Abstract

The increasing complexity of transportation networks demands computational architectures that integrate data, algorithms, and scalability in a unified framework. This talk introduces a tensor-based approach to transportation network modeling and optimization, emphasizing the use of layered computational graphs to handle flows, travel times, and system-wide performance. By leveraging layered tensor operations, network flows are redefined as multi-dimensional entities, enabling efficient propagation of origin-destination (OD) flows, path probabilities, and link travel times. This architecture bridges classical optimization techniques with neural methodologies to address challenges in dynamic traffic assignment, vehicle routing, and multimodal transport systems.

Bio

Xuesong (Simon) Zhou is a Professor of Transportation Systems at the School of Sustainable Engineering and the Built Environment, Arizona State University (ASU), Tempe, Arizona. Dr. Zhou's research focuses on developing methodological advancements in multimodal transportation planning applications, including dynamic traffic assignment, traffic estimation and prediction, large-scale routing, and rail scheduling. Dr. Zhou has served as an Associate Editor of Transportation Research Part C, is currently the Executive Editor-in-Chief of Urban Rail Transit, and an Editorial Board Member of Transportation Research Part B. He has also chaired the INFORMS Rail Application Section (2016 and 2025) and currently serves as a subcommittee chair of the TRB Committee on Transportation Network Modeling (AEP40).

Dr. Zhou is the Director of the ASU Transportation+AI Lab, where he is the principal architect and programmer for several open-source packages, including DTALite, NEXTA, and OSM2GMNS, which have collectively received over 100,000 downloads and many system deployments at various metropolitan planning agencies and state DOTs. He has published over 100 papers in Transportation Research Part B, Transportation Research Part C, and other leading transportation journals, with an H-index of 60 and a total of 11,000 citations in Google Scholar.

In addition to his academic achievements, Dr. Zhou is passionate about connecting practitioners, researchers, academics, students, and others involved in transportation planning and travel modeling. He serves as the conference chair for the TRB Innovations in Travel Analysis and Planning Conference in 2023, and a board member of Zephyr Foundation, a non-profit organization dedicated to advancing transportation research and education.

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