June 7, 2024 - Talk by Dr. Sean Qian

Sean Qian is a Full 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. 

On June 7, 2024 at 1 pm in room POT.168, he will start his talk by providing an overview of the Mobility Data Analytics Center (MAC), along with his three research thrusts on network modeling, optimal mobility service, and extreme incident prediction and proactive management. Then he will delve into details on the mesoscopic network modeling.

One challenge for any traffic network model is that, with the availability of data on all modes of transportation systems, how to make the best use of those spatio-temporal data to best understand travel patterns across those modes in high spatio-temporal resolutions. In a mesoscopic network modeling framework, we formulate and solve for spatio-temporal passenger and vehicular flows in a multi-modal network explicitly considering solo-driving, public transit, parking and ride-sharing. Vehicular flows, namely vehicles in different classifications, are integrated in a holistic dynamic network loading (DNL) model. We further propose a general formulation of multi-modal dynamic user equilibrium (MMDUE) problem considering both behavioral of travel demand and heterogeneous flow in multi-modal networks. The travel behavior models and network characteristics are learned from multi-source data, e.g. time-varying counts, speed and transit data. Machine learning (ML) techniques are employed to optimally tune network parameters to best fit the multi-source data. This framework has been applied in many use cases for regions, cities and communities to make optimal decisions. Case studies in the Columbus Ohio region will be presented to provide insights.