University of Michigan to trial new kind of on-demand transit system


Passengers could be trying out a new urban mobility system on the University of Michigan’s North Campus as soon as summer 2017. Its creators say it could deliver riders to their destinations in as little as half the time of the existing bus system, at a lower cost, and would eventually use a fleet of autonomous shared vehicles.

Called RITMO (Re-Inventing public urban Transportation and Mobility), the proposed system combines aspects of Uber-style ridesharing, fixed-route buses and light rail, into a single system called ‘hub-and-shuttle.’ The limited test deployment is likely to be the world’s first on-the-ground implementation of such a system. It would combine high-frequency buses serving the busiest transportation hubs, with a fleet of about 50 on-demand shared shuttles to get riders to and from those hubs. Passengers would hail rides using a smartphone app that would calculate the most efficient journey for a given destination. The app would track the progress of each passenger and each vehicle, feeding data to a back-end system that would be optimized in real time to minimize congestion and maximize efficiency.

The on-demand vehicles would have human drivers at first, but the designers hope they would eventually be replaced with autonomous vehicles, lowering costs further. Development of the system is funded with a US$1.4m grant from the Michigan Institute for Data Science (MIDAS). If the test deployment is successful, the team plans to expand the system across U-M’s campus within a year, to all of Ann Arbor within three years, and eventually to the Detroit area as well.

If the new system is successful, it could gradually replace U-M’s existing fixed-route bus system. The core U-M system would remain free to users, although riders will eventually be able to choose from several different transportation options for a given journey, some free, some not.

The key to making it all work is data. The team has already begun collecting that data, asking students and staff to volunteer anonymous GPS tracking information through the existing U-M smartphone app. The detailed data will enable them to apply data mining, machine learning, and mathematical optimization techniques to tease out patterns and make predictions about how people move around U-M’s campus. They are also using existing data from the U-M and Ann Arbor public transportation systems, as well as demographic and mobility data.

“It’s similar in some ways to the ride sharing that’s available now, but much more sophisticated,” explained RITMO’s lead researcher, Professor Pascal Van Hentenryck. “Obviously you can’t have everyone using something like Uber, because that would cause massive congestion. But on-demand hub-and-shuttle can provide some of the convenience of point-to-point travel, along with the efficiency of a high-frequency transit system. A bus costs many times more than a car, so high-capacity buses often aren’t the most efficient way to serve areas where demand is low. Serving those areas with small, on-demand vehicles would be much more efficient, and more convenient for riders.”

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Tom has edited Traffic Technology International (TTi) magazine and its Traffic Technology Today website since May 2014. During his time at the title, he has interviewed some of the top transportation chiefs at public agencies around the world as well as CEOs of leading multinationals and ground-breaking start-ups. Tom's earlier career saw him working on some the UK's leading consumer magazine titles. He has a law degree from the London School of Economics (LSE).