Image: New York City Department of Transportation
One of the main criticisms of Citi Bike, beyond the fact that it’s hemorrhaging money like crazy, is that the system has been fairly opaque with its data. But a huge data dump Monday changed that and will allow people to better visualize just how the system’s being used.
One of the coolest things about bike share programs in general is the huge amount of information they’re able to generate. Because every trip is logged, you can glean a lot about a city’s commuting patterns, traffic, and connections between things like subway delays and bikeshare usage. That information has led to all kinds of wonderful visualizations in cities like Washington, DC, where the information has been available for more than two years.
Citi Bike decided to make anonymized ride data available and includes everything from trip duration and length, starting and ending stations and times, and user type (annual vs temporary) to rider age and gender.
Already, you can see that people are doing some pretty amazing things with this. Take, for instance, this visualization of more than 75,000 Citi Bike trips taken between September 17 and 18. Created by researchers at New York University’s Rudin Center for Transportation (who had advance access to the data), the visualization is a mesmerizing flow of blue lines racing around Manhattan during rush hour and tapering off to near nothingness during the middle of the night (maybe the city really does sleep?).
More importantly, it’s not just a pretty map. The Rudin Center noticed something it called “reactionary biking,” a phenomenon in which “subway riders encountering delays likely switched modes to bike share for that trip … when delays increase, so does bike share ridership.”
Image: Rudin Center
The center notes that during the morning of September 17, the MTA sent out a text message alert that trains were delayed at Wall Street. Immediately following that, the Rudin Center noticed 17 rides that weren’t repeated the next day, when the trains were functioning normally.
“New Yorkers are avoiding or escaping transit delays by taking to bike share,” the blog notes.
This is the kind of information that Citi Bike is going to need if it wants to survive. It’s one thing to put in more than 300 stations around the city, and an entirely different one to know how they’re being used. This new treasure trove of information is the kind of thing that data heads lose their minds over, and with the raw data finally hitting the internet for anyone to play around with, we’re probably going to start learning a lot more about how well the system is working and how it could work better.