University of Michigan projects using ‘big data’ to solve future transportation issues


Two new data science projects at the University of Michigan (U-M) will be working toward solving the major problems facing transportation in the future, by developing on-demand, driverless public buses and data-driven accident avoidance systems, with the eventual aim of creating ‘smart’ traffic systems that dramatically reduce emissions and congestion.

Supported by the Michigan Institute for Data Science (MIDAS) Challenge Initiatives program and UM-Dearborn, the projects bring together interdisciplinary teams of researchers from both campuses to work with massive amounts of data being produced by automated and connected vehicle testing sites, as well as in conventional driver-directed settings, in Ann Arbor and around the country.

The first project, ‘Reinventing Public Urban Transportation and Mobility’, led by Pascal Van Hentenryck of the College of Engineering, will help design and operate an on-demand, multimodal public transportation system for urban areas, in which a fleet of connected and automated vehicles are synchronized with buses, light rail, shuttles, cars and bicycles, using predictive models based on high volumes of diverse transportation data. The project aims to address the ‘first-mile/last-mile’ problem; the challenge of getting people from their homes or final destinations into the transit system. The goal is to begin testing on the U-M campus within a year, and will then expand to Ann Arbor and Detroit.

Van Hentenryck said, “One of the goals is to make public transportation a viable option for getting to and from work, health care, and other services, for people who can’t afford to own a car. We’re trying to revolutionize mobility for entire population segments with poor access to transportation. On-call, affordable public transportation that can get you to and from work or the doctor’s office efficiently, would increase employment opportunities and result in better health care outcomes. The potential for improved quality of life is huge.”

The other project, ‘Building a Transportation Data Ecosystem’, led by Carol Flannagan of the U-M Transportation Research Institute, will create a system allowing researchers to access massive, integrated datasets on transportation in a high-performance computing environment, which will support future transport research and development.

Flannagan said, “Creating a common repository of transportation data, including data on driving, traffic, weather, accidents, vehicle messages, traffic signals and road characteristics, will inform the development of connected and automated vehicle systems of the future. For example, real-world and simulated data on vehicle accidents will be invaluable to federal regulators developing regulations and guidelines for crash avoidance technology in the new generation of automated and connected vehicles.”

MIDAS co-director Brian Athey, professor and chair of computational medicine and bioinformatics, commented, “These interdisciplinary projects will push innovation in data science and transportation research in ways that will have long-term impact on the way people and goods will move for years to come. This includes studying the social and behavioral side of the equation. For example, will drivers and riders accept automated and connected vehicles?”

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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).