A team of data geeks is using bus GPS and passenger count data to "simulate future demand at every stop in Chicago, and predict how well transit service is likely to perform under a particular schedule change."
In a blog post on Data Science for Social Good, Juan-Pablo Velez and Andres Akle Carranza note that "the vast quantities of vehicle location and passenger count data can probably allow us to predict crowding along a route before it develops.
Bus service simulations - reflecting the natural variability in ridership and service - would allow the agency to forecast how adding or removing service on a route might affect its crowding levels. These statistical models could help CTA make proactive service decisions that anticipate changes in ridership and reduce crowding before it starts.
The team of geeks has been hard at work this summer analyzing the data and developing solutions. It's part of the Eric and Wendy Schmidt Data Science for Social Good summer fellowship at the University of Chicago. They expect to have some answers very soon.
This is a true partnership with the CTA that hopefully will benefit all of us. I thank you in advance for your work.
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