Student Seminar by Dr. Zhenwei Gong on Nov 4, 2025, 1PM

Title: Urban logistics system design with truck-drone collaborative delivery

Speaker: Dr. Zhenwei Gong (Department of Data and Systems Engineering)

Date: Nov 4, 2025 (Tuesday)

Time: 1:00 pm - 2:00 pm

Venue: Room 8-28, Haking Wong Building, The University of Hong Kong

Registration Link: https://forms.office.com/r/UzVxX5SgF9

ITS Student Committee will provide light refreshments and drinks for registered participants.

Abstract: This study investigates the optimal design of truck-drone urban delivery systems by comparing three truck-drone collaborative schemes, i.e., alternating truck-drone (ATD), zonal truck-drone (ZTD), and two-tier truck-drone (TTD), against the traditional truck-only (TO) operations. We formulate a system cost minimization problem that accounts for both logistics carrier expenses (including travel distance, fleet costs, and makespan) and customer-related costs (order accumulation time, delivery time, and walking time). The optimization determines key decision variables, i.e., delivery headway, truck fleet size, drone fleet size, service sector partitioning, and mini-depot, to achieve efficient operations under varying demand and service region scales. Numerical results demonstrate that collaborative schemes consistently outperform TO delivery, with ZTD emerging as the most cost-effective solution due to its spatial workload partitioning strategy. ATD provides moderate cost reductions, while TTD excels in service quality by minimizing customer waiting times through its hierarchical mini-depot network. Sensitivity analyses reveal that truck-drone systems maintain robust performance even under fluctuating cost parameters, whereas TO operations suffer from poor scalability in large urban areas. These findings highlight that truck-drone collaboration is not only economically advantageous but also essential for meeting modern logistics demands, offering superior efficiency, flexibility, and customer satisfaction compared to conventional approaches. The study provides actionable insights for logistics operators in selecting optimal delivery strategies based on cost, service, and infrastructure priorities.

Bios: Dr. Zhenwei Gong earned his Ph.D. in September 2025 from the Department of Data and Systems Engineering at the University of Hong Kong (HKU). He had previously received his Master's degree from HKU in 2021 and his Bachelor's degree from Nanjing Normal University (NNU) in 2020. His research centers on transportation economics, analytics, and optimization, with a particular focus on civil aviation and low-altitude air transportation systems. Dr. Gong has been recognized with several honors and awards, including the HKSTS Outstanding Student Paper Award (2024), the CTS Best Paper Award (2025), and the Chu Tsun Hong Scholarship for Outstanding Research Achievement. His research has been published in leading transportation journals such as Transportation Research Part B and Transport Policy, and has presented his work at major international conferences, including the Transportation Research Board (TRB) Annual Meeting, the International Conference of the Hong Kong Society for Transportation Studies (HKSTS), and the Air Transport Research Society (ATRS) World Conference.

Student Seminar by Ms. Luyun Zhao on Oct 14, 2025, 1PM

Title: Strategic planning for public transportation electrification: large-scale electric bus network transition planning via deep reinforcement learning

Speaker: Ms. Luyun Zhao (Department of Urban Planning and Design)

Date: Oct 14, 2025 (Tuesday)

Time: 1:00 pm - 2:00 pm

Venue: Room 8-28, Haking Wong Building, The University of Hong Kong

Registration Link: https://forms.office.com/r/FThKpBgpfp

ITS Student Committee will provide light refreshments and drinks for registered participants.

Abstract: Urban bus electrification is gaining global interest, playing a crucial role in reducing emissions. This study defines and addresses the electric bus network transition problem (EBNTP), jointly optimizing battery electric bus (BEB) fleet transitions and charging facility planning over a multi-period horizon. Existing research often neglects this interdependent long-term planning and lacks scalable solutions for large systems. This study proposes a deep reinforcement learning (DRL) approach, formulating EBNTP as a Markov Decision Process modeling sequential planning decisions, and introduces the DRL-HetGNN method, integrating heterogeneous graph neural networks (HetGNN) to capture network effects and enhance efficiency in large-scale applications. Using Hong Kong's franchised bus system as a case study, DRL-HetGNN demonstrates superior performance and generalizability compared to benchmark methods. Scenario analyses explore budget allocations, independent operators, BEB subsidies, and price fluctuations, while examining policy-incentive mechanisms to accelerate electrification. The findings will support policymakers in planning sustainable public transportation systems.

Bios: Luyun Zhao is a PhD candidate at the Department of Urban Planning and Design, Faculty of Architecture, HKU. Her research interest lies in public transit, shared mobility, transportation electrification, and transportation modeling. She has more than five years of theoretical and practical experience in the transportation industry. Prior to HKU, she worked at Tencent Smart Transportation, DiDi Chuxing Public Transportation Department and Two-Wheeler Department, Bytedance, and the World Bank. Luyun received her Bachelor of Engineering and Bachelor of Economics degrees from Peking University and her Master of City Planning degree from the University of Pennsylvania.

Student Seminar by Dr. Yimo Yan on Sep 29, 2025, 3PM

Title: Event-driven policy optimization for dynamic ambulance dispatch: An attention-based reinforcement learning approach

Speaker: Dr. Yimo Yan (Department of Data and Systems Engineering)

Date: Sep 29, 2025 (Monday)

Time: 3:00 pm - 4:00 pm

Venue: Room 8-28, Haking Wong Building, The University of Hong Kong

Registration Link: https://forms.office.com/r/nvWGefbq5n

ITS Student Committee will provide light refreshments and drinks for registered participants.

Abstract: The global healthcare strain from events such as the COVID-19 pandemic has intensified ambulance shortages, leading to prolonged patient waiting times and increased mortality. In this respect, efficient ambulance dispatch presents a backbone for providing timely patient care. This paper tackles the problem of dynamic ambulance dispatch as a semi-Markov decision process, with the objective of minimizing severity-weighted waiting times through en-route re-dispatching and event-driven decisions. We propose a policy gradient algorithm with a self-attention approximator, which enables inter-task (patients) and inter-agent (ambulances) communications under uncertainty and variable inputs. To further enable interpretability, we distill the learned policy into a decision tree with theoretically grounded features. Experiments on synthetic and real-world cases demonstrate reduced average waiting time and its variance for patients, improved ambulance operational efficiency, and effective use of strategic withholding. Our approach contributes to the development of interpretable, rule-based ambulance dispatch systems in resource-constrained medical environments.

Bios: Dr. Yimo Yan graduated from the Department of Data and Systems Engineering at The University of Hong Kong in Aug 2025. Yimo’s work focuses on transportation and logistics optimisation, incorporating methods like mixed integer linear programming, reinforcement learning, deep learning and large language model. His research, published in various academic journals, addresses challenges in last-mile delivery and scheduling.