Behind the scenes with MBTA data.

Fare collection was temporarily suspended when the MBTA implemented mandatory rear-door boarding on buses and trolleys at street-level stops due to safety reasons during the pandemic. On July 20, after safety protocols were put in place, fare collection on buses and trolleys at street-level stops resumed along with the resumption of front-door boarding.

While ridership remains far from normal, the return to fare collection provided a natural experiment for learning more about how the system is being used and how passengers respond to fares. Being curious, we dug into the data a bit to examine what happened after fare collection resumed. Usually, when fares change, we see a corresponding change in ridership that can be explained to some extent by elasticity: as cost to passengers increases, ridership drops, and vice versa. In this case, there are some confounding circumstances which are likely affecting ridership:

  1. Most importantly, because of the pandemic, many usual riders are not traveling, and non-essential trips are discouraged. The concern over safety and general guidance to stay home is likely to have had a far greater impact on travel than changing fares. We would therefore expect a lower elasticity effect on the trips still being taken, since we believe more trips than usual are by passengers with few other choices.
  2. The subway system continued normal fare collection throughout the entire time period, and the MBTA provides free transfers between bus and rail. The T also has a number of bus routes that “feed” into the subway system. This means that for many trips, there was no difference in cost when bus fares were not collected, as passengers would need to pay the subway fare on the other leg of their journey.
  3. Fare collection was only paused for four months, and the pause was for safety reasons, so we would not expect to see passengers change their behavior in the way they might if it were a permanent change (for example, selling their car). Economists usually describe both “short-run” and “long-run” elasticity; in this case we only saw the short-run.

The rest of this post will discuss the changes and provide some hypotheses about what might be causing the changes and what they might mean.


Unfortunately, we by definition do not have any Automated Fare Collection system (AFC) data from the time when fares were not collected. That means we are unable to run our Origin-Destination-Transfers model (ODX) for that time period, nor can we follow particular cards or tickets over that time to see how their behavior may have changed when fares were re-instated. We at OPMI are used to having multiple data products at our disposal; during the pandemic, we’ve been unable to use many of them and have been limited in our ability to do field work or in-person surveys. So, we have to work with what we can. In this analysis, we largely used the Automated Passenger Counter (APC) data, which does not depend on fare collection and previous runs of ODX.

Chart showing ridership by day on buses and at gated stations for June and July 2020.

The above chart shows the bus ridership (from APCs) and the total gated station validations (from AFC) for the month before and after fare collection was resumed, along with a 7-day moving average. As you can see, while the trend on both bus and subway was slightly positive, when fare collection resumed on July 20, we saw a drop in bus ridership. This drop was about 9% from the week immediately previous to the fare collection change.

At the route level, we see a wide range in the ridership changes. Comparing the average ridership by route for the two weeks before fare collection with the average ridership for the two weeks after (and excluding routes with fewer than 500 passengers daily), the routes ranged from the SL3, which increased its ridership by 4%, to the 41, which dropped by 22%. Among Key Bus Routes, the biggest drop was the 1 with 16% fewer riders, and on the other end was the 28, which gained 2%. We have mapped the routes and shaded them by the amount of the change in the following map. You can also download the source data here.

Map of the change in ridership by route before and after resumption of fare collection.

There does not seem to be strong geographical variation in which routes were most affected. On the whole, routes which run further outside of the city center and have less overlap with the subway system seem to be less affected, but there are counter-examples as well. It is possible that riders on these routes have fewer other options, so they continued to take trips via bus at the same rate. For trips where passengers had a subway option, perhaps some passengers chose the bus leg when it was free but then either switched to subway or just walked further when bus fare collection resumed.

To test whether routes that are more often part of a multi-leg journey were less likely to lose ridership (discussed in bullet point 2 above), we first queried ODX to find how often a trip on each route was part of a multi-leg journey. We used data from July 2019 for this purpose – obviously data from 2020 would be preferable, but was not robust enough at this time. Below is a scatterplot of the transfer rate plotted against the change in ridership from before and after fare collection.

Scatterplot comparing the change in ridership with the transfer rate on each route.

From the scatterplot, the two measures don’t seem to correlate: the relationship is weak, and it is in the opposite direction from what we hypothesized. As transfer rate of the route increased, ridership actually tended to drop more. While the weak correlation does not disprove this hypothesis, it is likely that other factors are in play.

While we were unable to draw many conclusions from the difference in routes, we will keep thinking about this and as we get better data from 2020, will revisit the analysis. If you have any hypotheses about what factors may have affected these changes, email us at This email address is being protected from spambots. You need JavaScript enabled to view it.. We’ll update on the Blog if we discover anything!

When we last wrote about ridership on the Data Blog, we were still in the midst of closure here in Massachusetts. Since then, the Commonwealth has proceeded with multiple phases of its reopening plan. The MBTA has played its part by increasing service (especially where demand continues to be relatively high) and requiring masks as we cautiously reopen various sectors of the state.

While we still recommend people stay home when possible, we have seen the cautious reopening reflected in the MBTA’s ridership, as it continues to rise slowly but steadily each week. This post will provide an update on ridership overall, with a particular focus on the work we are doing on the data and technology side to better capture bus ridership.

We are keeping downloadable datasets for public use in this folder. These will be updated with the most recent data as often as we are able to. These datasets should be considered preliminary and subject to further adjustment, but they have been checked for major errors. Once datasets are finalized, we will add them to the Open Data Portal.

Datasets currently available for download in our public folder:

  • Gated Stations Validations by Station (updated each weekday) (1/1/2018 – present)
  • Gated Stations Validations by Line (updated each weekday) (1/1/2018-present)
    • These are the same datasets we have been sharing on the data blog since the pandemic started. They include the total validations at each gated station, aggregated by either station or line (Red, Blue, Green or Orange), by day, going back to January 2018. These data are also available in a published version on the Open Data Portal. The most recent data is subject to revision, but usually is received completely the next day.
  • Weekly Bus Ridership by Route (updated weekly)
    • This file includes the average weekday ridership (from the APCs), by week, for each MBTA bus route going back to the beginning of 2019. The first column is the route number, and each subsequent column is labeled with the date of the Monday that started that week’s data. For example, the column labeled “20-Jul-20” contains the average ridership for the weekdays in the week starting July 20th, 2020 (so 7/20-7/24).
  • Ridership by Route and Stop for TransitCenter (Static dataset)
    • This file contains the ridership by route and stop from early in the pandemic, with a comparable period from 2019. This is the data that we shared with the TransitCenter in their post linked below, so we are sharing it here as well. This dataset does not include added service during the COVID emergency (routes where we had the highest demand and crowding) and added additional trips (“run as directed” trips) will be undercounted.

How calculating bus ridership from APCs works

In order to track ridership on buses, we are using the Automated Passenger Counters (APCs) that are installed on 70% of the MBTA bus fleet. These record the boardings, alightings and load at each stop along a route. Since we do not have APCs on every bus, we scale the data we get up to the scheduled levels of service, and then scale ridership back down to account for scheduled service that did not run. You can see the outputs from this process on the Open Data Portal.

Since the pandemic, we have had to accelerate this process to generate ridership daily for internal use. Generally, this works well, but there are a few additional challenges that we have to account for:

  • A portion of bus trips are not included in the schedule and are run at the bus supervisors’ discretion to try to alleviate crowding. These are known as Run as Directed trips, or RADs. Since RADs provide more flexibility to respond to changing circumstances, the amount of bus trips that are provided by RADs has greatly increased under the MBTA’s COVID response as we prioritized high-ridership corridors and those that serve essential (including health care) workers. Because these trips are not included in the schedule, we depend on the operator entering a code in order to determine which route the trip ran. Usually they enter the right route, but since this is a manual entry, some trips are not assigned correctly. In these cases, the RADs will count towards our overall ridership, but not the route-level ridership. The new schedule that started on June 21 added more trips to routes that were previously more likely to have RADs assigned, so this problem was lessened, but not eliminated.
  • Before June 21, when the MBTA expanded service, two bus garages that have the lowest levels of APC coverage (Fellsway and Albany) were not used, which made the APC coverage in the remaining fleet higher. Since these garages reopened, our APC coverage has dropped. While this does not especially affect the accuracy of the totals, ridership at the route-level on some routes with lower levels of APC coverage is less accurate.

 How is Ridership Returning?

Ridership is slowly returning to the MBTA as the state proceeds with its cautious reopening. Interestingly, the trend has been slow and steady with very few big jumps or drops, even on days where various phases of the state’s reopening plan went into effect. The following charts show the daily weekday total gated station validations, and bus ridership from March 23 to July 17:

The following charts show the change on each day when compared to the rolling average 5 weekdays before, first for gated stations, then for buses, in order to show the rate of change as we regain ridership:

While there are day-to-day dips, each day since late April has been generally between 5-15% higher than the previous week. Gated stations have increased at a higher rate, but also dropped more to begin with, so they had more to gain.

What's Next

The pandemic has affected travel and ridership in different ways throughout the region. We at the T are looking closely at these changes and analyzing them as we plan service delivery for the Fall and beyond. While we all wish the circumstances were different, the pandemic does provide a natural experiment which can help us learn how passengers are using transit, and inform what service we should prioritize and improve, and where, for essential workers both during the pandemic and in the future. As one important example, we are leveraging our existing partnerships with cities and MassDOT to accelerate work on transit priority for what we already knew were the most important corridors for our passengers.

Our friends at the Transit Center took a look into the data (which we’ve shared again at the beginning of this post) and have written a post about it here. Like other analyses have shown, routes with higher levels of low-income and minority passengers, and those with low vehicle availability, tended to lose less ridership during the pandemic. 

Since the TransitCenter conducted the above analysis, ridership has returned to more routes, and the MBTA has restored service more on certain routes as well to try to meet demand. With the additional data, we at the Data Blog plan to take a detailed look at what factors influenced ridership changes during the drop in ridership and during its recovery. This and other research will help inform future service and other decisions.

Here at the blog, we will keep the datasets at the top of the post updated as we continue to get more data. We are also working on additional research as we try to help the MBTA and transit systems around the country make data-driven decisions to best serve passengers. If you found the above datasets or others useful in your own analysis, feel free to drop us a line at This email address is being protected from spambots. You need JavaScript enabled to view it..

Every month, we send out a survey to MBTA riders (you can sign up here!) and use the results to produce the Customer Satisfaction metrics that we publish on our dashboard. The four metrics (overall satisfaction, satisfaction with a rider’s most recent trip, satisfaction with the reliability of the MBTA, and satisfaction with the MBTA’s communication) are normally reported for respondents who had taken a trip on the MBTA within the past week. We exclude respondents who had not taken a trip within a week of completing the survey. 

This methodology allows us to ensure that survey responses are current and relevant and that the panel is representative of usual frequent riders; we have used other methodologies like in-person Intercept Surveys to target infrequent riders and visitors. Due to the current COVID-19 pandemic, however, many of our usual frequent riders have stopped taking the MBTA. As a result, until service and ridership have begun to return to normal, we will be reporting customer satisfaction in the following ways:

  • Satisfaction with a rider’s most recent trip will be reported only for respondents who have taken a trip within the past week
  • Overall satisfaction, satisfaction with reliability, and satisfaction with communication will be reported for all respondents

For trip ratings, we wanted to make sure that the metric reflected trips that were recent enough for the respondent to remember their trip well. In addition, as social distancing measures continue, many respondents will not have taken the MBTA for over a month, and their most recent trip will continue to be one from early March. For example, their April and March trip scores will be reflecting service from the same actual experience, making it impossible to measure changes in this metric on a month-to-month basis for these respondents who are not currently riding the MBTA.

However, we will report the other metrics for all respondents to the survey so that results for these questions can be comparable over time. The riders who are continuing to use our service are not a representative subset of all of our usual riders, and they do not respond to surveys in the same way. When possible, we want to continue to report customer satisfaction results from a group of respondents whose makeup is relatively stable over time to better track trends in customer satisfaction. Additionally, we understand that residents of the area still have opinions about the MBTA, how it is communicating, and the service it is providing, whether or not they have recently taking a trip on public transit. We want to continue to hear from all of our riders, regardless of if and how they are currently traveling, and our customer satisfaction reporting mirrors this interest.