12 Dec 2024 (Thu) 11:00 AM-12:30 PM | Seminar by Dr. Qiaochu He

Autonomous driving: Queueing game, information design and human-AI interactions

Speaker:

Dr. Qiaochu He, Southern University of Science and Technology

Date:    12 December 2024 (Thursday)

Time:   11:00 am - 12:30 pm

Venue:  Innovation Wing Two, G/F Run Run Shaw Building, HKU

Abstract

I am presenting recent research on service operations and business models within a mixed autonomy paradigm, focusing on navigation algorithms influenced by differentiated information and driver-algorithm interactions. Leveraging queueing game models, Bayesian persuasion-based information design, and reinforcement learning for experimental validation, this research examines complex traffic flows, platform operation strategies, the effectiveness of intelligent navigation models, and behavioral patterns in human-machine interaction. These efforts aim to enhance traffic system efficiency and social welfare, thereby advancing the development and application of autonomous driving technologies.

Specifically, I will present findings from five (working) papers. At the micro level, we had a POM paper explores the interaction between autonomous vehicles (AVs) and human-driven vehicles (HVs) using queueing game frameworks. In another POM paper, we proposed smart navigation algorithms through information design methods to mitigate traffic congestion in routing games. A third working paper extends this framework to incorporate targeted algorithms for further theoretical development. Additionally, two ongoing projects employ experimental economics and artificial intelligence (AI) techniques to investigate behavioral interactions between navigation algorithms and human drivers, thereby validating theoretical models of human-AI interaction. These experiments also assess whether AI algorithms can optimize traffic flow via implicit cooperation, even in the absence of centralized control. Furthermore, the studies attempt to explain algorithm aversion behaviors through a dynamic information design framework.

Speaker Bio:

Qiao-Chu He is a tenured Associate Professor at the SUSTech Business School. His primary research interests lie in operations management, with applications in areas such as smart cities, marketplaces and platforms, and human-algorithm/AI interactions. He serves as the principal investigator for several research projects funded by the National Natural Science Foundation of China (NSFC), the National “Ten-Thousand” Program for Young Scholars (2019), and the Hong Kong General Research Fund (HK-GRF). He holds a Bachelor of Engineering from Tsinghua University and a Ph.D. in Operations Research from the University of California, Berkeley. He has previously taught at HKUST and UNC Charlotte. His students hold faculty positions at Business School including HUST (Huazhong), USTC (Hefei) and the University of Liverpool (UK).

Student Seminar by Dr. Zhuoye Zhang on Nov 29, 2024 (4PM)

Title: Optimal operation strategies of an urban crowdshipping platform in asset-light, asset-medium, or asset-heavy business format

Speaker: Dr. Zhuoye Zhang (Department of Data and Systems Engineering)

Date: November 29, 2024 (Friday)

Time: 2:00 pm - 3:00 pm

Venue: Room 612B, 6/F, Haking Wong Building, The University of Hong Kong

About the talk: This research investigates the operation strategies of an urban crowdshipping platform, which utilizes the latent capacity of the traveling ‘crowd’ in the transportation system to facilitate parcel delivery. We develop an analytical model to characterize the decision-making and operation strategies of a crowdshipping operator in alternative business formats (asset-light/medium/heavy). Asset-light platforms connect customers with potential carriers in the crowd without involving delivery assets, whereas asset-medium and asset-heavy operators integrate crowd carriers with outsourced or owned delivery fleets, respectively. In particular, we first formulate the two-sided market equilibrium of crowdshipping system on account of customers’ willingness to use and crowds’ willingness to serve. Based on the market equilibrium, the crowdshipping operator’s optimal strategies in terms of pricing and/or fleet sizing are identified for profit-maximization or social welfare-maximization in alternative business formats. We show that the introduction of crowdshipping can simultaneously improve the benefits of logistics customers, the crowd, and the crowdshipping platform operator, leading to a win-win-win outcome. Furthermore, we establish analytical conditions for one business format being superior to another. We find that if the externality (or marginal social cost) of an unmatched order is smaller in a particular business format, it will result in larger consumer surplus for customers, greater net benefit for crowd carriers, and more profit for crowdshipping operator. Under mild conditions, the crowdshipping operator adopting the asset-light or asset-medium format can earn a positive profit at the social optimum.

About the speaker: Dr. Zhuoye Zhang is currently a postdoctoral researcher at the Department of Data and Systems Engineering, The University of Hong Kong (HKU). He received his Ph.D. degree in transportation engineering from HKU in 2024, his master's degree from Shanghai Jiao Tong University, and his bachelor's degree from Tongji University. His research interests include transportation economics, transportation network modeling and optimization, mainly focusing on the analysis of shared, automated and electrified transportation and logistics services. His work has been published in leading academic journals and conferences, including Transportation Research Part B/C, the International Symposium on Transportation and Traffic Theory (ISTTT), the Transportation Research Board (TRB) Annual Meeting, etc. Dr. Zhang is also a recipient of the Best Paper Award of the 5th Frontier Symposium on Traffic Behavior and Transportation Science (TBTS 2024), and the 14th Workshop on Computational Transportation Science (CTS).

18 Nov 2024 (Mon) 11:00-12:00 | Seminar by Prof. Peng Chen

Mileage-Based Tax in the US: Theory, Research and Practice

Speaker:

Prof. Peng Chen

School of Public Affairs, University of South Florida, USA

Date:    Nov 18, 2024 (Monday)

Time:   11:00am – 12:00nn

Venue:  Room 612B, 6/F Haking Wong Building, The University of Hong Kong

Abstract

This study reviews the theoretical foundations, empirical research, and emerging practice of mileage-based taxation (MBT) in the US. The theory section introduces economic perspectives from neoclassical, behavioral, and institutional traditions. Examining key issues from these perspectives advances the understanding of environmental benefits, data privacy, public acceptance, and the implications for equity and efficiency of MBT. The research section synthesizes recent studies on the financial, environmental, and social impacts of MBT, highlighting that transportation equity concerns are central to ongoing debates and deserve further exploration. The practice section analyzes empirical cases by comparing pilot programs and existing policies across various states, identifying reasons for differences in implementation and outcomes. This nuanced understanding of MBT underscores its potential as an effective policy tool for sustainable transportation and provides valuable insights for future research and implementation.

About the Speaker

Dr. Chen is an Assistant Professor in the School of Public Affairs at the University of South Florida, specializing in Transportation Planning and Policy with a focus on interdisciplinary research that integrates urban planning, transportation engineering, and quantitative analysis. He serves as an Associate Editor for Transportation Research Part D: Transport and Environment and received the Best Paper Award at the 14th World Conference on Transport Research. His research is funded by prominent organizations, including the National Institute for Congestion Research and the U.S. Department of Education. Dr. Chen earned his Ph.D. in Urban Planning from the University of Washington in 2016, along with a Master’s in Civil Engineering (2015) and a graduate certificate in Statistics (2014), having previously obtained a Master’s in Geography from Peking University (2010) and a Bachelor’s in Engineering from Wuhan University (2007). He has been recognized as one of the top 2% of highly cited researchers by Elsevier and Stanford University (annual ranking) since 2020.