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.
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 email@example.com.