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Behind the scenes with MBTA data.

Update 3/19:

The MBTA is actively monitoring rider volume and feedback, and making service adjustments accordingly to meet the needs of our community. As this situation evolves, we will continue to focus on providing workforce access to hospitals & food distribution locations. 

“As we continue monitoring the situation, we are making adjustments to ensure we can provide service for essential trips in the safest manner possible,” said MBTA General Manager Steve Poftak. “We recognize that some employees in key industries, including those in the medical community, rely on the MBTA to get to their places of work and we’re committed to providing service to those folks who rely on the T. That said, service continues to operate at reduced levels in an effort to maintain a responsible balance between protecting our workforce and operating safe service.”

In order to make service adjustments and to inform future scenario planning, we are analyzing ridership data at the subway line and bus route level. The beginning of this post has been updated to include ridership data from the week of 3/16. The remainder of the post has been left as originally written with data through 3/13/20.

Gated Stations

Ridership continued to drop on Monday 3/16 and Tuesday, 3/17, before settling somewhat on Wednesday 3/18. Overall, at gated stations on Monday, we recorded 68% fewer validations than we did in the comparison week of 2/24/20, and on Tuesday, 78% fewer. Wednesday recorded 80% fewer than our comparison week.

You may download the daily total validations at the following links. Please note that these are gated stations only, with no adjustment for anyone who may have transferred behind the gate or entered a gate without tapping a card or validating a ticket. The most recent data is preliminary and may be adjusted as additional data comes in. 

Validations at Gated Stations by Station (csv)

Validations at Gated Stations by Line (csv)

Bus

Bus ridership has dropped by roughly 50% overall from the baseline week of 2/24 to Monday, 3/16. Similar to gated stations, we saw an additional drop on Tuesday 3/17 and then a similar number of riders on Wednesday 3/18. You can see the estimated daily total in the following chart. We have also provided a chart showing estimated ridership on 3/16 on the top 20 routes, again compared to the baseline week.

These estimates are also preliminary and will be adjusted as time goes on, but represent our best estimates at the time of publication.

Original Post Below

COVID-19 and Massachusetts’ response to it will have far-reaching impacts throughout our society, and the MBTA is no exception. Here at OPMI, we are working hard to analyze its impacts on ridership, performance, and other aspects of the T and the transportation system. In particular, many people have requested information on how the pandemic is impacting ridership. This post will examine the preliminary data that we have to answer this question.

Before we go further, we should note that the MBTA has been increasing its cleaning efforts since the first outbreak of the virus in MA, and that while experts believe that buses and subways are safe to ride, passengers should take precautions such as avoiding crowded cars and buses, not touching their face, and using hand sanitizer (which the T has made available in stations) and washing their hands after riding the T. More information is available at the continuously-updated page here: www.mbta.com/covid-19

The Data

As we have discussed before, gathering and reporting accurate data on ridership is not as easy as it might seem, especially when a quick turnaround is required. Our usual ridership reporting includes factors to account for passengers who we do not observe through our automated equipment, and we usually wait at least a few weeks before reporting anything due to the delay in transferring data from our vehicles. We also conduct in-person counts to verify automated data and improve accuracy for our end of year reporting. 

With everything changing so quickly, we obviously had to shrink the timeline in order to provide useful data for leadership and the public. So we focused on three data sources for this post: Counts of validations at gated stations from the fare collection system, bus ridership estimates from automated passenger counters, and parking lot utilization to get an idea of ridership on commuter rail. Because all of these sources usually have extra processing and QA/QC done as noted in the previous paragraph, all ridership estimates in this post should be considered very preliminary and subject to change.

Gated Stations

To examine ridership on the rapid transit system, we used validations (taps or ticket insertions) at the 64 gated stations in the MBTA system. This data came from the fare collection system and is not adjusted to account for passengers who enter the gates without interacting with the equipment (this can be children, fare evaders, or people who enter when the gates aren’t functioning). 

We pulled the total validations by day, and then grouped them by station and by line. For stations where passengers can board multiple lines, we use a rough “split” factor to assign riders to each line (For example, at Park St, we estimate that 54% of people entering the gates are then going to board the Red Line, and 46% go on to board the Green Line).

The below chart shows the total taps by line since February 1:

Chart showing taps by line since Fe 1

The drop in validations is clear from the above. The week of 3/2 had slightly lower ridership, but only a few percent lower than usual. Last week, ridership really started to drop beginning Wednesday as people began working from home and events got canceled. 

To show these data a different way, see the below table. We’ve chosen the week of 2/24-2/28 as a “normal” comparison week and calculated the percentage change last week from that point. You can also download these data as CSV files at the end of this section.

   Average Validations Week of 2/24 Change 3/10  Change 3/11 Change 3/12 Change 3/13
Blue Line 47,344 -6% -9% -17% -28%
Green Line 75,007 -19% -22% -39% -52%
Orange Line 155,749 -10% -21% -31% -47%
Red Line 199,146 -13% -20% -35% -51%
Silver Line 4,990 -33% -37% -48% -68%
Total 482,235 -12% -20 -32% -48%

You can see the drop increase throughout the week, until by Friday we had 48% fewer taps than the comparison week. Usually Fridays are of course lighter ridership, but not nearly this much.

Importantly, though, the change in ridership was not uniform. You can see from the above that the Blue Line was just 28% less busy than usual. There were other differences when you break ridership down by station:

Stations with the largest and smallest changes are listed below:

Largest change:

Station Change from week of 2/24 to Friday, 3/13
Community College -71%
World Trade Center -70%
Kendall/MIT -69%
Arlington -69%
Courthouse -68%
JFK/UMass -64%
Alewife -64%

 Smallest change:

Station Change from week of 2/24 to Friday, 3/13
Suffolk Downs -18%
Revere Beach -20%
Wollaston -21%
Wood Island -22%
Andrew -23%
Maverick -23%
Airport -23%

As you can see, stations where much of the ridership comes from a nearby college, or tends to be more white-collar, had a larger drop, while much of the Blue Line had a much smaller drop. 

Bus Ridership

The above chart shows total ridership by day as estimated from the APCs on board buses. The more recent dates here have less precision than the earlier dates, but we are fairly confident in these totals. You can see that the overall drop in bus ridership was more modest – roughly a 32% drop from the week of 2/24. You can see the differences by route in the below chart of key bus routes:

As with the gated stations, you can see a fairly wide range in the level of change depending on the route. Without doing a detailed analysis, it seems plausible that as you might expect, routes where more riders are able to take time off and self-isolate saw a larger drop in ridership. We will keep an eye on these trends as the response to COVID-19 continues.

Commuter Rail

While we don’t have detailed ridership from commuter rail on a daily basis, we took a look at the utilization of MBTA-owned parking lots as a proxy for ridership. We saw that on Thursday, parking lots were 42% less-utlilized than during the week of 2/24, while on Friday, utilization had dropped by 69%.

The RIDE

For the RIDE, we have very detailed data as each completed trips is recorded in the RIDE’s software. We compared the trips taken last week to the average daily trips taken the week of 2/24 and found the following:

Date Trips taken  Change from Week of 2/24 average
3/9/2020 4,881 -7%
3/10/2020 5,248 0%
3/11/2020 5,059 -4%
3/12/2020 4,584 -13%

Conclusion

Understanding that a significant number of people continue to rely on the MBTA, we will keep a close eye on ridership levels and as always, learn what we can from them to continue making data-driven decisions that best address our customers’ needs.  Safety is, and always will be, our top priority.

We are proud to release the 2019 version of Tracker.

Tracker is our performance management report, which is our response to Chapter 25 of the Acts of 2009. In other words, and according to the Tracker website, “Tracker is the report card for the Massachusetts Department of Transportation (MassDOT) to our stakeholders, including state and local elected officials, DOT administrators, and all who use and rely on our network.” The data in Tracker come directly from each department, which our Office of Performance Management & Innovation (OPMI) uses to create a yearly report that acts as the central location for all MassDOT performance data. Though the responsibility of Tracker remains the same, OPMI strives to improve the  versatility and interactivity of Tracker each year.

                                         OPMI Improvement Process

The implementation of performance management helps improve project and program delivery and informs investment decisions, as shown in the figure above. Performance management is iterative and must be an on-going process to be effective. This process also links to the Capital Improvement Program, which determines how resources are allocated across Divisions, and which projects are prioritized for funding.

Transportation Secretary and CEO Stephanie Pollack said, “The Baker-Polito Administration is investing historic levels of funding into transportation systems throughout Massachusetts, with the goal of better enabling people to reach families, friends, jobs, businesses, and other opportunities. We are pleased to provide this resource which illustrates recent progress in improving our roads, bridges, airports, railways, bikeways, and the performance of bus, subway, rail, and the Registry of Motor Vehicles.”

Since 2010, Tracker has gone above and beyond the mandated legislative requirements, making the information easily accessible and digestible. Each of the four divisions within MassDOT (Highway, Aeronautics, Rail and Transit, and the RMV) and the MBTA has a scorecard with metrics tracked year over year. Interactive maps allow users to visualize areas of jurisdiction and the charts show change over time to easily indicate performance improvement or decline.   For each division and the MBTA, you will find metrics arranged in the following categories: Customer Experience, Safety, System Condition, Budget & Capital Performance, and Healthy & Sustainable Transportation. The website has all of the data, while the pdf contains summarized information, available in scorecards. The Tracker site allows users to take a deeper dive into the data and shows trends up to six years, whereas the scorecard only has change in performance from previous year. An “Other Resources” tab contains links to plans and reports referenced throughout the Tracker site.

For MassDOT’s tenth anniversary, Tracker, highlights advancements, while acknowledging where performance did not improve for 2019. This is the second year in which Tracker is available in a web-based and interactive platform. New for 2019 are MBTA performance measures for fatalities, injuries, and derailments, additional context on bicycle and pedestrian crashes within Highway safety, and conditional baselines for all MassDOT’s rail assets within the Rail and Transit Division. 

For scorecards and a high-level overview of the data, view the pdf here.

 

This year, MassDOT released new Statewide Bicycle and Pedes­­trian Plans with the goal of increasing the comfort, safety, and convenience of biking and walking for all people. The plans lay out a vision that all people, including new riders, will be able to take at least some of their reasonably distanced “everyday trips”, or non-recreational trips, on high-comfort bicycle and pedestrian infrastructure. The plans define a reasonable distance as a half-mile for walking trips, three miles for non-work bicycle trips, and six miles for bicycle commute trips.

One key piece of both plans is making it easier for people to access transit. Active modes (like biking and walking) should be viable options for people accessing a transit stations.

To measure the success of the plan at making active modes viable, the plans published the following metrics: 

  • Percentage of trips under 6 miles beginning or ending at a transit station that were made by bike. (Bicycle Plan)
  • Percentage of short trips beginning or ending at a transit station that were made by walking. (Pedestrian Plan) 

This analysis focuses on the modes of transportation that passengers used to access major MBTA stations. While the plans reference the state as a whole, the MBTA is the only agency for which we have data on access modes. Additionally, it is unlikely that any mode besides walking is used to access neighborhood bus stops because 96% of bus trips are part of a journey that the person began by walking (Systemwide Passenger Survey) and the MBTA is the only agency to offer non-bus service. 

This analysis relied heavily on the MBTA’s 2017 Systemwide Passenger Survey, which asked riders on all modes and lines to report on a specific trip taken on the system. Respondents identified their approximate origin, the station they accessed, and how they arrived at the station.  

Methodology

The data sources used for this analysis are as follows: the 2017 MBTA Systemwide Passenger Survey, the MBTA GTFS stops file, and the MassGIS Seaports file. 

It is important to note a few major decisions made pre-analysis that affected the results. We used the distance to the closest station even if that was not the station accessed because it was more philosophically in line with giving people the option to bike or walk. For example, if someone is driving to a farther Commuter Rail station because of parking constraints, parking costs, or traffic, we do not necessarily want to ensure them a direct biking path to a farther station. By providing safe and comfortable infrastructure to their nearest Commuter Rail station, we are giving them an option to use active transportation, which negates the parking issues that force them to drive farther. Additionally, we used a three-mile threshold instead of the original six-mile threshold published in the bicycle plan for this analysis to account for a realistic proportion of travel time dedicated to biking for a trip that also includes transit. Below are the steps we took to produce this analysis.

The analysis process itself was relatively simple. We started by mapping all of the applicable MBTA Rapid Transit, Commuter Rail, Silver Line, and Ferry stations. Using ArcGIS’s network analyst feature, we created half-mile and three mile buffers around the station along the road network. 

We then looked at the responses from the Systemwide Passenger Survey, and identified the relevant trips that started with one of the above modes. From that filtered dataset, we geocoded the origins of reported trips and identified which trips began within a “walkable” distance, and which trips began within a “bikeable” distance. The map below shows a sample of Wakefield residents and their chosen access modes. Those inside the red outline are within a half-mile of the Wakefield Commuter Rail station. 

Wakefield Station walkshed

 

Results

Nearly 95% of trips that start within a half-mile of a transit station begin with walking. Only about 5% happen with a car-based mode, including carpooling and ridesharing.

When you look at trips that start within a bikeable distance of transit (between a half-mile and three miles), nearly half are made walking. However, only 4% are made on bike. Just over 45% are made in a car. There is a big opportunity for all agencies who own and operate roads to improve street design and create bicycle infrastructure to help make some of those people who choose to drive switch over to a 15-minute or shorter bike ride instead. 

   Trips starting within half-mile (walkable)  Trips starting between half-mile and three miles (bikeable)
 Walked  94.8%  49.9%
 Biked  0.7%  4.1%
 Car-based travel  4.5%  46.0%

 

Equity

The Bicycle and Pedestrian plans put a big focus on equity in order to ensure that the opportunities and benefits of bicycling and walking are equitably distributed. They identify several specific populations of interest. During metric development, we incorporated equity checks into every measure to understand how well different populations are served by bicycle and pedestrian infrastructure in the state. As a part of this analysis, we also looked at how respondents belonging to equity groups accessed transit as well. We analyzed the access patterns for people of color, low income people, people under 18 and over 65, and no-car households. Due to sample size issues in the Passenger Survey, we were unable to compile access mode results for people with limited English proficiency or people with disabilities. The table below summarizes the results of our equity analysis for walkable distances (0 - 0.5 miles). 

 

   Minority  Low-           Income  No Car  65 or over &   under 18
 Walked  94.5%  97.5%  98.4%  92.1%
 Biked  0.6%  0.7%  0.9%  1.8%
 Car-based      travel  4.9%  1.8%  0.7%  6.1%

 

The equity analysis results for some other metrics in the Bicycle and Pedestrian Plans are relatively easy to interpret – for example, if there are fewer sidewalks in low-income neighborhoods, we view that result as inequitable. However, these results are a bit more complicated. When a higher percentage of people in low-income communities walk to transit, it is not necessarily is a sign of transportation equity, but could be a reflection of fewer opportunities for travelers who may sometimes legitimately need to access transit without walking. Treating these results as providing context allows us to interpret implications one by one, for example indicating a greater need for pedestrian infrastructure that connects low-income neighborhoods to transit. 

Conclusion

There are several other measures that we are using as a part of this project; we wanted to give an example of one of the simple ones to illustrate the underlying complexity of measuring access to transit. Future blog posts will cover some of these additional measures.