Carnegie Mellon University

Zhen (Sean) Qian

Zhen (Sean) Qian

Assistant Professor, Civil and Environmental Engineering

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


Zhen (Sean) Qian joined the Department of Civil and Environmental Engineering in July 2015. He directs the Mobility Data Analytics Center (MAC) at CMU. Qian was most recently an assistant research professor jointly appointed at the Heinz College and Institute for Complex Engineered Systems.

Qian's research lies in the integration and optimization of civil infrastructure systems. The primary focus of his research is to manage aging and overcrowded transportation infrastructure systems, and to build sustainable and resilient infrastructure networks. He is particularly interested in large-scale dynamic network modeling for multi-modal transportation systems, in development of intelligent transportation systems (ITS) and in urban system interdependency modeling.

He 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.


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 

  • 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.