MTA Subway Data

May 6, 2018
By

[This article was first published on R – NYC Data Science Academy Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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Have you ever taken a NYC subway, only to find that it took you much longer to get to your destination than you could have imagined? Was walking a faster option than taking the subway between adjacent stops? Perhaps you can recount your own personal horror story.

The reliability of the MTA’s subways in NYC has gone down over the last 5 years dramatically. According to recent ratings, of 20 large subway systems throughout the world, NYC’s subways rank DEAD LAST in terms of on time reliability, at only 65% of trains arriving at their final destination on time!

To many people, these are just small inconveniences, but the delays are causing a societal cost of $307 million every year. We always hear about the importance of the “New York Minute,” but it doesn’t seem like subway riders are able to capitalize on being able to move efficiently about the city.

I collected data from the first half of 2016 to see which stations riders are entering the stations at. Remarkably, there were several stations that had far higher ridership totals for entries than others. This was visualized using simple bar graphs, applied to all lines as depicted below:

Using the Shiny app, I was also able to depict these findings on a leaflet map of NYC. Below you can see the stations with the highest entries depicted with the largest blue circles. The user can scroll over a specific station circle to get the name and the exact amount of entries in 6 months. Using this visualization, any user can tell that there is a disproportionate amount of riders when comparing various stations.

In the process of combining all of the turnstile data, there were a few dates that had astronomically high entries. For example, hundreds of millions of riders entering a single station on a single day. This “bad data” was removed to eliminate any anomalies from the data.

Findings:

  • For those stations with significantly higher ridership totals, most were transfer points.
  • Stations on lines that did not have multiple transfers generally had lower ridership.
  • There was higher ridership for stations at the end of some of the lines.
  • Manhattan had the most subway ridership, even though there are more stations located in Brooklyn.

For further investigation, I would look into the delays caused at these high ridership stations in particular, as well as the lines that were connected to them. When comparing the delays data versus the ridership, perhaps I can pinpoint more specifically some of the problems that are plaguing the MTA Subway System right now!

After all, shouldn’t a world class city have a world class subway?

To leave a comment for the author, please follow the link and comment on their blog: R – NYC Data Science Academy Blog.

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