With the advent of driverless vehicles and ride-sharing in an age of continued urbanization, climate change and pollution, we are undoubtedly 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 to address challenges including automation, climate change and urban inequality. The MIDAS Transportation Research Hub aims to position U-M researchers at the forefront of our nation’s Big Data transportation research, develop innovative methods and tools, and apply the insight to solve real-world transportation challenges.  The MIDAS Data-Intensive Transportation Research Hub currently funds two projects.  Through a variety of hub activities, MIDAS hopes to:

  • Disseminate tools and methods to empower campus-wide transportation research.
  • Build a collaborative network of Big Data transportation researchers across the U-M campus.
  • Form industry partnerships and transform research findings into the next generation of transportation systems.

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.

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.