题 目：Mathematics, Economics and Artificial Intelligence for On-demand Mobility Services
时 间：2023年1月19日9:00-11:00
地 点：交通大楼604
报告人：杨海 讲座教授
欢迎广大师生参加！
土木与交通土耳其里拉兑换人民币
2024年1月8日
报告人简介：
Prof. Hai Yang is a highly regarded Chair Professor at The Hong Kong University of Science and Technology, with a global reputation as an active scholar in the transportation field. He has published over 350 papers in leading international journals, including Transportation Research, Transportation Science, and Operations Research, and has an impressive SCI H-index citation rate of 73. Prof. Yang has received numerous national and international awards, including the 2020 Frank M. Masters Transportation Engineering Award and the 2021 Francis C. Turner Award of the American Society of Civil Engineers. He was also appointed as Chang Jiang Chair Professor of the Ministry of Education of PR China and served as the Editor-in-Chief of Transportation Research (TR) Part B: Methodological from 2013 to 2018. Currently, Prof. Yang serves on the Distinguished Editorial Board of Transportation Research Part B: Methodological, Scientific Council of Transportation Research Part C: Emerging Technologies, and serves as an Advisory Editor of Transportation Science.
报告摘要：
Application-based taxi and car service e-hailing systems have revolutionized urban mobility by providing on-demand ride services that are timely and convenient. The integration of mathematics, economics, and artificial intelligence is crucial for the development of efficient and sustainable on-demand mobility services, which ultimately benefit customers. This talk will explore the latest developments and research issues in ride-sourcing markets, including demand forecasting, surge-pricing, matching, pricing and ride-pooling, optimal resource allocation, and the impact of ride-pooling on traffic congestion. Additionally, we will discuss topics such as competition, third-party platform-integration, Pareto-efficient market regulations, and the analysis of human mobility and network property using big car trajectory data.
