Watch a recent seminar given by Dr. Flannagan, this research team’s Principal Investigator.
The next transportation revolution relies on the availability and sophisticated processing and interpretation of extensive transportation data. The first bottleneck, data collection, is no longer a challenge. If anything, we now have a massive amount of transportation data, including data on driver behavior, traffic, weather, accidents, vehicle messages, traffic signals and road characteristics. Our next challenge is how to make such data accessible to researchers and to transportation regulation agencies, and how we make sense of the data, to gain insight for future transportation research, the development of connected and automated vehicle systems, and regulations and guidelines in response to new transportation systems.
This project, led by Carol Flannagan of the U-M Transportation Research Institute, will create a data ecosystem in a high-performance computing environment to understand driver behavior, and demonstrate its value through its application to a practical research issue and the development of a specific set of analytical methods. First, the research team will build the data ecosystem on a parallel, distributed computing platform that is optimized to support a variety of Big Data analytical methods and data integration across a variety of data sources, empowering researchers and transportation agencies. The team will then demonstrate the value of the data ecosystem by developing a database of rare or unusual traffic events and a rich and natural simulation environment for such events. It is often ineffective for technology developers and regulators to test designs and regulations in response to certain rare transportation events through road tests, because these rare events are, well, rare. An enriched simulation setup will allow much more effective testing. The research team will also develop methods to integrate Information collected by different sensors, with different levels of details and quality, and across different time scales. Success of such method development not only proves the value of a particular data ecosystem, but also has broad implications in the advancement of data-intensive research in many fields because the integration of signals of different origins, qualities, levels of details and time scales is a challenge that faces researchers in many fields such as patient data analysis and social media data analysis.
The research team include researchers from the School of Public Health, College of Engineering, U-M Dearborn, College of Literature, Science and the Arts, UMTRI, and the Institute for Social Research, a perfect combination of expertise to carry out the proposed multi-disciplinary research.