you up and drops you off at the light rail station, for example—we can connect people to the infrastructure so they can use transit more effectively,” he said. “We want that on-demand component because the first and last miles are harder to cover.”
Van Hentenryck’s project will use U-M, Ann Arbor, and Detroit—including the Ann Arbor-Detroit corridor, which is loaded with commuters—as testing grounds for these innovative models, and will include collaboration with the U-M Parking and Transportation Services Department for real-time data collection on driver behavior. In Detroit, Van Hentenryck’s team will look for neighborhoods that would benefit from, or even be rejuvenated by, an on-demand system that increases mobility and keeps transit costs low.
“We want to understand people first, so we’ll mine the huge amount of data available in the Ann Arbor-Detroit region to understand what people are doing, when and where they’re going, and how,” he added. “People are very predictable, in a sense. We can build models at the aggregate level and use our infrastructure more effectively.”
Van Hentenryck said one of the biggest challenges will be simply parsing the massive amount of raw data. He gave the example of data from the Ann Arbor bus system; researchers will be able to access boarding data but they won’t know when a rider gets off the bus.
“We have to connect the datasets,” he said. “We have lots of information about one part, but not the whole.” He expects to find out why the transportation system is currently organized the way it is: “There may be a reason beyond simplicity.”
Once the two projects are able to analyze the data to the point that researchers can offer recommendations, the final hurdle will be to convince people to alter their behavior. In Ann Arbor, the public transit system is already at 75 percent capacity. In Detroit, it’s only at about 25 percent capacity, which is why Van Hentenryck is so keen to test new models there.
“The next couple of years will be very exciting,” he said. “Autonomous vehicles will benefit public transportation models immensely. It’s the convergence of a lot of ideas—data science, but transportation is also changing completely. In cities without a history of good public transportation, can they benefit? We want to see how people respond.”
