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.

123C Baker/Porter Hall
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Sean Qian
Sean Qian’s CEE website
Sean Qian’s Heinz College website
Mobility Data Analytics Center
Sean Qian’s personal website

Mobility Data Analytics: Predicting Human Behavior to Improve Transportation Systems


2012 MS, Statistics, Stanford University

2011 Ph.D., Civil Engineering, UC Davis

2006 MS, Civil Engineering, Tsinghua University

2004 BS, Civil Engineering, Tsinghua University

Media mentions

US Department of Transportation

Qian receives grant from US DOT

CEE’s Sean Qian has received a two-year grant from the United States Department of Transportation’s Federal Highway Administration to create an AI-powered system to predict and respond to non-recurrent traffic events like accidents, severe weather, and road maintenance, among others.

CMU Engineering

From Twitter to traffic predictor

Sean Qian and Weiran Yao have used data from twitter to solve one of the greatest hurdles in traffic prediction.

CMU Engineering

Quantifying transportation relationships

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.

CMU Engineering

Optimizing ride-hailing systems

Sean Qian aims to leverage the platform of ride-hailing companies to benefit everyone in the transportation system.

CMU Engineering

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.

CMU Engineering

The future—delivered

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.

CMU Engineering

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.

CMU Engineering

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.