The Mobility Data Analytics Center (MAC) began in 2013 with funding from the Technologies for Safe and Efficient Transportation (T-SET) National University Transportation Center (UTC) and continued with support from the Mobility21 National UTC. Director Dr. Zhen (Sean) Qian created the MAC with goals to achieve more efficient, resilient, safe, and environmentally-friendly mobility. Dr. Qian is an expert in intelligent transportation systems, Assistant Professor in the Department of Civil and Environmental Engineering with a joint appointment to the Heinz College, and a T-SET/Mobility21 UTC researcher. For the last few years, MAC has used massive amounts of mobility data to develop and deploy smarter transportation systems.
One of the many accomplishments of the MAC includes the development of a centralized data engine that takes an array of mobility data, including crowdsourced and real-time data, and translates it into useful information for policy makers, planners, traffic managers, engineers, and many others. This centralized data engine will help connect previously disparate agencies and municipalities by providing an integrated platform that gives cities, boroughs, and townships the ability to fully utilize the available data to make better planning decisions.
MAC is currently working with public and private deployment partners to conduct research and develop decision-making tools for better management of transportation systems, and to create products or services that improve travelers’ experience. Their research is helping the City of Philadelphia to predict the traffic impact of road closures and plan alternative routes to reduce congestion and emissions. It’s also helping the City of Pittsburgh to predict traffic impacts, and to make the city more ‘bikeable.’
In 2016, MAC created a network model on behalf of PennDOT to predict real-time traffic evolution for all highways and major arterials in the Philadelphia Metro area. Additionally, by optimizing the messages shown on dynamic message signs, MAC was also able to reroute and improve Philadelphia’s traffic flow.
To improve bike infrastructure in Pittsburgh, the MAC is developing an optimization model that analyzes safety, traffic flow volume and speed, ride easiness, and bus coverage to determine ‘bikeability’ scores in Pittsburgh. The web application has been shared with the Department of Mobility and Infrastructure at the City of Pittsburgh, PennDOT, the Port Authority of Allegheny County, the Southwest Pennsylvania Commission, and Healthy Ride Pittsburgh. The resulting research has great potential to impact travelers’ daily transportation decisions, promoting cycling and ensuring greater safety for Pittsburgh cyclists, and changing long-term transportation planning.
In March of 2016, the City of Pittsburgh was named a finalist in the U.S. DOT’s Smart City Challenge. MAC worked closely in support of this effort to help Pittsburgh bring the DOT’s Smart City vision to life. While Pittsburgh lost the challenge to our neighbors in Columbus, Ohio, CMU’s T-SET hosted its own Smart Mobility Challenge in 2017. Dr. Qian and MAC are assisting three of the local projects that won the challenge, including projects in Townships of Cranberry and Mt. Lebanon, and the Boroughs of Dormont, and McKees Rocks.
This is not the first partnership between CMU and Cranberry Township, located at the junction of Interstate 79 and the Pennsylvania Turnpike. Cranberry has been a deployment partner since installing the region’s first set of Dedicated Short-Range Communications (DSRC) radios at the intersection of routes 19 and 228 in 2013. Located only 20 miles north of Downtown Pittsburgh in Butler County, Cranberry Township is home to 30,000, but the previously noted intersection supports 120,000 vehicles per day.
Kelly Maurer, Project Coordinator for Cranberry’s Engineering and Environmental Services, and Marty McKinney, Manager of Cranberry Traffic Operations shared some of the daily traffic challenges faced by their township. When there’s a backup on the Turnpike or routes 19 or 228, drivers exit onto the local road systems; “the local roads can’t handle the additional load,” says Marty, “and Sean [Qian] is helping us to mitigate some of the issues that come with that.” This is particularly true when those traffic events are unexpected.
Dr. Qian is using crowdsourced data to support the Township’s Coordinated Traffic Signal System. Using social media, real-time and historical traffic information from INRIX, the Coordinate Signal System will be able to trigger predictions into traffic delays which can be used in dynamic message boards and other communications to alert the public of potential delays. The system collects data from not just Cranberry Township, but from the region, allowing Cranberry to see how incidents in further location affect local traffic the future and be resilient to them.
The Coordinated Traffic Signal System will be used to mitigate traffic in Cranberry Township, but also will improve traffic conditions in the three townships sharing inter-municipal agreements, including one in Allegheny County. The benefits experienced in Cranberry Township reach farther than those three townships, though.
Research and deployments have been made possible by continued funding from Carnegie Mellon, the National Science Foundation, the U.S. Department of University Transportation Center Program, the Hillman Foundation the Benedum Foundation, the Pennsylvania Department of Transportation, PITA, the Pittsburgh Department of Public Works, TomTom, and many more. MAC receives continued support from CMU’s T-SET and Mobility21 National UTCs.