Carnegie Mellon University

Sean Qian

Sean Qian

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

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


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 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 PhD degree in Civil Engineering at the University of California, Davis in 2011 and his MS degree in Statistics at Stanford University in 2012. 


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


Research Group: EESSAIS
  • Dynamic large-scale network modeling
  • Intelligent transportation system (ITS)
  • Urban systems interdependency
  • Parking management
  • Infrastructure resilience
  • Multi-modal transportation modeling
  • Transportation economics and policy
  • 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.


Qian, S., and Rajagopal, R. (2014). "Optimal Dynamic Parking Pricing for Morning Commute Considering Expected Cruising Time" Transportation Research Part C, Vol.48, pp.468-490.

Qian, S., and Rajagopal, R. (2014). "Optimal occupancy-driven parking pricing under demand uncertainties and traveler heterogeneity: a stochastic control approach" Transportation Research Part B, Vol.67, pp.144-165.

Qian, Z. S., and Zhang, M. (2012). "On centroid connectors in static traffic assignment: their effects on flow patterns and how to optimize their selections" Transportation Research Part B, Vol.46(10), pp. 1489-1503.

Qian, Z. S., Xiao, F., and Zhang, M. (2012). "Managing morning commute with parking" Transportation Research Part B, Vol.46(7), pp. 894-916.

Qian, Z. S., Shen, W., and Zhang, M. (2012). "Solving path-based system-optimal dynamic traffic assignment considering queue spillback" Transportation Research Part B, Vol.46(7), pp. 874-893.

Qian, Z. S., Xiao, F., and Zhang, M. (2011). "The economics of parking provision for the morning commute" Transportation Research Part A, Vol.45(9), pp. 861-879

Qian, Z. S., and Zhang, M. (2011). "Modeling multi-modal morning commute in a one-to-one corridor network" Transportation Research Part C, Vol. 19(2), pp. 254-269

Recent Awards 

  • 2018: National Science Foundation CAREER Award
  • 2017: Transportation Research Board Greenshields Prize 
  • 2013: Berkman Faculty Development Grant
  • 2013: IBM Faculty Award
  • 2009: Sustainable Transportation Center Dissertation Fellowship

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.