The Big Data in Transportation and Mobility symposium held June 22-23, 2017, in Ann Arbor, MI brought together more than 150 data science practitioners from academia, industry and government to explore emerging issues in this expanding field.
Sponsored by the NSF-supported Midwest Big Data Hub (MBDH) and the Michigan Institute for Data Science (MIDAS), the symposium featured lightning talks from transportation research programs around the Midwest; tutorials and breakout sessions on specific issues and methods; a poster session; and a keynote address from two representatives of the Smart Columbus project: Chris Stewart, Ohio State University Associate Professor of Computer Science and Engineering, and Shoreh Elhami, GIS Manager for the city of Columbus.
Speakers and attendees came from a number of organizations from across the midwest including the University of Michigan, University of Illinois, University of Nebraska, University of North Dakota, North Dakota State University, Ohio State University, Purdue University, Denso International America, Fiat Chrysler, Ford Motor Company, General Motors, IAV Automotive Engineering and Yottabyte.
“This was an extremely valuable opportunity to share information and ideas,” said Carol Flannagan, one of the organizers of the symposium and a researcher at MIDAS and the U-M Transportation Research Institute. “Cross-discipline and cross-institutional collaboration is crucial to the success of Big Data applications, and we took a significant step forward in that vein during this symposium.”
Topics addressed in talks, breakouts, and tutorials included:
- New Analytic Tools for Designing and Managing Transportation Systems
- New Mobility Options for Small and Mid-sized Cities in the Midwest
- Automated and Connected Vehicles
- Transforming Transportation Operations using High Performance Computing
- On-Demand Transit
- Using Big Data for Monitoring Bridges
At the closing session, participants outlined some areas that could be fruitful to focus on going forward, including increasing data-science literacy in the general public; diversity and workforce development in data science; public data-sharing platforms and partners; and privacy issues.