Student Seminar by Ms. Xinyi Zhu on Nov 26, 2025, 1PM

Title: Resource allocation for an air-rail-integrated co-modality platform considering both demand and supply uncertainties

Speaker: Ms. Xinyi Zhu (Department of Aeronautical and Aviation Engineering, Hong Kong Polytechnic University)

Date: Nov 26, 2025 (Wednesday)

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/1nLK1SMMHh 

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

Abstract: The co-modal mode, i.e., passenger-and-freight mixed transportation, has received increasing interest, given the rapid growth of parcel volume and its potential to save transportation costs. This paper examines an air-rail-integrated co-modal mode that utilizes the excess capacity of passenger trains and flights considering uncertainties in both supply and demand. On the supply side, uncertainty arises from travel time delays of passenger trains and flights. On the demand side, while historical data on cargo orders are available, such as volume distribution between each origin and destination pair, the daily cargo orders/demands remain uncertain and will be revealed in real-time. We aim to dynamically allocate these resources (excess capacity of trains and flights) to serve cargo orders while effectively accommodating uncertainties. To address this problem, a two-stage stochastic programming model is developed to minimize the total costs associated with cargo transportation, holding, transshipment, delays, and ad-hoc service options (when the co-modal mode is unavailable). The sample average approximation solution approach, embedded with an adaptive large neighborhood search algorithm, is employed to solve the problem. The above model and algorithm are implemented in a rolling horizon framework to make time-dependent resource allocation decisions. The test instances are generated based on rail and air transportation data in Hong Kong (with Hong Kong West Kowloon Station and Hong Kong International Airport). Numerical studies and sensitivity analysis are conducted to evaluate (i) the benefits of the air-rail-integrated co-modality, (ii) the effectiveness of the proposed solution algorithm, and (iii) the impact of demand/supply characteristics on the air-rail-integrated co-modality operation.

Bios: Xinyi Zhu is a third-year Ph.D. candidate in the Hong Kong Polytechnic University. She had previously received her master's degree from Shanghai Jiao Tong University in 2023 and her bachelor's degree from Dalian Maritime University in 2020. Her research centers on transportation modeling, with a particular focus on multimodal transport network design under uncertainty. Her research has been published in transportation journal such as Transportation Research Part C, and has presented her work at major international conferences, including POMS International Conference in China (POMS China), the International Conference of the Hong Kong Society for Transportation Studies (HKSTS), and the Air Transport Research Society (ATRS) World Conference.

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.