How Subway Ridership by Time of Day is Changing

The COVID-19 pandemic has greatly impacted transit use, changing trends that were previously well-established and static over time. These circumstances have necessitated a new look at our data to investigate transit changes in a thoughtful way. This post will focus on how we’re thinking about changing ridership trends (other posts on ridership can be found here). Previously, we have analyzed these trends by looking at the number of validations by the time of day, as visualized for each rapid transit line here:

Line graph showing number of taps for each T line throughout 24 hour day for blue, green, orange, and red lines. Lines on graph are highest at 8am and 5pm peaks and decrease then increase in between them. After second peak line decreases.

This graph uses data from 2019, and before the pandemic, the trends remained very consistent; if the same graph was shown for 2017, for example, it would be almost identical. Therefore, looking at how daily ridership changed over time was usually not pertinent and averages over a range was sufficient. However, the pandemic has had a remarkable and ongoing impact on ridership, with transit patterns changing based on the current conditions. Looking at ridership throughout the day in this way only tells us how many taps are occurring at each time, not how trends are changing over time because of factors such as workers returning to in-person work. To best understand shifts in ridership over time, we needed to view the data in a different way. First, we sectioned the weekday into eight distinct time periods that are related to the time periods in the Service Delivery Policy, as shown below:

Time periods: Early Morning is 4:30 to 6 am, Am Shoulders are 6 to 7 am and 9 to 10 AM, AM peak is 7 to 9 AM, Midday is 10 AM to 4 PM, PM shoulders are 4 to 4:30 and 6:30 to 7:30, PM peak is 4:30 to 6:30, Evening is 7:30 PM to 12 AM, Night is 12 to 4:30 AM.

When first analyzing within-day ridership, we looked at the number of taps for each time period over time using this graph:

Line graph shows number of taps for each time period since 1/1/21. Midday line is much higher than all others, then PM peak, AM peak, Evening, Am shoulders, PM shoulders in that order at similar heights, then sunrise and night below that. Midday height is about 80 thousand taps, with next being about 45 thousand.

The Midday time period has the most taps, but it is also longest time period as it covers six hours of the day. This graph can be misleading because we want to compare the rates of ridership between time periods, and this is not possible when the time periods are various lengths. To show how busy the T was in each time period, we altered the data to show validations per hour within each one, giving a normalized representation of which time periods have the highest rates of ridership.

New line graph (still since 1/1/21) has more evenly spread lines. PM peak is highest, then AM peak, PM shoulders, midday, AM shoulders, evening, sunrise, then night. PM peak height is about 22 thousand taps.

In the updated graph the PM Peak has the highest ridership per hour, with Midday falling significantly below. Each time period has more distinct ridership rates using this method. The updated graph is more accurate to what a rider will experience when using the subway; the PM and AM Peaks are the most crowded, and both have significantly higher ridership per hour than Midday when less people are riding.

Pandemic Impact

Using this new method, we can analyze ridership trends pre- and post-pandemic. Looking at 2019, the AM and PM Peaks have very similar rates, trading off which is the highest throughout the year. Every other time period is very distinct and remains relatively stable, with none overtaking another at any point. This supports what was mentioned earlier; looking at daily ridership trends over time was not necessary pre-pandemic because they remained consistent. The biggest change we can see is near the end-of-year holidays, when many usual commuters and students traveling in the AM and PM peak are on vacation.

Line graph with line for each time period for 2019, each line is mostly straight and doesn't touch another except for AM and PM peak. These are very similar and overlap. The height is about 55 thousand taps.

Looking at the trends post-pandemic shows noteworthy results. Ridership rates throughout the day are more similar, especially in the months after the start of the pandemic. This is indicative of lower overall ridership for all time periods, as well as a larger drop for peak ridership in proportion to off-peak. Comparing 2019 and 2021, January AM peak ridership went from approximately 45,000 taps per hour to 7,000, while Midday dropped from approximately 20,000 to 6,500. Interestingly, the PM Peak now has a distinctly higher ridership rate than the AM Peak; AM Peak ridership rate is never above the PM peak at any point. A portion of this phenomenon might be explained by riders having more flexible hours at work post-pandemic, causing workers to go into work at later times and still go home around the end of the traditional workday. It could also be influenced by passengers who are working from home and use the subway only in the evening after their workdays.

Line for each time period since 1/1/21. Same as previous graph, PM peak is highest, then AM peak, PM shoulders, midday, AM shoulders, evening, sunrise then night.

Implications for the Future 

Ridership was greatly impacted by the pandemic and is still in flux, as seen by the response to Omicron depicted in the previous graphs. Adapting to these changes and thinking of the most accurate way to measure transit activity is important to OPMI, and we will continue to make improvements in how we visualize data. When ridership is at more similar rates throughout the day, like we have seen since the start of the pandemic, there is less overcrowding at peaks and more people will need reliable service during off peak times. Overall, we want to prioritize service to demand, so looking at data like this is helpful in providing the best service possible as we look towards the future.