Carnegie Mellon University

Sean Qian

Sean Qian

Henry Posner, Anne Molloy, and Robert and Christine Pietrandrea Associate Professor, Civil and Environmental Engineering

Address
Civil & Environmental Engineering
Carnegie Mellon University
Pittsburgh, PA 15213-3890

Bio

Sean Qian is 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 large-scale 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, U.S. DOE, U.S. DOT, Pennsylvania Department of Transportation (PennDOT), Maryland Department of Transportation (MDOT), Pennsylvania Department of Community and Economic Development (DCED), IBM, Honda R&D, Benedum Foundation, and Hillman Foundation. Prof.

Qian serves an Associate Editor for Transportation Research Part C: Emerging Technologies, Transportation Science, Transportmatrica B, and Journal of Public Transportation, 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 PhD degree in Civil Engineering at the University of California, Davis in 2011 and his M.S. degree in Statistics at Stanford University in 2012.  

Education

PhD 2011 - University of California Davis
MS 2012 - Stanford University
MS 2006 - Tsinghua University
BS 2004 - Tsinghua University

Research

Research Group: EESSAIS
  • Dynamic network modeling (routing, simulation and optimization)

  • Intelligent transportation system (ITS)

  • Applications of ML/AI in infrastructure management

  • Infrastructure resilience

  • Urban systems interdependency

  • Multi-modal transportation

  • Parking management

  • Transportation economics

  • Traffic operations

Mobility Data Analytics Center (MAC)

Over the last decade, new technologies and innovations in transportation systems have produced massive amounts of data, which has enabled us to better monitor, evaluate and manage our transportation systems. The rich data from various sources provides unprecedented opportunity for the transportation industry to understand travel behavior and to propose efficient management strategies. However, those data sources are usually established by disparate public agencies and private companies. They rarely communicate with each other and as a result, data is only used and analyzed for a particular piece of the transportation system, such as an intersection, a stretch of freeway or bus routes operated by the same agency. With disparate data sources, each part of the system is individually operated and clearly the entire transportation system is far from being socially optimal. 

The Mobility Data Analytics Center (MAC) aims to collect, integrate and learn from the massive amounts of mobility data and contribute to the development of smarter multi-modal multi-jurisdictional transportations systems. The ultimate objective of MAC is to:

  • Provide archived and real-time traffic data of each element of multi-modal transportation systems
  • Reveal the behavior information for both passenger transportation and freight transportation
  • Serve as a key instrument for managing transportation systems
  • Target a range of users including legislators, transportation planners, engineers, researchers, travelers and companies.

Publications

Wei Ma, Sean Qian, (2021), "High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach", Sensors, 21(2), 464. [URL]

Weiran Yao, Sean Qian, (2021), "From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data", Transportation Research Part C, Vol.124, 102938. [URL] 

Wei Ma, Xidong Pi, Sean Qian, (2020) "Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs", Transportation Research Part C, Vol.119, 102747. [URL]

Pinchao Zhang, Sean Qian, (2020) "Path-based system optimal dynamic traffic assignment: a subgradient approach", Transportation Research Part B, Vol.134, pp.41-63. [URL]

Shuguan Yang, Wei Ma, Xidong Pi, Sean Qian, (2019) "A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources",  Transportation Research Part C, Vol.107, pp. 248-265

Xidong Pi, Wei Ma, Sean Qian, (2019) "A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles, public transit and parking", Transportation Research Part C, Vol.104, pp. 369-389

Recent Awards 

  • Jan 2020: 2019 Professor of the Year, ASCE Pittsburgh Section
  • Jan 2019 - present: Henry Posner, Anne Molloy, and Robert and Christine Pietrandrea Career Development Chair
  • Nov 2018-Oct 2019: Global Future Council Fellowship, World Economic Forum
  • Mar 2018: NSF CAREER award
  • Jan 2018: 2017 Greenshields Prize
  • Dec 2013: IBM faculty award
  • Nov 2013: Berkman Faculty Development Grant, Carnegie Mellon University

Sean Qian: Mobility Data Analytics: Predicting Human Behavior to Improve Transportation Systems

We all hate when roads close—no one’s happy when the inevitable traffic jams jar our routines. But what if we could predict how road closings will affect traffic? Civil and Environmental Engineering Assistant Professor Sean Qian discusses using real-time data to predict future traffic volumes and reduce congestion and emissions.