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Behind the scenes with MBTA data.

In our review of the impacts of the COVID-19 pandemic on ridership, we showed the following charts that display average entries at gated stations over the day. This shows us how ridership during the pandemic has been not only lower in volume, but also less focused on the traditional peak times around 8 AM and 5 PM on weekdays.

Chart showing average validations over the course of the day for Fall 2019

Chart showing average validations over the course of the day for Fall 2020

While the “traditional” pattern is clearly very peaky, the contrast becomes even more stark if you start to dig into the data more granularly. Here is a chart of average entries on weekdays (all lines) in Fall 2019, sorted into 5-minute buckets (compared to the 30-minute buckets above).

Chart showing average validations by 5 minutes over the course of the day for Fall 2019

In the five-minute period from 5:10 to 5:15 PM, the T averaged just over 6,200 taps at gated stations in Fall 2019. But, just a few minutes later from 5:25-5:30, the T averaged 4,998 taps, and a few minutes earlier, from 4:55-5:00, we averaged 4,679 taps per weekday.

Here is a chart showing the same time period, focusing just on the downtown stations on the Red Line: South Station, Downtown Crossing and Park Street:

Chart showing average validations in 5-minute increments over the course of the day at downtown stations for Fall 2019

From 4:55-5:00 PM, these three stations see an average of 840 taps per weekday. But just a few minutes later from 5:05-5:10, we average 1,240 taps. Some of these passengers entering at these stations are headed for the Green, Orange or Silver Lines, but other passengers are also transferring behind the gate from these lines to the Red Line. Fortunately, ODX estimates these boardings, and we have them already aggregated here by 15 minute buckets: 

Chart showing average boardings on Red Line over the course of the day for Fall 2019

Counting transfers and non-interaction, ODX estimates that over 4,000 people board the three core stations on the Red Line in the time period from 5:15-5:30 PM. By 6-6:15 PM, this has dropped to about 2,800 passengers.

If three trains pass through these stations in that time period and we assume passengers are traveling in both directions equally, that’s over 600 passengers (most of an entire train!) who are boarding each train during the busiest time. When you add in additional passengers who enter at busy workplaces like Kendall, Central, and Broadway, not to mention any passengers traveling across downtown, then it’s no wonder that crowding reaches extreme levels at this time of the day. With trains at capacity, there is very little margin for error: When there is even just a slight delay, or a particularly busy day of ridership, passengers are at crush capacity, are left behind, and ultimately have a bad experience. For some passengers, the crowding levels even when things are working perfectly is unacceptable and forces them to choose other options (We examined this with some focus groups and surveys in a blog post here).

Mass transit’s particular advantage over other transportation modes is its ability to move large numbers of people from a wide area into a particular place at the same time without them needing to park a vehicle. But when transit is over capacity, we have a “tragedy of the commons” situation where the service works significantly less well. And adding peak capacity is very expensive, and sometimes impossible. 

One of the potentially positive things to come from the pandemic is the likelihood of increased flexibility in work hours for many people who were previously part of the peak-of-the-peak crush load. Additionally, transit agencies throughout the country have realized (or emphasized) that their essential ridership – those who have continued to ride transit throughout the pandemic – are often riding outside of peak hours. Importantly, these non-work trips are also the types of trips that transit needs to be competitive for in order to reduce car ownership among inner core neighborhoods. 

If traditional 9-5 passengers are more flexible in when they leave work, and transit agencies are able to provide more service off-peak and in neighborhoods to better serve non-work trips, we could eventually reach a scenario where ridership is similar (or even higher) than pre-pandemic, but less concentrated on the peak-of-the-peak. Consider the following chart, which shows hypothetical Red Line boardings during the PM peak, if they were more spread out:

Chart showing average validations at peak times for Fall 2019 and a hypothetical post-pandemic scenario

To create this chart, a “capacity” of 3400 boardings per 15 minutes was applied to the data from Fall 2019 shown above. This amount of demand, given peak-level service, would create full, but not overcrowded trains in each direction. Additional boardings above this capacity were distributed among the other time periods between 3 and 7:30 PM in the same proportion as the existing ridership. To keep things simple, 54 boardings were eliminated entirely. In this purely illustrative scenario, we can see that nearly the exact same number of passengers board the Red Line between 3 and 7:30 pm, but they are simply less concentrated at the busiest time. Add in some additional ridership off-peak and on weekends, and you could see a scenario with even higher ridership than before. 

Time Period Fall 2019 Avg. Hypothetical Future Scenario
3:00 PM 1,397 1,469
3:15 PM 1,605 1,687
3:30 PM 1,700 1,787
3:45 PM 1,674 1,761
4:00 PM 2,085 2,193
4:15 PM 2,530 2,661
4:30 PM 2,744 2,885
4:45 PM 3,025 3,180
5:00 PM 3,685 3,400

5:15 PM

4,460 3,400
5:30 PM 3,738 3,400
5:45 PM 3,285 3,400
6:00 PM 2,867 3,014
6:15 PM 2,615 2,750
6:30 PM 2,325 2,445
6:45 PM 1,819 1,913
7:00 PM 1,604 1,687
7:15 PM 1,364 1,434
TOTAL 44,521 44,467

The MBTA would not, and likely could not enforce a particular capacity on boardings. But, this thought experiment illustrates that if future flexibility in passengers’ schedules allowed more passengers to board outside of the busiest time, the MBTA could carry just as many passengers as pre-pandemic. Just as importantly, this scenario would likely provide more timely (due to reducing the likelihood of being “left behind”), comfortable trips for all passengers, and more reliable service overall.

In the past two posts, we’ve given an overview of how ridership changed during the pandemic, both over the course of the year and spatially throughout the system. In this post, we’ll take a look at how patterns of ridership changed temporally on a weekly and daily level.

Ridership by Time of Day

Chart showing validations at MBTA faregates by time of day for Fall 2019

Validations by time of day, Weekdays 9/1/2019 - 12/31/2019

Chart showing validations at MBTA faregates by time of day for Fall 2020

Validations by time of day, Weekdays 9/1/2020 - 12/31/2020

Early on in the pandemic we showed these charts that show taps over the course of the day. We have updated them here, showing total validations per half-hour both for weekdays in Fall 2019 (9/1/19-12/31/19) and Fall 2020 (9/1/20-12/31/20). The updated charts look pretty similar – the pattern of ridership over the day did not change a lot from the beginning of the pandemic to the end of 2020. This suggests that those “essential” workers who were still riding in March continue to drive the patterns of ridership, while some other riders have returned but not concentrated in particular times.

The most interesting thing about these charts is the broad spread of the peaks. While passengers in normal times are highly concentrated in the peaks (and really, in the peak-of-the-peak) at 8 AM and 5 PM, we see in Fall 2020 that the number of boardings at 3 PM is almost exactly the same on average as the number at 5 PM. Additionally, the midday ridership is up to about 50% of the peak, while in Fall 2019 it was less than a third of the peak level. While we do not expect these patterns to continue in precisely the same way once we reach the “new normal”, even small changes in the concentration of passengers during the peak would have big implications for service provision, as often the extreme crowding at the peak-of-the-peak slows trains, increases dwell times, and reduces overall capacity.

Ridership by Day of the Week

We also saw different distributions of ridership throughout the week than we usually do. The following table shows the total validations at all gated stations on the average day of the week, for the same Fall 2019 and Fall 2020 periods as above. Holidays where the MBTA ran different levels of service than a weekday schedule are excluded.

Year Sunday Monday Tuesday Wednesday Thursday Friday Saturday
Fall 2019 Validations 155,783 454,830 473,372 505,383 503,383 474,962 210,193

(% of the week)

5.6% 16.4% 17.0% 18.2% 18.1% 17.1% 7.6%
Fall 2020 Validations 64,150 110,633 116,405 115,734 110,999 123,988 87,863
(% of the week) 8.8% 15.2% 16.0% 15.9% 15.2% 17.0% 12.0%

We have also made a chart showing validations by time of day over each day of the week. These are similar to the charts at the beginning of the post, but all lines are added together. We’ve highlighted Fridays, Saturdays and Sundays as they show the biggest changes (click to enlarge):

Chart showing ridership by day of the week, comparing Fall 2019 and 2020

Usually, Wednesdays and Thursdays have the highest ridership levels, which makes sense since people tend to take days off either at the beginning or the end of the week. All weekdays in Fall 2019 ranged between 16.4% and 18.2% of the total validations for the week. In Fall 2020, however, weekdays were a smaller proportion of weekly ridership, ranging from 15.2% to 17% of the total, which makes sense given the lack of 9-5 commuters. Interestingly, Fridays were the busiest weekday in 2020 by a significant margin. In the time of day charts, it appears the Friday morning peak is similar to the other weekdays, but ridership increases in the afternoon and evening. It is possible these additional passengers are working from home most of the week, but then ride the system on Fridays as they end their week.

Weekend days provided a higher proportion of ridership than usual in Fall 2020. While Saturdays in Fall 2019 were 44% of the average weekday, in Fall 2020 they had 76% of the average weekday’s ridership. Sundays showed a similar pattern, increasing from 32% of the average weekday (in 2019) to 56%. Again, this suggests that passengers who rely on transit for travel continue to ride each day or have different schedules than the usual peak patterns, while those who tend to mostly ride during the week are no longer traveling or have changed their modes.

While research continues, the characteristics of ridership continue to show that transit provides an essential service for those who don’t or can’t drive cars and who are unable to work from home. The MBTA continues to keep a close watch on ridership and monitor passenger behavior in multiple dimensions: volumes over time, volumes by route and location, and behavior over the course of the day. While we expect 9-5 commuters to return to the system, the pandemic has emphasized that hundreds of thousands of passengers rely on the MBTA who cannot work from home, and those passengers tend to more often travel at times outside the traditional peak. To best serve those essential trips well on into the future, we will need to carefully examine travel behavior using all available tools, and plan carefully, thinking beyond the usual emphasis on peak travel.

In the last post, we took a broad look at ridership on the MBTA in 2020, and dove into the details on which types of passengers continued to ride the system. In this post, we’ll examine where passengers rode the system and how that changed from the patterns we typically see.

The above map (click to enlarge) shows the MBTA’s bus and rapid transit routes and lines and is colored by the amount of ridership change they saw, comparing Fall 2019 with Fall 2020 data. The ridership is normalized per revenue vehicle hour to account for changes in service levels between the two time periods. While all routes lost some ridership, the amount varied greatly – the least affected routes (colored in the yellow end of the color scale) retained about 60% of their ridership, while the most affected routes (deep red) lost nearly all of their ridership (while some had no service at all in this time period). You can explore this data in this file.

From this map, a few broad types of routes seem to have retained ridership particularly well:

  • Routes in Roxbury / Dorchester / Mattapan, Chelsea / East Boston, and Lynn / Salem
  • Routes that travel a long distance, such as the 70
  • Routes that provide the only service to a particular area, such as the 34E

Each of these categories fits with the ongoing research that transit is currently serving “essential” trips, and these are the types of routes that the MBTA prioritized in its Forging Ahead service changes.

Total Ridership Change by Station

We also took a look at how ridership changed by rapid transit station (excluding the Surface Green Line as detailed data is unavailable). This file shows the % change in average weekday validations from January / February (combined) to each month in 2020. The top and bottom 10 are shown below for October (excluding Lechmere and Science Park which were closed for Green Line Extension construction). October was chosen as the point post-pandemic when ridership was the highest, which would best illustrate the differences between stations.

  Jan./Feb. Avg. Weekday Validations Jan./Feb. Rank Oct. Avg. Weekday Validations Oct. Rank % Change
Revere Beach

2918

54 1680 34 -42%
Suffolk Downs 755 62 376 60 -50%
Beachmont 3266 51 1505 39 -54%
Maverick 11206 13 5046 2 -55%
Andrew 5232 39 2321 21 -56%
Orient Heights 4438 42 1900 29 -57%
Fields Corner 4770 40 2027 25 -57%
Airport 7011 26 2977 17 -58%
Bowdoin 2327 55 967 49 -58%
Charles/MGH 10387 16 4162 3 -60%

Most of the stations that had high levels of retained ridership were on the Blue Line. This is also reflected in the line-level data that we covered in our last post. Other stations on the top ten list likely have a high number of passengers without vehicle access (Andrew and Fields Corner), or are near major medical facilities (Charles / MGH) which of course continued operation throughout the pandemic.

  Jan./Feb. Avg. Weekday Validations Jan./Feb. Rank Oct. Avg. Weekday Validations Oct. Rank % Change
Kendall / MIT 16870 4 2382 20 -86%
South Station 24385 1 3666 7 -85%
Alewife 11295 12 1857 30 -84%
Courthouse 3600 48 647 57 -82%
Arlington 6595 29 1198 44 -82%
Porter 8284 20 1518 37 -82%
Oak Grove 6236 30 1185 45 -81%
Davis 11397 11 2181 24 -81%
Park Street 15544 7 3000 16 -81%
Harvard 16546 5 3313 10 -80%

It seems safe to conclude that the stations that lost the most ridership tended to be a combination of:

  • those with high numbers of usual riders who can work from home, 
  • high concentrations of college students, or 
  • those with high numbers of passengers who drive and park.

It should be noted that these are also some of the busiest stations on the system. This is important for two reasons: First, even with many usual passengers not riding, these stations still served significant numbers of riders. The 3,000+ taps seen daily at Park St, Harvard and South Station (not even counting commuter rail passengers) is comparable to the ridership at stations like Wollaston or Stony Brook during normal times. Even though they were among the stations with the highest portion of ridership lost, Park Street, Harvard, South Station and even Kendall were still in the top 20 busiest stations in October. Second, it confirms that the subway system is largely designed around bringing passengers to work in the usually busy areas in the center of the city, while the bus system tends to serve more radial and outlying trips. While this is not a new observation, rarely is it revealed in such a stark manner. Even starker is the drop in ridership on Commuter Rail, which is even more focused on serving the center of Boston and oriented around peak travel than subway and bus.

In our next post, we’ll look into how ridership patterns changed by time of day and throughout the week.