Session prochaine (session hybride: présentiel + visioconférence)

Date: 7 juin 2024
Lieu: Batiment Bienvenüe, et en visioconférence (Zoom).

Programme:

14h00	Professor Sharon Di, Associate Professor at Columbia University, New York.
	AI for Urban Transportation Digital Twin.
	Abstract: Transportation digital twins have become increasingly popular tools to improve traffic efficiency and safety. However, the majority of effort nowadays is focused on the “eyes” of the digital twin, which is object detection using computer vision. I believe the key to empowering the intelligence of a transportation digital twin lies in its “brain,” namely, how to utilize the information extracted from various sensors to infer traffic dynamics evolution and devise optimal control and management strategies with real-time feedback to guide the transportation ecosystem toward a social optimum. My research aims to employ tools including machine learning and game theory to develop an urban transportation digital twin, leveraging data collected from the NSF PAWR COSMOS city-scale wireless testbed being deployed in West Harlem next to the Columbia campus. In this talk, I will primarily focus on two solutions: (1) scientific machine learning that leverages both domain knowledge and available data, and (2) mean field game that bridges the gap between micro- and macroscopic behaviors of multi-agent dynamical systems. In the first topic, physics-informed deep learning will be introduced and applied to traffic state estimation and uncertainty quantification. In the second topic, I will introduce how to model behaviors of new actors (e.g., a large number of autonomous vehicles) in a transportation system and their interaction with existing actors (e.g., human-driven vehicles).

Sharon Di, Associate Professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University in the City of New York. She also serves on the committee of Columbia's Center for Smart Cities in the Data Science Institute.