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transportation

Jun 23 2017
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Big Data in Transportation and Mobility symposium highlights diverse, emerging issues

By | Events, General Interest, News

MBDH-transThe 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.

For a complete list of speakers and topics, please see the agenda. Videos of selected talks will be posted at midas.umich.edu in the coming days.

Jun 23 2016
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MIDAS awards first round of challenge funding in transportation and learning analytics

By | General Interest, Happenings, News | No Comments

Four research projects — two each in transportation and learning analytics — have been awarded funding in the first round of the Michigan Institute for Data Science Challenge Initiatives program.

The projects will each receive $1.25 million dollars from MIDAS as part of the Data Science Initiative announced in fall 2015.

U-M Dearborn also will contribute $120,000 to each of the two transportation-related projects.

The goal of the multiyear MIDAS Challenge Initiatives program is to foster data science projects that have the potential to prompt new partnerships between U-M, federal research agencies and industry. The challenges are focused on four areas: transportation, learning analytics, social science and health science.

May 04 2016
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UMTRI Speaker Series: Henry Liu, Next Generation Traffic Control Systems with Connected and Automated Vehicles

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Abstract: Traditionally traffic signal systems are designed in such a way that different time slots are allocated to conflicting traffic streams in order to ensure vehicle safety. In the future, such design constraints may be relaxed with connected and automated vehicle (CAV) streams because crash avoidance can be achieved through distributed control of vehicle trajectories, therefore traditional traffic signals may no longer be needed.

In this talk, we will discuss the opportunities and challenges for traffic control systems with varying percentages of connected and automated vehicles. In particular, we will present our findings using the massive data set collected from the Safety Pilot Model Deployment project and Ann Arbor Connected Vehicle Test Environment, both supported by USDOT.

Bio: Henry Liu, PhD, is a Research Professor at the U-M Transportation Research Institute (UMTRI) and a Professor in the U-M Department of Civil and Environmental Engineering.