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SUMMARY:Seminar: AI-empowered Digital Twin for Traffic Safety Analysis
DESCRIPTION:Abstract: Traffic safety research faces the paradox of rarity—the most critical crashes occur so infrequently that passive methods relying on historical records cannot accurately estimate traffic risks. This talk first reviews current methodologies and highlights their limitations in handling rare\, high-risk events. Building on these insights\, we chart a new path that fuses active safety analysis of near-miss events and generative AI. As a preliminary foundation for the generative AI–enabled safety analysis\, our recent work provides digital-twin–enabled active safety analysis through three pivotal components: (1) a physics-grounded active safety module that identifies near-miss events across diverse traffic contexts; (2) group-wise interaction modeling that captures multi-agent behaviors in traffic; and (3) a high-fidelity digital twin integrating detailed vehicle dynamics\, tire–road interaction\, and the above active-safety and interaction models. Together\, these elements enable proactive risk prevention\, expanding safety analysis beyond observed outcomes to the full spectrum of what could happen\, and lay the foundation for Vision Zero.\n\nBio: Dr. Yang Zhou is an assistant professor in the Zachry Department of Civil and Environmental Engineering at Texas A&M University\, and career initiation fellow of Texas A&M Institute of data science. He received his Ph.D. degree and Master degree from the University of Wisconsin-Madison and the University of Illinois at Urbana and Champaign\, respectively. Before joining Texas A&M\, Yang worked as a postdoctoral research associate supported by the Department of Civil and Environmental Engineering\, University of Wisconsin-Madison.  Yang has over ten years of experience in connected automated vehicle control and analysis\, traffic flow analysis\, AI applications on transportation\, and high-fidelity simulation. Yang has PIed multiple federal and local grants such as FHWA-EAR and SS4A. Yang has published more than 70 top-tier transportation journals\, including Transportation Research Part B\, Transportation Research Part C\, and IEEE Transactions on Intelligent Transportation Systems.
URL:https://c2smart.engineering.nyu.edu/event/seminar-ai-empowered-digital-twin-for-traffic-safety-analysis/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
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DTSTART;TZID=America/New_York:20251202T123000
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DTSTAMP:20260410T023635
CREATED:20251107T191349Z
LAST-MODIFIED:20251107T191420Z
UID:90272-1764678600-1764685800@c2smart.engineering.nyu.edu
SUMMARY:AECOM Fireside Chat & Panel
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/cmaa/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
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DTSTART;TZID=America/New_York:20260109T140000
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DTSTAMP:20260410T023635
CREATED:20251201T170925Z
LAST-MODIFIED:20251201T170925Z
UID:90461-1767967200-1767970800@c2smart.engineering.nyu.edu
SUMMARY:Seminar: A Large-scale Simulation Platform and AI-driven Operational Strategies for On-demand Ride Services
DESCRIPTION:Abstract: In this work\, we develop a novel multi-functional and open-sourced simulation platform for on-demand ride service operations\, which can simulate the behaviors and movements of various agents (including drivers and passengers) on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms\, especially reinforcement learning algorithms\, for a variety of tasks\, including on-demand matching\, idle vehicle repositioning\, and dynamic pricing. Evaluated by experiments based on real-world datasets\, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations. \nBio: Dr. Jintao Ke is an Assistant Professor in the Department of Civil Engineering at the University of Hong Kong (HKU). Dr. Ke received his B.S. degree (2016) in Civil Engineering from Zhejiang University\, and his PhD degree (2020) in Civil and Environment Engineering from Hong Kong University of Science and Technology. His research interests include on demand mobility services\, transportation big data analytics\, multimodal transportation system optimization\, transportation pricing\, spatiotemporal traffic prediction\, etc. He has published more than 50 SCI/SSCI indexed research papers in top-tier journals in the field of transportation research and data mining\, such as Transportation Research Part A-F\, IEEE Transactions on\nIntelligence Transportation System\, IEEE Transactions on Knowledge and Data Engineering\, IEEE Internet of Things\, Computer-Aided Civil and Infrastructure Engineering. He has been ranked as the World's Top 2% most-cited scientists by Stanford University since 2023. He is serving as an Editorial Board Member of Transportation Research Part C\, Transportation Research Part E\, and Travel Behavior and Society.
URL:https://c2smart.engineering.nyu.edu/event/seminar-a-large-scale-simulation-platform-and-ai-driven-operational-strategies-for-on-demand-ride-services/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
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DTSTART;TZID=America/New_York:20260408T173000
DTEND;TZID=America/New_York:20260408T190000
DTSTAMP:20260410T023635
CREATED:20260324T161026Z
LAST-MODIFIED:20260326T180459Z
UID:90686-1775669400-1775674800@c2smart.engineering.nyu.edu
SUMMARY:Women in Transportation 2026
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/women-in-transportation-2026/
CATEGORIES:Seminars,Student Events
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