Commuter Rail May and June 2019 On-Time Performance by Route, Time, and Train

Filter by rail line, on or off peak, direction, and hour


Download Raw Data

May/June combined performance for each train


A histogram shows frequencies in each category. Below, you can see how many trains were 0 to 4 minutes late, 5 to 9 minutes late, and so on. You can change the width of the categories (called bins) with the slider below.



This app was created by Sharon Machlis from data received by request from the MBTA. The data includes commuter rail daily scheduled and actual departure and arrival times for each train -- but for the train's first and last station only.

In the first tab, You can see on-time performance filtered by commuter rail line; AM Peak, PM Peak, AM and PM Peak, or Off Peak; Inbound or Outbound, and by arrival hour. A graph and text summary aggregates performance of all trains in the data, based on any filters you've chosen in the left column. There is also a table showing each train's aggregated performance -- for example, how the 508 performed overall, how the 510 did, and so on.

The second tab lets you see the raw data behind those summaries -- every trip by every train each day in the data, filtered based on any that you applied using the left column. Filtering using the search bar above the table will not affect your data download.

The third tab shows a histogram, which visualizes frequencies by category. The default view lets you see how many trains were 0 to 4 minutes late, how many were 5 to 9 minutes late, etc. You can filter the data using the controls on the left and also set the width of the categories (number of minutes in each) with a slider above the histogram.

Note that if you download the daily raw data, it will be filtered based on any filters you selected in the left-hand column. It will not be filtered based on any search you do in the table's search box.

Sharon is a long-time journalist and the author of, Practical R for Mass Communication and Journalism . She is currently director of editorial data and analytics at IDG Communications.