

10

8

9

80%

90%

95

In

8

0

2

0%

25%

95

Out

9

2

4

22%

44%

95-5

Out

1

1

1

100%

100%

96

In

14

11

13

79%

93%

96

Out

14

3

5

21%

36%

99-7

In

6

2

5

33%

83%

99-7

Out

7

0

1

0%

14%

100-3

In

9

1

3

11%

33%

100-3

Out

8

1

3

13%

38%

101-1

In

4

2

2

50%

50%

101-1

Out

2

2

2

100%

100%

101-2

Out

4

3

3

75%

75%

101-3

In

39

32

36

82%

92%

101-3

Out

32

15

19

47%

59%

101-4

In

13

7

9

54%

69%

101-4

Out

18

9

12

50%

67%

104

In

48

25

30

52%

63%

104

Out

45

18

26

40%

58%

105-1

In

1

0

0

0%

0%

105-1

Out

1

0

0

0%

0%

106

In

7

0

4

0%

57%

106

Out

24

9

11

38%

46%

106-3

In

19

11

13

58%

68%

106-4

In

1

1

1

100%

100%

106-4

Out

1

0

0

0%

0%

106-6

Out

1

0

0

0%

0%

106-7

In

11

8

11

73%

100%

106-7

Out

10

2

5

20%

50%

108-6

In

7

3

3

43%

43%

108-6

Out

8

2

3

25%

38%

109

In

52

19

35

37%

67%

109

Out

48

34

38

71%

79%

109-1

In

2

1

1

50%

50%

109-1

Out

8

0

1

0%

13%

110

In

36

19

23

53%

64%

110

Out

35

7

11

20%

31%

110-2

Out

2

1

1

50%

50%

110-4

In

13

9

11

69%

85%

110-4

Out

11

9

10

82%

91%

111-2

In

66

36

46

55%

70%

111-2

Out

72

45

52

63%

72%

111-5

In

88

61

69

69%

78%

111-5

Out

78

27

41

35%

53%

111-6

In

27

22

26

81%

96%

111-8

In

19

1

5

5%

26%

111-8

Out

24

11

14

46%

58%

112

In

20

7

10

35%

50%

112

Out

21

5

11

24%

52%

112-3

Out

1

0

0

0%

0%

132

In

1

0

0

0%

0%

132

Out

1

1

1

100%

100%

134-5

In

3

1

2

33%

67%

134-5

Out

3

0

1

0%

33%

134-6

In

1

1

1

100%

100%

134-7

In

5

3

4

60%

80%

134-7

Out

7

1

1

14%

14%

136-4

In

2

1

2

50%

100%

136-4

Out

2

0

0

0%

0%

136-5

In

1

0

0

0%

0%

136-5

Out

1

0

0

0%

0%

137-3

In

2

0

0

0%

0%

137-3

Out

2

0

1

0%

50%

194

In

1

0

0

0%

0%

325

In

7

2

3

29%

43%

325

Out

7

7

7

100%

100%

325-1

In

9

7

7

78%

78%

325-1

Out

6

0

1

0%

17%

325-2

Out

3

3

3

100%

100%

326

In

6

0

0

0%

0%

326

Out

11

11

11

100%

100%

326-1

In

12

12

12

100%

100%

326-1

Out

5

2

4

40%

80%

326-2

Out

1

0

1

0%

100%

352

In

9

5

5

56%

56%

352

Out

8

7

8

88%

100%

354-4

Out

1

0

0

0%

0%

411-1

In

1

1

1

100%

100%

411-1

Out

1

1

1

100%

100%

411-8

In

1

0

0

0%

0%

411-8

Out

1

0

0

0%

0%

430-1

In

1

1

1

100%

100%

430-1

Out

1

1

1

100%

100%

430-2

In

2

2

2

100%

100%

430-2

Out

2

1

2

50%

100%

608

In

60

1

1

2%

2%

Total

1,684

797

1,053

47%

63%

 

Table 7
Run-Time Variability by Trip End Point—Charlestown District, Fall 2015

Trip End Point

Scheduled Weekday Trips

Number of Trips with 80 Percentile Run Time Greater than 120 Percent
of Scheduled
Run Time

Number of Trips with 90 Percentile Run Time Greater than 120 Percent
of Scheduled
 Run Time

Percentage of Trips with 80 Percentile Run Time Greater than 120 Percent
of Scheduled
 Run Time

Percentage of Trips with 90 Percentile Run Time Greater than 120 Percent
 of Scheduled
Run Time

sull

378

211

283

56%

75%

hayms

246

142

172

58%

70%

welst

158

64

94

41%

59%

malst

108

48

64

44%

59%

milk

105

53

77

50%

73%

woodc

80

28

42

35%

53%

wacry

72

45

52

63%

72%

davis

62

6

14

10%

23%

haymg

60

1

1

2%

2%

lindn

56

36

41

64%

73%

cntsq

42

27

35

64%

83%

wondw

35

7

11

20%

31%

bwypk

35

20

24

57%

69%

medfd

34

14

18

41%

53%

clarh

29

14

18

48%

62%

plast

25

15

19

60%

76%

leban

25

9

11

36%

44%

elm

21

8

11

38%

52%

woodi

20

7

10

35%

50%

bally

14

11

13

79%

93%

amall

13

6

9

46%

69%

fklin

10

2

5

20%

50%

statx

9

5

5

56%

56%

chnut

9

7

8

78%

89%

glndl

8

0

1

0%

13%

uphgh

7

0

1

0%

14%

wnbrk

5

4

4

80%

80%

nwobn

3

0

1

0%

33%

saug

3

2

3

67%

100%

readc

3

0

1

0%

33%

rosec

3

3

3

100%

100%

wakef

2

0

0

0%

0%

rdstn

1

1

1

100%

100%

grana

1

1

1

100%

100%

lwdly

1

0

0

0%

0%

revhs

1

0

0

0%

0%

Total

1,684

797

1,053

47%

63%

 


 

Table 8
Run-Time Variability by Trip End Time—Charlestown District, Fall 2015

Trip End Time

Scheduled Weekday Trips

Number of Trips with 80 Percentile Run Time Greater than 120 Percent of Scheduled
Run Time

Number of Trips with 90 Percentile Run Time Greater than 120 Percent of Scheduled
 Run Time

Percentage of Trips with 80 Percentile Run Time Greater
than 120 Percent of Scheduled
Run Time

Percentage of Trips with 90 Percentile Run Time Greater
than 120 Percent of Scheduled
Run Time

4:00-4:59

2

0

0

0%

0%

5:00-5:59

71

21

35

30%

49%

6:00-6:59

96

45

56

47%

58%

7:00-7:59

121

81

95

67%

79%

8:00-8:59

128

81

96

63%

75%

9:00-9:59

101

40

61

40%

60%

10:00-10:59

55

16

28

29%

51%

11:00-11:59

58

16

30

28%

52%

12:00-12:59

55

14

25

25%

45%

13:00-13:59

54

8

15

15%

28%

14:00-14:59

66

28

41

42%

62%

15:00-15:59

91

53

73

58%

80%

16:00-16:59

123

89

100

72%

81%

17:00-17:59

140

109

118

78%

84%

18:00-18:59

127

81

96

64%

76%

19:00-19:59

92

31

53

34%

58%

20:00-20:59

71

20

34

28%

48%

21:00-21:59

61

18

33

30%

54%

22:00-22:59

58

19

27

33%

47%

23:00-23:59

50

14

21

28%

42%

24:00-24:59

46

10

11

22%

24%

25:00-25:59

18

3

5

17%

28%

Total

1,684

797

1,053

47%

63%

 

Table 9 provides a list of fall 2015 trips that were scheduled as a lead in to a non-deadhead interline that did not pass the 80 percentile run-time comparison to the 120 percent of the scheduled run-time threshold (147 out of 290 such trips).

 

Table 9
Trips Leading into Non-Deadhead Interlines that Do Not Pass the Run-Time Variability Threshold—Charlestown District, Fall 2015

Route

Dir.

Start Time

Number of Obs.

80 Percentile Run Time (min.)

120 Percent of Scheduled Run Time (min.)

80 Percentile Run Time Minus 120 Percent of Scheduled Run Time (min.)

101-3

In

7:23

31

46.9

31.2

15.7

101-3

In

7:10

57

46.8

31.2

15.6

104

In

24:41

57

38.3

22.8

15.5

93-2

In

15:58

44

33.9

20.4

13.5

101-3

Out

14:07

5

61.1

48

13.1

106-3

In

7:33

54

52.6

40.8

11.8

89

In

7:45

59

35.8

25.2

10.6

101-3

In

6:50

41

41.5

31.2

10.3

101-3

In

7:42

55

44.7

34.8

9.9

89-2

In

7:39

55

29.5

20.4

9.1

101-3

In

8:18

56

48.1

39.6

8.5

101-3

In

16:32

43

39.6

31.2

8.4

110

In

7:18

55

45.5

37.2

8.3

106-3

In

7:10

59

51.4

43.2

8.2

101-3

In

8:30

52

44.0

36

8.0

89-2

In

8:15

51

28.2

20.4

7.8

101-3

In

7:54

53

47.1

39.6

7.5

89

In

7:27

57

31.1

24

7.1

93-1

In

7:19

39

28.4

21.6

6.8

101-3

In

15:35

50

37.6

31.2

6.4

104

In

16:44

51

37.6

31.2

6.4

91

Out

16:05

47

29.1

22.8

6.3

89

In

16:55

46

29.0

22.8

6.2

325

In

16:15

49

33.8

27.6

6.2

106-3

In

7:57

55

43.3

37.2

6.1

101-3

In

15:05

54

36.6

31.2

5.4

93-1

In

15:30

54

25.7

20.4

5.3

89

In

14:43

56

26.7

21.6

5.1

89-2

In

18:04

55

23.1

18

5.1

89

In

7:09

55

27.9

22.8

5.1

101-4

Out

8:40

5

48.3

43.2

5.1

101-3

In

6:30

57

32.5

27.6

4.9

91

Out

14:45

43

27.6

22.8

4.8

93-1

Out

7:53

43

26.4

21.6

4.8

112

Out

7:20

55

58.6

54

4.6

89-2

In

17:28

54

25.9

21.6

4.3

91

Out

17:35

55

28.2

24

4.2

104

In

17:44

37

31.8

27.6

4.2

134-5

In

6:10

47

49.6

45.6

4.0

110

In

16:44

56

38.8

34.8

4.0

104

In

16:05

17

35.1

31.2

3.9

109

In

16:45

34

30.0

26.4

3.6

106-3

In

5:30

57

30.0

26.4

3.6

92-3

Out

16:24

43

27.6

24

3.6

92-3

Out

16:09

42

24.0

20.4

3.6

101-3

In

6:40

9

31.1

27.6

3.5

89-2

In

15:13

56

25.0

21.6

3.4

104

In

14:00

57

40.6

37.2

3.4

110

In

18:25

56

33.3

30

3.3

430-2

In

22:25

54

30.9

27.6

3.3

92-1

In

15:26

54

34.5

31.2

3.3

104

In

20:40

53

25.9

22.8

3.1

93-2

Out

8:38

51

24.6

21.6

3.0

109

In

14:55

54

31.8

28.8

3.0

91

Out

13:45

53

23.4

20.4

3.0

93-1

Out

16:45

54

28.2

25.2

3.0

91

Out

18:05

39

26.8

24

2.8

91-7

Out

19:45

53

19.5

16.8

2.7

92-3

Out

8:32

27

21.9

19.2

2.7

93-1

Out

7:10

50

21.9

19.2

2.7

111-2

In

16:31

51

21.8

19.2

2.6

92-3

Out

17:39

55

27.8

25.2

2.6

104

In

18:15

44

27.8

25.2

2.6

93-1

Out

19:05

55

25.4

22.8

2.6

92-3

Out

15:54

54

23.0

20.4

2.6

109

In

5:21

53

25.3

22.8

2.5

93-2

Out

18:05

56

28.9

26.4

2.5

91

Out

18:30

51

25.3

22.8

2.5

92-3

In

7:35

43

26.4

24

2.4

93-1

In

15:45

43

22.8

20.4

2.4

89-2

In

21:25

56

19.2

16.8

2.4

109

In

13:40

38

31.2

28.8

2.4

92-3

Out

16:39

55

27.5

25.2

2.3

93-1

Out

17:10

46

31.0

28.8

2.2

101-4

In

13:06

54

33.4

31.2

2.2

89-2

In

13:30

54

20.1

18

2.1

89-2

In

16:30

52

23.7

21.6

2.1

91-7

Out

22:15

56

16.5

14.4

2.1

93-2

Out

14:25

56

27.2

25.2

2.0

89

In

16:36

45

27.2

25.2

2.0

91

Out

7:15

49

19.9

18

1.9

101-3

In

17:08

4

33.1

31.2

1.9

104

Out

17:52

46

35.5

33.6

1.9

91

Out

7:45

59

19.9

18

1.9

104

In

5:11

50

23.5

21.6

1.9

93-1

Out

6:56

49

21.0

19.2

1.8

93-1

Out

17:17

53

29.4

27.6

1.8

89

In

11:52

55

23.4

21.6

1.8

91

Out

8:15

54

19.8

18

1.8

93-2

Out

13:25

55

25.8

24

1.8

89-2

In

16:07

56

23.3

21.6

1.7

93-1

Out

8:08

53

23.3

21.6

1.7

89

In

18:56

50

24.5

22.8

1.7

110-4

In

5:36

48

22.0

20.4

1.6

93

Out

5:03

51

11.2

9.6

1.6

110-4

In

17:48

56

23.2

21.6

1.6

430-2

In

21:00

57

37.5

36

1.5

93-1

Out

8:15

49

23.1

21.6

1.5

411-1

In

18:25

56

18.3

16.8

1.5

104

In

19:45

57

24.3

22.8

1.5

91

Out

8:40

53

19.5

18

1.5

109

In

7:16

42

42.3

40.8

1.4

89

In

8:40

55

26.6

25.2

1.4

91

Out

12:35

55

21.8

20.4

1.4

106-3

In

6:30

56

35.0

33.6

1.4

110-2

Out

14:17

18

27.8

26.4

1.4

89-2

In

19:55

56

18.1

16.8

1.3

93-1

Out

17:01

52

30.1

28.8

1.3

92-3

Out

8:47

46

20.5

19.2

1.3

93-2

Out

7:28

54

22.8

21.6

1.2

104

In

15:05

33

32.4

31.2

1.2

112

Out

18:00

55

51.6

50.4

1.2

93-1

Out

17:49

47

27.5

26.4

1.1

134-7

In

21:15

56

13.0

12

1.0

94-1

In

21:15

54

17.8

16.8

1.0

134-7

In

20:15

56

12.9

12

0.9

110

In

7:48

55

38.1

37.2

0.9

92-3

Out

16:54

37

29.7

28.8

0.9

109

In

21:10

56

22.5

21.6

0.9

109

In

18:15

48

22.4

21.6

0.8

93-1

In

7:33

45

26.0

25.2

0.8

91

Out

9:00

15

20.0

19.2

0.8

134-7

In

23:15

59

11.5

10.8

0.7

104

In

6:55

13

39.1

38.4

0.7

89-2

In

6:03

51

16.3

15.6

0.6

110-4

In

6:12

57

25.8

25.2

0.6

93-7

Out

23:25

51

13.8

13.2

0.6

104

Out

14:50

50

37.8

37.2

0.6

104

In

19:20

49

24.6

24

0.6

93-1

Out

16:37

59

24.5

24

0.5

92-1

In

15:38

43

31.7

31.2

0.5

92-1

In

16:10

50

31.7

31.2

0.5

91

Out

6:50

56

13.7

13.2

0.5

93-1

Out

17:33

55

26.9

26.4

0.5

101-4

In

9:44

52

36.4

36

0.4

94-1

Out

5:39

30

13.6

13.2

0.4

101-1

Out

18:53

13

20.8

20.4

0.4

100-3

In

6:00

52

14.7

14.4

0.3

94-1

Out

24:10

47

17.1

16.8

0.3

93-1

Out

15:40

55

24.3

24

0.3

93-1

Out

16:04

52

24.3

24

0.3

111-5

In

18:25

40

25.5

25.2

0.3

325

In

16:35

54

27.9

27.6

0.3

92-3

Out

17:09

43

30.2

30

0.2

89-2

In

10:17

57

19.4

19.2

0.2

93-1

Out

18:35

49

23.0

22.8

0.2

104

Out

15:10

45

34.9

34.8

0.1

 

Table 10 provides a list of fall 2015 trips that were scheduled as a lead-in to an interline that required a deadhead movement that did not pass the 90 percentile run-time comparison to the120 percent of the scheduled run-time threshold (46 out of 74 such trips).

 

 

Table 10
Trips Leading into Deadhead Interlines that Do Not Pass the Run-Time Variability Threshold—Charlestown District, Fall 2015

 

Route

Dir.

Start
Time

Number of Obs.

90 Percentile
Run Time (min.)

120 Percent of Scheduled
 Run Time (min.)

90 Percentile Run Time Minus 120 Percent of Scheduled Run Time (min.)

326

Out

18:35

50

39.8

19.2

20.6

352

Out

15:20

50

53.6

38.4

15.2

134-6

In

7:25

44

40.5

27.6

12.9

110

In

7:08

56

49.5

37.2

12.3

352

In

8:30

46

58.7

46.8

11.9

101-4

Out

21:30

9

43.1

32.4

10.7

111-5

In

6:37

53

41.8

31.2

10.6

326

Out

17:53

48

30.6

20.4

10.2

91

Out

15:05

47

32.7

22.8

9.9

110

In

7:28

56

46.1

37.2

8.9

325-2

Out

18:10

43

30.5

22.8

7.7

109

In

14:15

54

36.0

28.8

7.2

101-3

In

14:35

56

41.8

34.8

7.0

352

Out

17:35

51

51.4

44.4

7.0

111-2

In

15:50

50

25.5

19.2

6.3

101-2

Out

7:10

52

24.2

18

6.2

101-2

Out

7:20

53

24.1

18

6.1

101-3

In

8:06

4

44.3

38.4

5.9

101-3

In

14:50

17

38.0

32.4

5.6

91

Out

11:50

53

25.4

20.4

5.0

101-3

In

15:50

44

36.2

31.2

5.0

326-1

In

7:20

54

32.5

27.6

4.9

136-4

In

6:00

33

33.5

28.8

4.7

93-2

Out

8:48

40

25.8

21.6

4.2

136-4

In

5:30

52

32.9

28.8

4.0

106-7

In

10:15

42

49.4

45.6

3.8

106-3

In

17:16

55

39.7

36

3.7

104

Out

16:22

56

42.1

38.4

3.7

111-6

In

6:23

57

24.0

20.4

3.6

91

Out

13:20

54

23.9

20.4

3.5

111-5

Out

18:55

9

30.8

27.6

3.2

95-5

Out

7:00

43

17.6

14.4

3.2

110-4

In

6:30

57

28.3

25.2

3.1

104

Out

22:15

56

25.6

22.8

2.8

101-3

In

14:09

56

38.7

36

2.7

111-6

In

6:06

56

22.9

20.4

2.4

111-6

In

6:13

48

22.8

20.4

2.4

112

Out

8:40

48

56.2

54

2.2

101-4

In

12:33

48

36.8

34.8

2.0

104

Out

19:20

44

30.5

28.8

1.7

89

In

15:25

56

26.7

25.2

1.5

104

In

17:14

48

32.3

31.2

1.1

93-1

Out

6:36

55

20.2

19.2

1.0

104

In

14:45

57

34.6

33.6

1.0

326-1

In

6:25

59

25.0

24

1.0

106-4

In

18:33

45

11.2

10.8

0.4

 

3.2      Deadhead Movements

Unlike vehicle-revenue trips, HASTUS does not allow schedule makers to attach recovery time to deadhead movements. Therefore, it is especially important that programmed run times for deadhead movements are not undervalued. CTPS assessed deadhead run times using AVL observations from spring 2016. Table 11 provides a list of 41 (out of 437) scheduled weekday deadhead movements that had median run times greater than scheduled run times.

 

Table 11
Deadhead Movements with Median Run Times Greater than
Scheduled Run Times, Spring 2016

District

Start Point

End Point

Start Time

Number of Obs.

Scheduled Run Time (min.)

Median Run Time (min.)

Median Run Time - Scheduled Run Time (min.)

Cabot

jasst

Dudly

14:52

47

41

54.8

13.8

Cabot

cntsq

brway

17:22

32

28

38.8

10.8

Quincy

qnctr

nwbhl

14:07

63

28

35.3

7.3

Cabot

fhill

jqnsb

14:40

50

20

27.1

7.1

Quincy

newbl

qnctr

15:08

61

17

23.2

6.2

Cabot

rugg

jqnsb

10:18

10

12

17.2

5.2

Charlestown

hayms

warbp

6:08

48

20

24.6

4.6

Charlestown

malst

uphgh

7:50

60

10

14.6

4.6

Bennett

clarh

arlht

15:09

47

26

30.5

4.5

Charlestown

hayms

wacry

9:05

9

12

16.4

4.4

Charlestown

malst

medfd

8:43

63

17

21.4

4.4

Arborway

fhill

louis

13:53

23

22

25.5

3.5

Charlestown

mdlsc

sull

15:08

49

27

30.4

3.4

Bennett

rindg

alewf

15:12

64

18

21.1

3.1

Quincy

nqncy

fldcr

15:03

22

17

19.8

2.8

Charlestown

fells

sull

14:54

45

16

18.5

2.5

Charlestown

sull

davis

18:33

22

17

19.4

2.4

Bennett

alewf

rindg

19:02

19

13

15.1

2.1

Cabot

bumed

andrw

8:14

28

11

13.1

2.1

Arborway

matpn

fhill

18:37

18

23

25.0

2.0

Cabot

matpn

kenbs

14:25

33

41

43.0

2.0

Cabot

conat

ctypt

8:27

61

8

9.5

1.5

Bennett

alewf

bdfwd

17:12

57

68

69.4

1.4

Arborway

ashmt

matpn

14:54

12

16

17.3

1.3

Arborway

louis

fhill

7:17

13

22

23.1

1.1

Bennett

cntsq

alewf

17:31

50

34

35.1

1.1

Cabot

bumed

andrw

8:34

9

11

12.1

1.1

Arborway

Dudly

jpctr

6:59

58

14

15.1

1.1

Charlestown

woodc

welst

14:39

61

16

16.9

0.9

Arborway

louis

fhill

7:06

57

22

22.9

0.9

Cabot

ashmt

Dudly

16:14

48

29

29.8

0.8

Arborway

fhill

cloop

15:59

57

27

27.8

0.8

Cabot

conat

ctypt

8:34

58

8

8.7

0.7

Bennett

bally

sull

18:47

50

21

21.7

0.7

Cabot

conat

ctypt

7:57

48

8

8.6

0.6

Charlestown

hayms

warbp

5:37

59

21

21.4

0.4

Lynn

wlynn

mavck

15:01

49

19

19.4

0.4

Cabot

conat

ctypt

8:06

61

9

9.2

0.2

Cabot

andrw

bumed

15:16

58

10

10.2

0.2

Charlestown

sull

davis

19:19

54

16

16.1

0.1

Cabot

andrw

bumed

15:36

54

10

10.1

0.1

 

3.3      Garage Pull-Outs

Unlike vehicle-revenue trips and deadhead movements between two vehicle-revenue trips, assessing delay caused by pull-out movements from the garage as a function of observed run time would be misleading, as operators are permitted to leave the garage as much as 20 minutes early to pick up items such as coffee and water on the way to their first in-service trip. Without knowing which AVL observations contain such a stop along the way, assessing delay is limited to investigating how often operators leave the garage on time, and how late operators arrive at the start point of their first vehicle-revenue trip when they do not leave the garage on time.

 

Figure 5
Departure Lateness from Garage—Systemwide Observations, Spring 2016

 

Figure 5 is a non-accessible bar chart. The figure provides a summary of departure time from the garage for all pull-out trips with AVL observations from spring 2016.

 

Table 12 provides a summary of the number of pull-out trips that departed the garage more than three minutes late, delineated by garage.

 

Table 13 provides a summary of how late trips arrived at the start point of their first vehicle-revenue trip when they departed more than three minutes late from the garage, delineated by garage.


 

Table 12
Summary of Departure Lateness from Garage, Spring 2016

Garage

Spring 2016 Observations

Number of Observations that
Departed Garage More than
Three Minutes Late

Percentage of Observations that
Departed Garage More than
Three Minutes Late

Percentage of all Observations that Departed Garage More than Three Minutes Late and Arrived for First Trip After Scheduled Departure

Albany

7,775

2,814

36.2%

18.9%

Arborway

14,787

6,099

41.2%

33.0%

Cabot

12,439

4,035

32.4%

25.7%

Charlestown

11,254

3,111

27.6%

20.1%

Fellsway

5,271

1,489

28.2%

17.3%

Lynn

6,841

1,401

20.5%

12.6%

North Cambridge

782

338

43.2%

29.5%

Quincy

7,308

1,961

26.8%

21.8%

Southampton

3,207

934

29.1%

15.4%

Somerville

9,558

2,694

28.2%

17.0%

Total

79,222

24,876

31.4%

22.1%

 

 

Table 13
Summary of First Trip Arrival Lateness of Pull-Outs that Depart more than Three Minutes Late from the Garage, Spring 2016

Garage

Number of Observations that Arrived for First Trip After Scheduled Departure

Percentage of Observations that Arrived for First Trip After Scheduled Departure

Arrival Lateness per Trip of Observations that Arrived for First Trip After Scheduled Departure (min.)

Albany

1,472

52.3%

3.6

Arborway

4,876

79.9%

3.8

Cabot

3,202

79.4%

4.5

Charlestown

2,260

72.6%

3.9

Fellsway

914

61.4%

3.0

Lynn

859

61.3%

3.9

North Cambridge

231

68.3%

2.7

Quincy

1,591

81.1%

3.0

Southampton

493

52.8%

4.0

Somerville

1,621

60.2%

4.4

Total

17,519

70.4%

3.9

 

Of the 79,222 observations in the spring 2016 dataset, 24,876 (31.4 percent) of pull-out trips departed more than three minutes late from the garage. Of these, 17,519 (22.1 percent of all pull-outs, and 70.4 percent of pull-outs that departed more than three minutes late) arrived at the start point of their first vehicle-revenue trip after the scheduled departure time, with an average arrival lateness of 3.9 minutes per trip.

 

3.4      Supplemental MBTA Bus Trips

On weekdays throughout the midafternoon and early PM peak periods, the MBTA operates a series of supplemental bus trips; these are different from typical scheduled bus service as they traverse along a unique route variation and hold at their origin point until bus operations field staff release them for departure. The time of day that bus operations field staff release each supplemental trip often can change without notice, presenting a challenge when building them into a schedule each quarter. Because supplemental trips are typically scheduled following a pull-out from the garage, arriving on time for departure is generally not an issue from a scheduling perspective. However, when bus operations field staff adjust when a supplemental trip is released it affects when the bus arrives to start its next trip, which may cause that trip to depart late. Such delay has the potential to resonate throughout the remainder of the block heading into the PM peak period.

 

With the use of automatic blocking software, it has become increasingly important that scheduled departure times and run times for supplemental trips reflect the reality of what is being operated. To assess supplemental bus trips, CTPS used fall 2015 AVL data provided by the MBTA. Summaries of origin departure lateness, end-point arrival lateness, and run times for supplemental trips based on the fall 2015 AVL observations are provided in Appendix B along with a summary of suggested scheduling adjustments. The summaries provided in Appendix B are not intended to reflect the current state of these supplemental trips, as the MBTA makes constant schedule adjustments, but rather to highlight the importance of closely monitoring them.

 

3.5      Bus Operator Swing-Ons

A bus operator swing-on occurs when there is a scheduled change in operators while a bus is in service, usually at the start or end point of a trip. In general, the operator stepping off the bus ends their assignment while the other operator starts theirs. A swing-on provides the benefit of reducing the total number of non-revenue vehicle-hours when compared to the alternative option of pulling a vehicle in and out of service to change operators at the garage. The potential burdens of scheduling swing-ons include the extra time it takes for an operator switch to occur and delays caused by operators showing up late to the swing-on location. In addition, swing-ons increase the number of trips in a row a vehicle is scheduled to complete, which can perpetuate delay that occurs early on in the block. For blocks containing trips with poor on-time performance, a scheduled trip back to the garage for an operator change may be utilized as a layover to recover from delay that occurred on previous trips.

 

CTPS used fall 2015 AVL data provided by the MBTA to assess swing-on delay under two scenarios, as outlined in Figure 6.

 

Figure 6
Procedure for Assessing Swing-On Delay

Figure 6 is a non-accessible graphical representation. Figure 6 assesses swing-on delay under two scenarios using fall 2015 AVL data provided by the MBTA. Scenario A reflects a situation where the bus arrives for its swing-on before its scheduled departure time. Scenario B reflects a situation where the bus arrives for its swing-on after its scheduled departure time.

 

As shown in the figure above, scenario A reflects a situation where the bus arrives for its swing-on before its scheduled departure time. Swing-on delay for a trip under this condition is assessed as the amount of time that passes from three minutes past the scheduled departure time to when the trip departs. Three minutes past a trip’s scheduled departure time was selected as a threshold because it is similar to the MBTA’s definition of a late departing trip.

 

Scenario B reflects a situation where the bus arrives for its swing-on after its scheduled departure time. Swing-on delay that occurs under this condition is assessed as the amount of time between arrival and departure that is in excess of three minutes, under the assumption that three minutes is a reasonable amount of time for an operator switch to be completed and all passengers to be loaded. The frequency at which scenario B occurs is important, as a trip where scenario B occurs frequently might benefit from a schedule adjustment that assigns the operator change back at the garage with a hook to a different trip or deadhead movement in order to minimize delay in the system.

 

Appendix C provides swing-on delay summaries by route, start-point location, and time of day. In the fall 2015 schedule, there were 720 scheduled swing-ons each weekday.

 

In scenario A, 28,077 AVL observations were recorded, with total assessed swing-on delay of 414 hours (53 seconds of delay per swing-on). In scenario B, 9,077 AVL observations were recorded, with total assessed swing-on delay of 195 hours (77 seconds of delay per swing-on) in addition to any amount of delay from a late arrival. Scenarios A and B combined yield an average of 59 seconds of delay per swing-on. Scenario B accounted for 24.4 percent of all observations (that is, at least 24.4 percent of swing-ons begin after their scheduled departure time). Staff recommend that the MBTA review specific trips, locations, and times of day where scenario B is a common occurrence.

 

3.6      Early Pull-Up Opportunity

CTPS was tasked with assessing the amount of time that departure lateness at origins could be reduced if operators who have time to lay over are held to a higher standard of on-time departure performance by pulling up early to load passengers so that all passengers are boarded no later than the scheduled departure time.

 

CTPS used fall 2015 AVL data and fall 2015 APC data provided by the MBTA to assess early pull-up opportunity for four high-frequency routes at four locations; Route 28 at Ruggles, Route 32 at Forest Hills, Route 57 at Kenmore, and Route 111 at Haymarket. Early pull-up opportunity is categorized by the four scenarios (A, B, C, and D) outlined in Figure 7. An arrival time at the terminal was assigned using the AVL data, and door open and door close time was assigned using the APC data. Layover time was assessed as the time between buses’ arrival at the terminal (using AVL data) and doors opening (using APC data). Dwell time was assessed as the time between the doors opening and doors closing (using APC data).

 

Scenario A occurs when the bus arrives at the origin after its scheduled departure time. Because the bus arrives late, there is no opportunity to achieve any time savings by cutting the layover short, and the missed opportunity for time savings is zero. Departure lateness is equal to the time when the doors close minus the scheduled departure time.

 

Scenario B occurs when the bus arrives at the origin before its scheduled departure time, but the doors do not open until after the scheduled departure time. The missed opportunity for time savings is equal to the time when the doors close minus the scheduled departure time, or the time when the doors open minus the time when the bus arrives, whichever is least. Departure lateness is equal to the time when the doors close minus the scheduled departure time.

 

Scenario C occurs when the bus arrives at the origin and opens its doors before its scheduled departure time, but the doors do not close until after the scheduled departure time. As with scenario B, the missed opportunity for time savings is equal to the time when the doors close minus the scheduled departure time, or the time when the doors open minus the time when the bus arrives, whichever is least. Departure lateness is equal to the time when the doors close minus the scheduled departure time.

 

Scenario D is the preferred scenario, where the operator has pulled up early enough to load all passengers before the scheduled departure time. All opportunity for time savings has been utilized, and there is zero departure lateness.

 

Figure 7
Procedure for Assessing Early Pull-Up Opportunity

Figure 7 is a non-accessible graphical representation. It categorizes early pull-up opportunity according to four scenarios: Scenario A occurs when the bus arrives at the origin after its scheduled departure time. Scenario B occurs when the bus arrives at the origin before its scheduled departure time, but the doors do not open until after the scheduled departure time. Scenario C occurs when the bus arrives at the origin and opens its doors before its scheduled departure time, but the doors do not close until after the scheduled departure time. Scenario D is the preferred scenario, where the operator has pulled up early enough to load all passengers before the scheduled departure time.

 

Table 14 provides a summary of early pull-up opportunity for each route; Table 15 provides a summary of all observations combined; and Appendix D provides a summary by trip. The number of observations for each route is limited by the number of APC observations, as the percentage of buses equipped with APC devices varies by garage.

 

Table 14
Summary of Early Pull-Up Opportunity by Route

Route - Location

Scenario

Number of Obs.

Total Departure Lateness (min.)

Total Missed Opportunity for Savings (min.)

Departure Lateness per Trip (min.)

Missed Opportunity for Savings per Trip (min.)

28 - Ruggles

A

1,015

8,644

N/A

8.5

N/A

28

B

110

259

134

2.4

1.2

28

C

744

1,369

336

1.8

0.5

28

D

1,137

N/A

N/A

N/A

N/A

28

Total

3,006

10,271

469

3.4

0.2

32 - Forest Hills

A

116

1,190

N/A

10.3

N/A

32

B

25

70

23

2.8

0.9

32

C

122

246

68

2.0

0.6

32

D

90

N/A

N/A

N/A

N/A

32

Total

353

1,506

91

4.3

0.3

57 - Kenmore

A

86

698

N/A

8.1

N/A

57

B

8

38

28

4.7

3.5

57

C

190

277

51

1.5

0.3

57

D

246

N/A

N/A

N/A

N/A

57

Total

530

1,013

79

1.9

0.1

111 - Haymarket

A

943

10,132

N/A

10.7

N/A

111

B

96

404

176

4.2

1.8

111

C

955

1,842

626

1.9

0.7

111

D

723

N/A

N/A

N/A

N/A

111

Total

2,717

12,378

802

4.6

0.3

 

 

Table 15
Summary of Early Pull-Up Opportunity—All Observations

Scenario

Number of Obs.

Total Departure Lateness (min.)

Total Missed Opportunity for Savings (min.)

Departure Lateness per Trip (min.)

Missed Opportunity for Savings per Trip (min.)

A

2,160

20,664

N/A

9.6

N/A

B

239

770

360

3.2

1.5

C

2,011

3,734

1,081

1.9

0.5

D

2,196

N/A

N/A

N/A

N/A

Total

6,606

25,168

1,441

3.8

0.2


 

Staff formed 6,606 observations from AVL and APC data for the four routes combined. Of these observations, 33.7 percent were categorized under scenario A, where the bus arrived at its origin after its scheduled departure time, leaving no opportunity to achieve any time savings by reducing the layover. Staff categorized 33.2 percent of all observations under scenario D, where the bus closed its doors before the scheduled departure time, utilizing all opportunity for time savings.

 

The remaining 33.1 percent of observations were categorized under scenarios B and C, where a potential for time savings exists. For these 2,250 observations, there were 4,504 minutes of departure lateness (2.0 minutes per trip) and 1,441 minutes of missed opportunity for savings (0.6 minutes per trip). However, the amount of missed opportunity for time savings is likely less than reported, as this methodology did not account for any required travel time between the location of the AVL time point and the passenger pickup location.

 

A comparison of the total amount of departure lateness (25,169 minutes) to the total missed opportunity for savings (1,441 minutes) suggests that earlier pull-ups at origins would not contribute to a significant reduction in departure lateness along these routes.

 

 

Appendixes