Sean Qian is the Henry Posner, Anne Molloy, and Robert and Christine Pietrandrea Associate Professor jointly appointed at the Department of Civil and Environmental Engineering (major) and Heinz College of Information Systems and Public Policy (minor) at Carnegie Mellon University (CMU).
He directs the Mobility Data Analytics Center (MAC) at CMU. Qian’s research interest lies in large-scale dynamic network modeling and big data analytics for multi-modal transportation systems, in development of intelligent transportation systems (ITS) and in understanding infrastructure system interdependency.
His research has been supported by a number of public agencies and private firms, such as NSF, DOE, FHWA, Pennsylvania Department of Transportation (PennDOT), Pennsylvania Department of Community and Economic Development (DCED), IBM, Benedum Foundation, and Hillman Foundation.
Professor Qian serves an associate editor for Transportation Research Part C: Emerging Technologies, and an editorial board editor for Transportation Research Part B: Methodological, and is an active member of the Network Modeling Committee of Transportation Research Board.
He is the recipient of the NSF CAREER award in 2018 and Greenshields Prize from the Transportation Research Board in 2017. Qian was a postdoctoral researcher in the Department of Civil and Environmental Engineering at Stanford University from 2011 to 2013, and received his Ph.D. degree in civil engineering at the University of California, Davis in 2011 and his MS degree in statistics at Stanford University in 2012.
Mobility Data Analytics: Predicting Human Behavior to Improve Transportation Systems
From Twitter to traffic predictor
CEE’s Sean Qian and his Ph.D. student Weiran Yao have used data from twitter to solve one of the greatest hurdles in traffic prediction.
Quantifying transportation relationships
CEE’s Sean Qian studied the relationship between Uber and public transportation, proving it can vary by time of day and location.
Qian cited in Forbes on increasing ride-hailing service efficiency
CEE’s Sean Qian was cited in Forbes about how to increase ride-hailing service efficiency. He believes this can be achieved by incentivizing drivers to take specific paths and riders to avoid travel-heavy times.
Optimizing ride-hailing systems
CEE’s Sean Qian aims to leverage the platform of ride-hailing companies to benefit everyone in the transportation system.
Improving ridesharing predictions
Sean Qian, Director of the Mobility Data Analytics Center, partnered with Gridwise, a local startup company, to optimize ridesharing platforms.
Qian’s team creates AI system to predict parking occupancy
A paper published by CEE’s Sean Qian, Xidong Pi, Wei Ma, and Shuguan Yang on an AI system the team created to predict parking occupancy in real time was the subject of a recent story in VentureBeat.
CEE’s Costa Samaras and Sean Qian will head a DOE-funded analysis of next-generation delivery networks composed of aerial drones, robots, AVs, EVs, and “intelligent delivery zones.”
Qian and Zhang correlate traffic conditions with energy usage
CEE’s Sean Qian and Ph.D. student Pinchao Zhang were featured by GCN for their recent project that used nighttime and morning energy usage to predict traffic conditions.
Qian interviewed on research on nighttime energy and morning traffic
CEE’s Sean Qian was interviewed about his recent study that analyzes how nighttime energy use may help predict the following morning’s traffic. Using data from the Austin, Texas metropolitan area, Qian and his team found that eight out of 10 patterns had an effect on highway traffic.
Can nighttime energy use predict morning traffic?
To predict when morning traffic is likely to grind to a halt, it may be more effective to examine how we use electricity in the middle of the night instead of travel-time data.
Ride on through
Sean Qian receives NSF award to combine big data and transportation system modeling to improve traffic flow and the way we create and operate transportation systems.
Uber, CMU launch traffic data-sharing tool
CMU’s Traffic21 researchers are using Uber Movement to help infrastructure operation and planning decisions in Pittsburgh.