Student Seminar by Dr. Zhuoye Zhang on Nov 29, 2024 (2PM)

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).

Academic Exchange Seminars with Tongji University Delegation on Oct 16, 2024

Invited by ITS Fellow Dr. Jintao Ke, a delegation of 2 teachers and 14 research students from the College of Transportation Engineering, Tongji University visited the Department of Civil Engineering of HKU on Oct 16, 2024. In the 2-hour academic sharing session, Yijun Liu (Tongji University), Jian Liang (HKU), Hao Zhong (Tongji University), Taijie Chen (HKU), Weihan Bi (Tongji University), and Jiguang Wang (HKU) presented their studies. Each presentation was followed by a Q&A session for in-depth discussions about the research. Dr. Jintao Ke was presented a souvenir from the delegation.

Student Seminar by Shaocheng Jia on Sep 27, 2024 (10AM)

Title: Uncertainty Estimation of the Connected Vehicle Penetration Rate: Modeling Complex Residual-vehicle Effects

Speaker: Mr. Shaocheng Jia (Department of Civil Engineering)

Date: September 27, 2024 (Friday)

Time: 10:00 am - 11:00 am

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

About the talk: In the transition to full deployment of connected vehicles (CVs), the CV penetration rate plays a key role in bridging the gap between partial and complete traffic information. Several innovative methods have been proposed to estimate the CV penetration rate using only CV data. However, these methods, as point estimators, may lead to biased estimations or suboptimal solutions when applied directly in modeling or system optimization. To avoid these problems, the uncertainty and variability in the CV penetration rate must be considered. Recently, a probabilistic penetration rate (PPR) model was developed for estimating such uncertainties. The key model input is a constrained queue length distribution composed exclusively of queues formed by red signals in undersaturation conditions with no residual vehicles. However, in real-world scenarios, due to random arrivals, residual vehicles are commonly carried over from one cycle to another in temporary overflow cycles in undersaturation conditions, which seriously restricts the applicability of the PPR model. To address this limitation, this talk will introduce a Markov-constrained queue length (MCQL) model that can model the complex effects of residual vehicles on the CV penetration rate uncertainty. Comprehensive VISSIM simulations and applications to real-world datasets demonstrate that the proposed MCQL model can accurately model the residual-vehicle effect and estimate the uncertainty. Thus, the applicability of the PPR model is truly extended to real-world settings, regardless of the presence of residual vehicles. A simple stochastic CV-based adaptive signal control example illustrates the potential of the proposed model in real-world applications.

About the speaker: Shaocheng Jia is currently a Ph.D. candidate at The University of Hong Kong and a (student) member of IEEE, IEEE ITSS, INFORMS, INFORMS TSL, CHTS, IACIP, and ITS of HKU. He received his B.E. degree from the Department of Electronic Information Engineering, China University of Petroleum (Beijing), and M.E. degree from the Department of Automation, Tsinghua University, in 2014 and 2018, respectively. His research interests include intelligent perception and control, connected and automated transportation, stochastic modeling and optimization, and machine learning. Shaocheng has published over 10 papers in prestigious journals and conferences, including Transportation Science, IEEE TITS, TR-Part C, the International Symposium on Transportation and Traffic Theory (ISTTT), etc., as well as 6 national invention patents. Shaocheng is also a recipient of the Outstanding Master’s Dissertation Awards from CHTS and Tsinghua University. He has chaired a regular session in the 26th IEEE ITSC and a Young Scholar Tech Talk at HKU, co-organized two workshops in IEEE ITSC, and been a reviewer for IEEE TITS, IEEE TNNLS, IEEE TMC, TRR, IEEE ITSC, TRB, etc.