The RITMO project focuses on urban transportation and mobility. It builds on two key enablers, connected and automated vehicles, and leverages the tremendous progress in artificial intelligence, data science, and operations research to design and operate a new generation of on-demand urban transit systems. RITMO assembles a multi-disciplinary team of researchers, from computer science, industrial and operations engineering, medicine, the school of information, urban planning, Ford Motor Company, and the transportation research institute.
RITMO collaborates with UM Poverty Solutions to improve accessibility to jobs, health-care, and education for all segments of the population with case studies in Ypsilanti and Detroit.
RITMO is starting a phased deployment of an on-demand multimodal transit system in Ann Arbor, featuring mobile applications, a cloud computing platform, and real-time optimization algorithms. The deployment is in partnership with Ford Motor Company, UM Logistics, Transportation, and Parking, UM Information and Technology Services, and UM advanced research computing technology services.
- Tutorial on how to use the Rider Mobile App.
- Map of the virtual stops for the first phase of the deployment.
- How to register for the VIP launch (starting on January 16, 2018).
- Interested in driving for RITMO? Check the job descriptions for students and non-student drivers. See also the flyer.
Data and Decision Sciences for Transportation
RITMO believes that data and decision sciences present a unique opportunity to transform urban mobility. We now have access to substantial data sets about mobility, infrastructure, transit systems, and congestion. The RITMO project is collecting and mining huge data sets to inform the design and operations of on-demand transportation systems.
The following video visualizes a potential transit system at the University of Michigan. High-frequency buses are depicted in blue, on-demand shuttles in green, and waiting riders in red. The taller the buses, the shuttles, and the waiting queues are, the more people are traveling on the buses and the shuttles or waiting at the stops.The visualization is based on real ridership data.
Some other potentially interesting links:
- Record and Michigan Daily articles (January 2018).
- University may begin testing new kind of on-demand transit system;
- Problem solving with real-time data;
- 2016 MIDAS Symposium.