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Please join us at the ASU Transportation Webinar on Friday, February 1, 2019, where Xuesong Zhou, associate professor at ASU will present a webinar titled, “How to integrate deep learning methods with transportation model calibration: a computational graph-based approach with multiple data sources.”
How to integrate deep learning methods with transportation model calibration: a computational graph-based approach with multiple data sources.
Presented by Xuesong, Zhou, Associate Professor, Arizona State University
Friday, February 1, 2019
Noon
Attend online via Adobe Connect
About the talk
This presentation aims to provide an understanding of the complex transportation modeling process using the emerging deep-learning approach by constructing computation graphs and calculating the derivatives among different layers while considering available heterogeneous data sources. Specifically, by recognizing the multiple sources of information in emerging big data applications, we map different levels of traffic demand variables to various data sources in multi-layer Hierarchical Flow Networks (HFN) for traffic demand estimation applications. By introducing the computational graph approach, a feed forward pass on the hierarchical network sequentially implements trip generation prediction, spatial distribution estimation and path flow-based traffic assignment, respectively. A back propagation step on the computational graph is applied to aggregate different layers of partial first-order gradients and minimize composite/non-convex error functions. Finally, the proposed methodology is tested in real-world travel demand forecasting models.
About the speaker
Xuesong Zhou serves as an associate professor in the School of Sustainable Engineering and the Built Environment at Arizona State University (ASU). His research focuses on dynamic traffic assignment, traffic estimation and prediction, large-scale routing and rail scheduling. Zhou is currently an Associate Editor of Transportation Research Part C, an Associate Executive Editor-in-Chief of Urban Rail Transit, an Associate Editor of Networks and Spatial Economics and an Editorial Board Member of Transportation Research Part B. He was the former Chair of INFORMS Rail Application Section (2016), and the Co-Chair of the IEEE ITS Society Technical Committee on Traffic and Travel Management, as well as a subcommittee chair of the TRB Committee on Transportation Network Modeling (ADB30). He is the principal architect and developer of DTALite, a lightweight open-source traffic assignment/simulation engine, and has been assisting FHWA, many state DOT and metropolitan planning agencies to learn and deploy advanced transportation network modeling tools.
About the Tomnet Transportation Center
TOMNET is a U.S. Department of Transportation Tier 1 University Transportation Center that aims to develop new methods, tools and algorithms for integrating attitudes, values and perception variables in regional transportation planning models in an effort to enhance behavioral realism and sensitivity of travel forecasts when analyzing a wide variety future scenarios. The center is led by Arizona State University and includes Georgia Tech, the University South Florida and the University of Washington as consortium partners. The center is conducting the TOMNET Leadership Webinar Series to help advance technology transfer, disseminate research and foster workforce development.
