SPEAKER：柯锦涛 助理教授，香港大学

TIME/DATE: 2025.7.2   10:00

CLASSROOM: A1201

ABSTRACT:

On-demand food delivery (OFD) services have experienced a significant surge in popularity in recent years, which poses various operational challenges for service operators such as Meituan, Doordash, among others. Many studies have been established to investigate this new type of urban transportation. However, the OFD related literature is still at an early stage where there lacks fundamental study in decision-making analysis, network equilibrium analysis, empirical analysis, etc. In this talk, three recent research works of my research group will be introduced. First, an analytical model will be presented. It captures the complex interplay of the OFD system by considering adjustable service region size, order bundling, and batch-matching processes. Various managerial insights are derived in mathematical language and depicted in numerical experiments. Second, the OFD service is analyzed in a network context, where the spatial heterogeneity and network effects are well considered. In the network model, drivers’ traveling behavior appear to be more complex since it involves both individual choice behaviors and the dispatching decisions from the platform. The three market players and a central platform are modeled in a Stackelberg leader-follower game structure where their behaviors and the network matching equilibrium are analyzed. Finally, we study the uncertainty of the OFD service, specifically on the customers’ order cancellation behaviors. Our findings reveal distinct cancellation patterns across different stages of the order fulfillment process: the matching and pick-up stage.

GUEST 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. The vision of his research is to develop novel models, algorithms, and conduct data-driven quantitative analyses to better manage, operate, and regulate various types of emerging mobility services. 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.