Data-friendly mesoscopic network modeling: learning, prediction, and decision making
A Warren Distinguished Lecture with
Sean Qian
Civil and Environmental Engineering
Carnegie Mellon University
ABSTRACT
With the availability of various data sources across all modes of transportation systems, it remains a challenge how to take advantage of those diverse spatio-temporal data to best understand travel patterns across those modes in high spatio-temporal resolutions. In a mesoscopic network modeling framework, Qian formulates and solves for spatio-temporal passenger and vehicular flows in a multi-modal network explicitly considering solo-driving, bus, metro, parking, curb use and ride-sharing. Vehicular flows, namely vehicles in different classifications, are integrated in a holistic dynamic network loading (DNL) model. Qian further develops a general formulation of heterogeneous flow in their respective choices of modes, facilities and time. Through a computational graph approach, travel behavior models and network characteristics can be jointly learned from a generic set of data, for example, time-varying counts, speeds, census, transit data, and curb use data. Machine learning (ML) techniques are employed to optimally tune generic parameters to fit the multi-source high-granularity data. This framework has been applied in many use cases for regions, cities and communities to make optimal decisions in transportation planning. The mesoscopic modeling approach can also be applied to real-time traffic operations, particularly early anomaly detection and proactive traffic management.
SPEAKER
Sean Qian is H. John Heinz III Professor of Civil and Environmental Engineering at Carnegie Mellon University (CMU). He is jointly appointed at the Department of Civil and Environmental Engineering, the Heinz College of Information Systems and Public Policy, and the Department of Electrical and Computer Engineering (by courtesy). He directs the Mobility Data Analytics Center (MAC) at CMU. In 2020, Qian founded a CMU technology spinoff firm, TraffiQure Technologies, to commercialize AI/ML technologies in the infrastructure and mobility service domain. Qian's research interest lies in large-scale dynamic network modeling and AI/ML applications for multi-modal transportation systems, in development of intelligent transportation systems (ITS) solutions 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, PennDOT, Maryland SHA, IBM, Honda Research Institute, Fujitsu Research, Hitachi Rail, Benedum Foundation, and Hillman Foundation. Qian serves an Associate Editor for Transportation Research Part C: Emerging Technologies, Transportation Science, Transportmatrica B, and Journal of Public Transportation, and is an editorial board editor for Transportation Research Part B: Methodological. He is an active member of the Computational Methods and Analytics Committee of TRB and the AI Committee of ASCE. He is the recipient of ASCE Francis Turner Award in 2026, the NSF CAREER award in 2018 and Greenshields Prize from TRB 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.
