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Carnegie Mellon University will receive $14 million over the next five years from the U.S. Department of Transportation (U.S. DOT) to establish a new National University Transportation Center (UTC).

The center, named Mobility21, will safely and efficiently improve the mobility of people and goods in the 21st century by investigating and deploying novel technologies, incentives, policies, and training programs. The center is a partnership between the College of Engineering

Transportation costs are the second largest expense for U.S. households. We spend more than 40 hours stuck in traffic each year, and congestion costs are estimated to be $121 billion. Truck congestion alone wastes $27 billion in time and fuel, annually.

To address challenges spanning multiple modes of transportation, the College of Engineering and its consortium partners, including the Community College of Allegheny Country, University of Pennsylvania, and Ohio State University, will explore:

  • smart city technologies;
  • connected and autonomous vehicles;
  • improved transportation access to disadvantaged neighborhoods;
  • multi-modal traveling;
  • assistive technologies for people with disabilities;
  • data modeling for monitoring traffic control systems; and
  • regional planning to establish priorities and aid transportation deployment.

“This significant award from the U.S. DOT recognizes Carnegie Mellon’s national and global leadership in the computational technologies that are revolutionizing transportation. Building on the real-world experience and expertise we have established with other CMU initiatives such as Metro21 and Traffic21, this cross-disciplinary effort, led by our College of Engineering, will develop and deploy solutions that will fuel our economy, keep drivers safe, and deliver efficient and reliable transportation,” says Farnam Jahanian, provost and chief academic officer of Carnegie Mellon.

With the City and Carnegie Mellon working together, residents throughout the city will have safer, faster, and more reliable commutes.

Bill Peduto, Mayor, Pittsburgh, PA

Raj Rajkumar, the George Westinghouse Professor of ECE, leads Mobility21. Rajkumar is a globally recognized expert in autonomous vehicle research.

“Carnegie Mellon’s research has helped establish Pittsburgh and Pennsylvania as a national hub for developing safe automated vehicles and has attracted technology companies to Pennsylvania,” says Pennsylvania Department of Transportation (PennDOT) Secretary Leslie S. Richards.

PennDOT is one of a number of partners that Mobility21 will tap to deploy projects. Deployment partners will help identify real-world transportation needs, aid technology licensing and commercialization, and provide venues for testing technologies.

“Pittsburgh is a testbed for deploying new technologies that can connect communities and provide access to new opportunities. With the City and Carnegie Mellon working together, residents throughout the city will have safer, faster, and more reliable commutes,” says Pittsburgh Mayor Bill Peduto.

“Mobility21 will actively bridge the bold ideas of its research team to meet the pressing needs of our increasingly congested transportation system. The benefits of infrastructure investments can be multiplied with the infusion of innovative technologies and forward-looking policies,” adds Rajkumar, who also directs Metro21, Carnegie Mellon’s Smart and Connected City Initiative.

Mobility21 is the second national UTC located at Carnegie Mellon. Since 2013, the university has been home to the Technologies for Safe and Efficient Transportation National UTC on Safety, which develops and deploys innovations pertaining to in-vehicle technologies, infrastructure technologies, human-vehicle interactions, mobility/data analytics, and policy.

Here are some topics that engineering researchers in Mobility21 will explore:

Predicting real-time traffic congestion and mitigation at a city scale

Thanks to ubiquitous wireless connectivity, connected cars, and millions of mobile devices, we can leverage data analytics and machine learning to develop a real-time traffic prediction system and subsequently, ways to mitigate congestion in major cities.

Extracting road information from vehicle sensor data

Researchers are applying machine learning and signal-processing techniques to make sense of data gathered from vehicle sensors and stored in the cloud. This work will help address traffic challenges such as detecting pedestrians, vehicles, signage, and even potholes.

Building an accessible, low-stress, safe, and sustainable bicycle infrastructure network

Bicycles are the most efficient way to travel short distances, but to keep people cycling for the long term, there is need for systematic infrastructure planning that considers pedestrians, bicycles, and vehicles. Engineers are tapping transportation datasets to expand infrastructure networks for bikes and to provide cyclists with route information, like riding ease and safety risks.

Latency-aware cloud-based route planning

Freight trucks are a major source of greenhouse gas emissions, and because they accelerate slowly, they cause traffic delays. By minimizing left-hand turns at intersections, freight trucks and automated vehicles would realize improvements in safety, latencies, and emissions. By implementing routing policies that consider vehicle types, delay implications, hazards, and GPS availability, vehicles can be rerouted depending on their needs and destinations.

Estimating changes in parking and urban form from vehicle automation

Parking in urban areas is expensive, takes up space, and causes congestion as drivers hunt for parking spots. By examining rideshare and traveler information services, researchers will assess cost saving and changes in urban form, and the results will be generalized for large metropolitan areas.

Managing of mobility impact of utility and roadway construction through incentives

When pipelines or utility systems fail, roadway pavement is often cut to make repairs, which disrupts motorists. Researchers will study infrastructure maintenance operations to predict repairs and minimize disruptions in communities. They will also develop incentive models to help motorists improve their mobility when repairs occur.