Seminar: A Large-scale Simulation Platform and AI-driven Operational Strategies for On-demand Ride Services

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.

Bio: 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
Intelligence 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.