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Behind the scenes with MBTA data.

To use the MBTA, passengers typically have to walk, drive, or otherwise travel between our stations and their homes, offices, and schools. The question of how passengers travel between stations and their ultimate origin or destination is called the “last mile problem.” Typically, when the MBTA tries to answer questions involving the last mile problem (e.g., determining how many jobs are within walking distance of T stations), we assume that passengers won’t walk more than half a mile. However, studies of walking distances of different subway networks have found that walk distances vary considerably from station to station. In this blog post, we are going to explore how walk distances may vary from station to station in our MBTA network. 

For this post, we’re using survey answers from our most recent Rider Census, where passengers were asked to provide information about their most recent trip on the MBTA, including the location of their origin and destination. This provides us an opportunity to calculate how far passengers walk between their ultimate origins and destinations and MBTA stations. For each rail station and Silver Line station, as well as for each bus line, we used bootstrapping to calculate a confidence interval for the average distance passengers walk to and from MBTA stops. We then focused our analysis on the Red and Orange Lines, and identified three interesting trends: passengers walked longer distances to reach stations at the ends of the Red and Orange Lines, passengers walked shorter distances to stations constrained by bodies of water, and passengers walked shorter distances to stations in the middle of the Orange Line. 

Methodology and Data Sources

As mentioned, the MBTA and CTPS recently conducted a systemwide passenger survey. For the survey, we asked passengers about their most recent trip on the MBTA. The survey asked passengers to list their origin and destination locations—where they are coming from before arriving at a MBTA stop/station and where they are traveling to after completing their trip on the MBTA. They were able to classify these locations in a variety of ways, like home, workplace, school, etc. The survey then asked passengers to list their mode of travel (driving, walking, biking, or use of a non-MBTA service) when going to and from the T in order to learn more about this “last mile.” Passengers listed the specific MBTA service they used (e.g. Green Line, bus route 7, Fitchburg Line, etc.) and at what specific stops they boarded and alighted. Passengers also provided basic demographic information.

Not every respondent provided an origin or destination location, so we separated the dataset into two groups: responses that included an origin location, and responses that included a destination location. (Responses that included both an origin and destination location were counted twice.) Since we are investigating walkability, we filtered the datasets so that they only contained responses from passengers who identified their access and egress modes as walking. This left 15,934 responses from passengers who identified an origin location and walked to their first MBTA boarding and 18,161 responses from passengers who identified a destination location and walked from their last MBTA alighting.

For each of the responses, we calculated the walk distance by calculating the straight line distance in meters from origin and destination locations to the location where they boarded or exited their first or last MBTA service experience. In cases where passengers were using rail or Silver Line service, the survey identified the exact stop at which passengers boarded and exited the service. However, in cases where passengers were using bus service, the survey did not identify the exact stop at which passengers boarded and exited; the survey only identified the bus line that passengers took. Therefore, we assumed that bus passengers would walk to the bus stop closest to their origin or destination location, and used the bus stop nearest to the passenger’s origin or destination location to calculate the walk distance.

Finally, we filtered out stops and bus lines that had less than thirty data points. The Green and Blue Lines did not have a lot of stations with more than thirty data points, whereas the Red and Orange Line stations all had more than thirty data points each . Therefore, we decided to focus on the Red and Orange Lines for the purposes of this blog post. We mapped the mean and median walk distances for the Red and Orange Lines in QGIS (we did not map the walk distances for Downtown Crossing, as that station is shared by the Red and the Orange Line).

Possible limitations of the data include:

  • The number of responses for each station and line are not proportional to the ridership of the respective stations/lines.
  • Women, English speakers, and regular MBTA passengers were more likely to respond to the survey.
  • Because passengers were asked to describe their most recent trip, the survey responses were often biased towards trips taken in the morning.

Results*

Line Station Number Datapoints Mean Walk Distance Mean Lower CI Mean Upper CI Median Walk Distance Median Lower CI Median Upper CI
Orange Line Assembly Station 57 513.657 437.840 587.424 320.230 200.572 320.230
Orange Line  Back Bay  388  497.198 359.258 603.427 331.830 311.550 333.988
Orange Line  Downtown Crossing  387  462.723 366.399 545.144 301.413 286.933 330.088
Orange Line  Forest Hills  119  712.659 578.353 839.518 429.573 325.896 507.278
Orange Line  Malden Center  151  718.944 527.984 852.199 561.643 535.360 579.449
Orange Line Mass Ave  221  466.500 382.312 532.923 261.808 199.563 261.808
Orange Line North Station  303  558.156 299.258 709.093 272.793 234.126 272.793
Orange Line Oak Grove 77  1142.579 778.101 1446.468 716.906 647.668 904.608
Orange Line Sullivan Square 90 674.677 547.066 786.468 432.475 334.869 433.871
Red Line Alewife 188 832.947 647.254 980.625 658.636 558.152 727.017
Red Line Charles MGH 934 261.047 238.093 280.972 175.378 140.958 175.378
Red Line Davis Square 462 787.405 493.758 961.490 585.864 544.225 650.085
Red Line Downtown Crossing 501 526.546 412.159 621.614 306.902 290.354 339.374
Red Line Kendall Square 1218 421.666 400.187 441.639 315.829 315.829 315.829
Red Line South Station 684 424.869 354.815 484.151 264.058 219.488 282.041

Key: 

Confidence Interval = CI

* Click the link to view the above table with more stations listed.An image of the mean walk distances for the Red and Orange Lines.

An image of the median walk distances for the Red and Orange Lines.

Conclusions

There are a number of interesting conclusions that can be drawn from the mean and median walk distances from each station. We have tried classifying them into a few main trends as explained below:

Physical Landscape — Safety & Geography

The Charles MGH station is notable for having a substantially lower median and mean walk distance compared to the other Red Line stations. There are a few possible explanations for this. First, the built environment of Charles MGH is particularly inconvenient to pedestrians: the station has only two crosswalks, two entrances, and is surrounded by busy roads. Pedestrians are also constrained by two geographic features--Beacon Hill (the hill, not the neighborhood) and the Charles River—which could limit how far pedestrians are able to walk to reach Charles MGH. Kendall Square, which has the fourth lowest mean walking distance out of all Red Line stations, also is adjacent to the Charles River, which provides further evidence for bodies of water like the Charles River affecting station walkability. A similar effect can be seen at Assembly Station on the Orange Line, which is surrounded by the Mystic River and Interstate 93, and has a lower average and median walkshed than the adjacent stations (Sullivan Square and Malden).

Last Stations on Subway Lines

Stations at the ends of the Red and Orange Lines—Alewife, Davis, Forest Hills, Malden, and Oak Grove—tend to have larger average and median walk distances. This is probably because passengers who live beyond the reach of the Red and Orange Lines prefer the Red and Orange Lines to alternative MBTA services (the bus network and the commuter rail), and are willing to walk further distances to reach the Red and Orange Lines. 

Competition

The stations at the center of the Orange Line—beginning at around Mass Ave and ending at around North Station—tend to have lower medians and means compared to other Orange Line stations. There are a few possible explanations for this. First, this section of the Orange Line is not only very close to the E branch of the Green line, but also runs parallel to it. This means that passengers can choose between the E branch and the Orange line, and it’s likely that one of the factors that goes into that decision is which line has the closest stations, so passengers are likely minimizing their walking distances during that section of the Orange Line. Another factor is that Orange Line stations in the center of the Orange Line are particularly close together, which could affect how far passengers need to walk to reach an Orange Line station.

Transportation is responsible for a significant chunk of the carbon emissions that is causing climate change. The IPCC has found that approximately one-quarter of global CO2 emissions in 2014 were from the transportation sector, and that this sector has seen faster emissions growth than any other. Public transit is one of many solutions that can help us reduce our collective transportation emissions. Trains and buses lower emissions because they can efficiently move many people at once. Additionally, the more priority (bus lanes, transit signal priority, etc.) that we are able to give buses in particular, the larger the emissions savings and the better the experience for our riders. 

The MBTA, in partnership with policy makers, municipalities, and businesses, has a very important role to play in making it easier for people to make sustainable transportation choices. One of the ways that the T works to get people out of single occupancy vehicles and onto trains and buses is through our Perq program, formerly known as the Corporate Pass Program. Through Perq, employers can offer pretax or subsidized monthly passes to their employees. Perq is a way for employers to incentivize their employees’ to replace vehicle trips with transit for their daily commute. According to AASHTO, work trips make up 19% of all person miles traveled in the U.S. However, access to a subsidized transit pass increases your likelihood of taking transit for other, non-commute trips as well. Employees and employers have pointed out that the “ease-of-use” aspect of employer-provided MBTA passes, in addition to the cost savings, also increase the likelihood that employees will use transit to commute. 

We know that mass transit has a lower carbon footprint than driving in most situations, and having convenient access to an MBTA pass can make the choice between driving and taking transit a little bit simpler. But, just how big of an effect can employer-provided transit passes actually have on emissions? To try to answer to this question, we partnered with a large education company based in Cambridge that participates in the Perq program. We looked into their employees’ transit patterns in aggregate to try to estimate just how many emissions they are potentially offsetting. 

What trips are employees making in the first place?

In order to know what emissions are being saved (or generated) through this company’s transit pass program, we first have to know which trips employees are actually making. How are they traveling? On which modes? Trip lengths and mode choices can significantly change the environmental benefits of transit.

To begin, we identified all Perq CharlieCards in use by employees in September of 2018, the last month for which we have complete, processed trip data. We ran those card numbers through our ODX model in order to identify every trip (an origin station and a destination station) that was taken that month. Our final dataset included each unique origin-destination (OD) pair, how many times that trip was taken, and whether the primary mode used was subway or bus.  

This analysis is structured to protect riders' privacy by keeping trips anonymous – our final model contains only OD pairs and modes and removes any personal CharlieCard information. 

We ran each OD pair identified by the ODX model through the Google Distance Matrix API in order to get the distance, in miles, for each trip if it was taken using transit, and then again for the driving equivalent. For example, the most common trip taken in this dataset was from Community College to Oak Grove on the Orange Line. This trip is approximately 4.6 miles on the train, but nearly double that (8.7 miles) when driving. With the ODX and API data, we are able to calculate the total number of passenger miles taken on bus, subway, and equivalent driving trips. 

The Perq program, however, does not just facilitate bus and subway trips. It also allows employers to provide their employees with Commuter Rail passes. Because the Commuter Rail does not use any automated fare collection systems, the only information that we have about how employees used Commuter Rail is how many passes were purchased for each zone. There is no way to identify particular stations, particular lines, or trip frequencies for these employees. Additionally, zone passes are not assigned specific employees in our data, so this analysis is inherently anonymous.

We do know more generally how Commuter Rail riders behave, through a variety of surveys, including a monthly panel surveys and biannual Keolis passenger surveys. While it can be difficult to relate reported behavior and actual behavior, we made assumptions about the average behavior of a Commuter Rail rider, and that employees who choose to commute on the Commuter Rail behave similarly. The vast majority of trips taken on the Commuter Rail are in the peak direction and include the terminal station of the line (either North Station or South Station). This is likely particularly true for employees given the location of their office near North Station. In addition, Commuter Rail riders that use the service for work, tend not to use transit for other trip purposes. 

In order to estimate trip distances for employees that ride Commuter Rail, we found the average transit and driving distances for all stations within a zone to their respective terminal stations. Driving mileage was calculated using the Google API, whereas we used track distances to determine the transit mileage. Take for example, the Zone 7 stations in the table below. We identified the transit and driving distances between each Zone 7 station and the terminal station on its line. From those distances, we determined the average transit and driving mileage from a Zone 7 station to downtown Boston. There were two employees who received Zone 7 Commuter Rail passes through Perq, and so we assumed that their trip lengths were equal to the zone average. 

Stop Name Terminal Station Transit Distance (Miles) Driving Distance (Miles)
Bradford North Station 32.5 37.4
Gloucester North Station 31.6 36.6
Haverhill North Station 32.9 37.1
Littleton/Rt 495 North Station 30.1 36.1
Rowley North Station 31.2 35.8
West Gloucester North Station 29.6 34.9
Attleboro South Station 31.8 39.4
Halifax South Station 28.1 35.4
South Attleboro South Station 36.8 44.5
Westborough South Station 34.0 36.0
       
  ZONE 3 AVERAGE 31.9 37.3

For these trips, we assumed that employees, on average, took the commuter rail every workday except one (in September 2018 that translates to 18 round trips). 

By the end of this process, we have calculated: 

1) total passenger miles on buses, 

2) total passenger miles on subway, 

3) total passenger miles on Commuter Rail, and 

4) total passenger miles driven for the equivalent of all transit trips. 

Total Passenger Miles, by mode  

Mode Total Estimated Passenger Miles, September 2018
Transit 35,231
Bus 4,718
Subway 20,069
Commuter Rail 10,444
Driving 47,892

How do those trips translate to emissions?

Estimating carbon emissions that result from different modes of transportation is a difficult process because there are so many confounding factors. Not only does the mode matter, but the age of a vehicle, the speed at which it is traveling, the condition of the road or track, and so much more can impact the emissions released. 

Thankfully, the Massachusetts Department of Environmental Protection has done a lot of this thinking already. MassDEP developed a carbon emissions calculator that takes into account the unique transportation landscape in Massachusetts to estimate emissions factors for each mode. The tool is slightly out of date, with some of the data being sourced from 2012. We are working to update the tool and come up with more accurate emissions factors, but as of now, the calculator is the most reliable source specific to our region. 

To get total carbon emissions, we ran the total estimated passenger miles by mode through the calculator. 

Results

After running the DEP calculator, we can compare multiple scenarios to see just how many emissions the Perq program is helping to offset. 

Spoiler Alert: Companies who encourage transit use can save A LOT of carbon emissions.  

The number we are relatively sure of is just how many passenger miles were ridden on transit, which the DEP calculator reports as having generated 9,810 pounds of CO2 in September of 2018. What we are less sure of is just how many of those trips replaced car trips. If we assume that every transit trip replaced a driving trip, we could estimate that 41,705 pounds of CO2 would have been generated by driving — this means that riders who took transit reduced their emissions by up to 76% 

Realistically, we know that not all transit trips replaced car trips. Some were likely biked, walked, already taken on transit, or not taken at all, meaning that these trips were either net neutral or actually generated emissions. Without a company specific travel survey, it is difficult to know exactly how employees behaved before the Perq pass, so we had to make some assumptions. To create a low bound estimate, we took MassDOT’s definition of a “bikeable distance,” and assumed that for every trip under six miles, the Perq program actually generated emissions. In this case, the comparison point for driving emissions is 24,977 pounds of CO2. Even in this fairly absurd low bound scenario (Was everyone who was traveling six miles or less biking or walking? Probably not.), riders who took transit still saw an overall emissions reduction of 60%. In the table below, you can see the amount of CO2 generated by driving in a variety of scenarios. 

Scenario Lbs of CO2 from driving
All trips would have happened in a car 41,705
All trips over one mile would have happened in a car 41,453
All trips over one and a half miles would have happened in a car 40,882
All trips over two miles would have happened in a car 39,970
All trips over four miles would have happened in a car 31,476
All trips over six miles would have happened in a car 24,977

What is a pound of CO2 after all? Are these savings very big? Try putting in some of these values into the EPA emissions equivalencies calculator below. Remember that these savings are for just one company in one month – the Perq program works with approximately 1,500 companies and is constantly growing. 

Conclusion

Transportation is the biggest contributor to carbon emissions in the state of Massachusetts, contributing 43% of the state’s total emissions, a share higher than the US and global averages. Replacing as many single occupancy vehicle trips by transit or active modes is one of the most effective ways we can reduce our carbon emissions. Employers have an important role to play in reducing the carbon footprint of commuters, and many who currently partner with the MBTA are thinking of innovative solutions to do just that. More information about the Perq program – one of many solutions for employers looking to lower their carbon footprint – is available here

 

 

The MBTA is excited to be taking many steps to improve our bus services. You can read about the process used to analyze the changes proposed by the Better Bus Project in our previous post. We're also in the beginning stages of re-designing our overall bus network. And of course, we're continuing our work to partner with cities and towns to implement street-level changes in order to prioritize the movement of buses.

Over the last two+ years, the MBTA, in partnership with cities, towns and other stakeholders, has begun piloting and implementing bus-only lanes on some of our key corridors. We will continue to partner to improve bus service by implementing more bus-only lanes as well as other interventions like queue jumps and transit signal priority.

These lanes take various forms: some are operational only during peak times, some are all-day, and they are different lengths and affect different areas. Here at the blog, we will be presenting some analysis on these lanes and all our bus priority interventions as data becomes available. Read below for an overview of the bus-only lanes we’ve introduced so far. To get an in-depth look at some of the rationale for why these lanes are so important, take a look through our bus ridership report!

The basic idea behind the bus lanes is that there are 40 or more people on a bus during peak times, and usually just one in a private vehicle, so it makes sense according to simple geometry to prioritize the travel of buses. In many cases, these lanes have taken the place of a parking lane which was only lightly used. When you separate buses from mixed traffic, you can both improve the speed of bus travel along the corridor and decrease the variability of run times, both of which make taking the bus a more competitive option with driving, and over time, you can not only improve the experience for passengers but also attract more passengers to the bus. We’ll take a look at how well these interventions have met these goals below.

1. Broadway Bus Lane, Everett

The Broadway Bus Lane in Everett operates in the inbound direction for about one mile from 4AM to 9AM, Monday through Friday. It has been in operation since December 5, 2016, though various improvements have been made since then to improve the operation of the lane. There are 5 bus routes that serve the 11 stops within the bus lane. Route 109 serves the entirety of the corridor, while routes 97, 104, 110, and 112 join the corridor at various points along the way (Figure 1). More information about the project is available on the MassDOT website.

Figure 1: Routes serving the Broadway Bus Lane in Everett

Figure 1: Routes serving the Broadway Bus Lane in Everett

1.1 Run Time Changes

To evaluate changes in run time, we used data from the MBTA’s automated passenger counter (APC) system. APCs collect information about when the doors open and close at each stop and the number of passengers boarding or alighting. Dwell times, defined here as the time in between when the doors are opened and closed at each stop, are impacted by the number of passengers boarding and alighting, whether they pay in cash or prepaid fare media, the presence of wheelchairs and strollers, and other factors. In order to focus on run times (since other programs like AFC 2.0 are intended to help reduce dwell times), the run time analysis looked at the run time between when the doors close at one stop until they open at the next stop. 

To calculate the change in run time from the bus lane, we used data from weekdays between September 1 and November 15 in 2016 and 2018. The data from 2016 represent the before condition, while 2018 represents the after condition. It is important to compare times from similar seasons, because traffic patterns vary throughout the year due to school schedules, holidays, and weather. Fall is a good time to use for comparison because ridership and traffic are generally at their highest annual levels, and travel patterns are not generally impacted by holidays and/or bad weather. 

Because various routes enter the corridor at different points, we began by looking at route 109, the only route that covers the entire corridor. We first calculated the run time for each trip in the corridor by excluding the dwell time. Next, we grouped these observations by hour and calculated the median and 90th percentile run times by hour for the corridor. The median value represents the “normal” run time for each hour of the day, while the 90th percentile is useful for MBTA operations, because each trip’s scheduled run time and recovery time is based on the 90th percentile run time. Reductions at the 90th percentile run time will have an outsized impact in vehicle reliability and the frequency at which the MBTA can operate buses.  

These results are shown in Figure 2 and Figure 3 below for the median and 90th percentile, respectively. We collected data throughout the day, but these figures show the results for peak hours when the greatest numbers of buses are in service and sufficient data was collected. While there was a clear reduction in run time between 2016 and 2018 between 5 and 9 AM, there was no discernible change during the 9-10 AM hour and the PM peak hours when the bus lane is not in operation. The savings were particularly strong from 7-8 AM, when buses saved almost 8 minutes at median and almost 11 minutes at the 90th percentile. Other routes on the corridor realized a smaller portion of the savings because they utilize a smaller portion of the bus lane. In the next section, this is considered in greater detail. 

Figure 2: Median corridor run time by hour

Figure 2: Median corridor run time by hour

Figure 3: 90th percentile corridor run time by hour

Figure 3: 90th percentile corridor run time by hour

1.2 Passenger Time Savings

After determining the run time savings, we then looked at passenger time savings, which is a function of both time saved and the number of passengers on the bus. The steps in the calculation are as follows:

  1. Calculate the median and 90th percentile run time by hour for each stop-to-stop pair in 2016 and 2018, using the methods described above. The time savings for each hour is the difference between 2016 and 2018.
  2. Calculate the average stop time and passenger load of each trip leaving each stop in 2018.
  3. Multiply the time savings times the load for each trip-stop and aggregate. 

In Fall 2018, on the median weekday morning, passengers saved 24 hours of travel time. On the 90th percentile "bad" day, passengers saved 65 hours altogether. The results by hour are shown in Figure 4 below. Passengers on routes 104 and 109 accounted for more than two-thirds of the total savings due to higher ridership and more utilization of the bus lane corridor on those routes. 

Figure 4: Passenger-hours saved per day

Figure 4: Passenger-hours saved per day

2. Mt. Auburn Street

The Mt. Auburn St. Bus Lane in Cambridge and Watertown operates in the inbound direction 24 hours a day, 7 days a week. It began operation on October 15, 2018, though there were various implementation and signal issues at first. By November 15, 2018 it began its normal operation. Routes 71 and 73 serve the bus lane. There are short bus-only lanes on Belmont Street and Mt. Auburn Street, which are served by the 73 and 71, respectively, just before the routes join together at Belmont Street at Mt. Auburn Street, as well as installed signal priority and queue jumps. From there, the bus lane continues east to Fresh Pond Parkway, though it is not continuous in throughout the corridor. More information about the project is available on the City of Cambridge’s website.

Figure 5: Map of the Mt. Auburn Bus Lane in Cambridge and Watertown

Figure 5: Map of the Mt. Auburn Bus Lane in Cambridge and Watertown

2.1 Run Time Changes

Routes 71 and 73 are served by Electric Trolley Buses (ETBs), which were the last portion of MBTA’s bus fleet to not have any vehicles equipped with Automated Passenger Counters (APCs). In July 2018, 5 of the 28 ETBs were equipped with APCs, and it took several months after installation before data was regularly collected due to configuration issues. As a result, we did not collect enough data before the bus lane installation to conduct a thorough year-over-year analysis.  The automated vehicle locator (AVL) system data was used as an alternative. It is less granular than APC data, so it is less useful for drilling down into bus lane performance, but all vehicle are tracked by AVL, offering comprehensive coverage.

To calculate the change in run time from the bus lane, we used data from weekdays between January 1 and March 31 in 2018 and 2019. The data from 2018 represent the before condition, while 2019 represents the after condition. Again, it is important to compare times from similar seasons, because traffic patterns vary throughout the year due to school schedules, holidays, and weather. Although data from Fall is preferable, the implementation and refinement of the bus lane occurred in various stages throughout October and November, making Fall 2018 a poor time for comparison. 

We then calculated the run time for the route segment between the intersection of Mt. Auburn and Belmont St and the eastern side of Mt. Auburn Hospital. This segment encompasses all of the bus lane shared by routes 71 and 73, and omits only the separate portions on Belmont St and Mt. Auburn St. before the intersection. Next, we grouped these observations by half-hour and calculated the median and 90th percentile run times by half-hour in both directions. The median value represents the “normal” run time for each hour of the day, while the 90th captures what a typical “bad day” is like. Even if a day like this only happens 1 out of 10 times, bus riders likely need to plan for the full range of possible travel times when planning their trips. Similarly, the 90th percentile is useful for MBTA operations, because each trip’s scheduled run time and recovery time is based on the 90th percentile run time. Reductions at the 90th percentile run time will have an outsized impact in vehicle reliability and the frequency at which the MBTA can operate buses.  

The inbound and outbound results are shown in Figure 6 and Figure 7 below. In the inbound direction, buses save 3-4 minutes at median and 5-8 minutes at the 90th percentile during the busiest time of the AM peak, and 0.5 to 2 minutes throughout the rest of the day. At the 90th percentile, this represents an 8-12% reduction of the maximum cycle time during the AM peak period. Despite there not being any bus lane in the outbound direction, run times were consistently shorter throughout the day, with buses saving 0.5-1.5 minutes. This is likely due in part to changes in signal timing that have helped move all vehicles through the project area. 

Figure 6: Year-over-year change in inbound segment run times on Mt. Auburn St.

Figure 6: Year-over-year change in inbound segment run times on Mt. Auburn St.

Figure 7: Year-over-year change in outbound segment run times on Mt. Auburn St.

Figure 7: Year-over-year change in outbound segment run times on Mt. Auburn St.

2.2 Next Step

Next, we will work to update these calculations as more seasonal data is collected. We also plan to estimate the savings in terms of passenger hours saved by the bus lane, though as discussed above there are some data issues that complicate this calculation. 

3. Washington Street, Roslindale

The Washington Street bus lane operates in the inbound direction from Roslindale Village to the Forest Hills MBTA Station, a distance of about one mile, from 5AM-9AM Monday – Friday. After a pilot period in May 2018, permanent operation began on June 18, 2018. Nine MBTA bus routes—30, 34, 34E, 35, 36, 37, 40, 50, and 51—operate in the corridor. More information about the project is available on the City of Boston’s website.

3.1 Run Time Changes

To evaluate changes in run time, we again used data from MBTA’s automated passenger counter (APC) system. As previously explained, APCs collect information about when the doors open and close at each stop and the number of passengers boarding or alighting. This number of passengers boarding and alighting directly affects dwell times. Therefore, in order to focus on run times (since other programs like AFC 2.0 are intended to help reduce dwell times), the run time analysis looked at the run time between when the doors close at one stop until they open at then next stop—excluding dwell times. 

Unlike the bus lanes discussed above, the Washington Street bus lane presents the ideal conditions to evaluate the bus with APC data. All buses that serve the corridor are equipped with APCs, providing a rich source of data.

To calculate the change in run time from the bus lane, we used data from weekdays between January 1 and March 15 in 2018 and 2019. The data from 2018 represent the before condition, while 2019 represents the after condition. Although data from the Fall is preferable, Fall 2017 could not be used because bus travel times were greatly impacted by road construction around Forest Hills, making it a poor choice for comparison. 

We first calculated the run time for each trip in the corridor by excluding the dwell time. Next, we grouped these observations by half-hour and calculated the median and 90th percentile run times by half-hour for the corridor. The median value represents the “normal” run time for each half-hour of the day, while the 90th percentile is useful for MBTA operations, because each trip’s scheduled run time and recovery time is based on the 90th percentile run time. Reductions at the 90th percentile run time will have an outsized impact in vehicle reliability and the frequency at which the MBTA can operate buses.  

These results are shown in Figure 8 below. There was a clear reduction in run time between 6AM and 9AM, when the bus lane is in operation, while times were very similar during the rest of the day. Buses save 2 minutes at median and 5-7 minutes at the 90th percentile during the busiest time of the AM peak. In the next section, we test how this impacted passenger and total ridership.

Figure 8: Year-over-year change in inbound segment run times on Washington Street

Figure 8: Year-over-year change in inbound segment run times on Washington Street

3.2 Passenger-weighted Savings

After determining the run time savings, we then looked at passenger time savings, which is a function of both time saved and the number of passengers on the bus. The steps in the calculation are as follows:

  1. Calculate the median and 90th percentile run time by hour for each stop-to-stop pair in 2018 and 2019, using the methods described above. The time savings for each hour is the difference between 2018 and 2019.
  2. Calculate the average stop time and passenger load of each trip leaving each stop in 2019.
  3. Multiply the time savings times the load for each trip-stop and aggregate. 

Using this method, the incremental and cumulative savings in passenger travel times is shown in Figure 9 below. In total, MBTA riders save 41 total hours of travel time at the median and 176 hours at the 90th percentile due to the Washington St bus lane per weekday. It’s possible that some of the savings calculated here may be due in part to lingering construction impacts prior to the start of the bus lane, and we will continue to evaluate year-over-year changes to confirm these estimates. 

Figure 9: Reduction in passenger-hours of travel times due to decreased run times on Washington Street

Figure 9: Reduction in passenger-hours of travel times due to decreased run times on Washington Street

3.3 Changes in Ridership

Finally, we looked at ridership in the corridor to determine whether there was any year-over-year change in ridership. During the hours of 5AM – 9AM, we found a 4% increase in ridership between Fall 2017 (3,181 arrivals at Forest Hills) and Fall 2018 (3,300 arrivals at Forest Hills). We found a similar increase of 4% between Winter 2018 (2,911 arrivals at Forest Hills) and Winter 2019 (3,034 arrivals at Forest Hills). Because there are many external factors that may impact ridership, changes in ridership cannot be solely attributed to the bus lane. However, we will continue to monitor ridership in the corridor.

3.4 Next Steps

Next, we will work to update these calculations as more seasonal data is collected. We will also evaluate the other bus lanes that are newly installed or planned for the future.