With the advent of driverless vehicles and ride-sharing in an age of continued urbanization, climate change and pollution, there is no doubt that we are at the brink of the next transportation revolution. Data science is at the center of this revolution. The collection of data on transportation and driver behavior is no longer a bottleneck; our current challenge is to develop sophisticated data analysis and interpretation that impact the design of future transportation modes and systems to address challenges including automation, climate change and urban inequality. The Center for Data-Intensive Transportation Research at MIDAS aims to position U-M researchers at the forefront of our nation’s transportation research, through the development of innovative methods and tools for Big Data, and the application of the insight gained through Big Data research to the design of the next generation of transportation systems.

The launch of the Center for Data-Intensive Transportation Research coincides with the funding of two projects through the MIDAS Challenge Initiative. One 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.  The other project, “Reinventing Public Urban Transportation and Mobility,” led by Pascal Van Hentenryck of the College of Engineering, will use predictive models based on high volumes of diverse transportation data to design an on-demand public transportation system for urban areas with a fleet of connected and automated vehicles, including buses, light-rail, shuttles, cars and bicycles.

With these two projects as the initial core research, the continuing work of the Center for Data-Intensive Transportation Research at MIDAS includes:

  • Disseminating tools and methods, as soon as they become available, to empower campus-wide transportation research.
  • Building a collaborative network of Big Data transportation researchers across the U-M campus.
  • Forming industry partnerships and transform research findings into the next generation of transportation systems.

If you are interested in finding out more about collaboration, partnership and resources, please contact us: midas-contact@umich.edu.

Research Areas


Reinventing Public Urban Transportation and Mobility

Let’s imagine what a public urban transportation system for the future will look like: There will be a network of bus stops and transit stations strategically positioned connecting each neighborhood to the city’s business districts, hospitals and shopping centers.  There will be a fleet of buses, light-rail, shuttles, driverless cars and bikes, all controlled by an intelligent management system.  The system analyzes real-time data on the number of passengers at each location and their destinations and traffic conditions, and deploys the fleet to take every passenger to his/her destination in the most efficient and economical way.  No more walking for two miles before getting to the nearest bus stop.  No more congestion at the end of a workday.  No more loss of employment opportunities because two communities are not connected by public transportation.

Building a Transportation Data Ecosystem

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.