Student Seminar by Mr. Yuxuan Zhou on Mar 4, 2026, 2PM

Title: Mobility, Segregation, and Inequalities: How experienced income segregation relates to travel behaviour and health inequalities

Speaker: Mr. Yuxuan Zhou (Department of Architecture & Civil Engineering, City University of Hong Kong)

Date: Mar 4, 2026 (Wednesday)

Time: 2:00 pm - 3:00 pm

Venue: Room 1010, CLL, Department of Geography, 10/F, The Jockey Club Tower, The University of Hong Kong

Registration Link: https://forms.office.com/r/4WLBeJ73Y5

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

Abstract: Income segregation is a barrier to social inclusivity and equality. It is affected by individuals’ travel behaviour and socioeconomic contexts and may be intensified by localized living models emphasizing activities within immediate neighbourhoods. However, how the relationship between mobility-based experienced income segregation and travel behaviour varies across neighbourhood social and urban contexts remains unclear. Moreover, income segregation has been widely linked to social inequalities, including health inequalities. Yet most existing studies rely on residential segregation as a static measure of exposure, and rare studies have examined segregation–health relationships using a purely mobility-based approach that captures dynamic, real-world social exposure that may yield more accurate estimates. This seminar introduces two nationwide studies based on large-scale mobility data from the United States. We examine experienced income segregation and its associations with travel behaviour across different neighbourhood social and urban contexts, as well as how experienced segregation relates to income-related health inequalities. We find that longer travel distances and more diverse activity destinations are associated with lower levels of experienced segregation in less affluent neighbourhoods, particularly in less urbanized areas. In addition, higher levels of experienced segregation are associated with more pronounced income-related health disparities. These findings highlight potential trade-offs between localized living models and adverse social consequences and provide implications for how upward mobility, activity-based social mixing may be considered in future efforts to understand and address social inequalities associated with segregation. 

Bios: Yuxuan’s research focuses on exploring the complex relationships between the built/social environment, human behaviour, and health outcomes using multi-source spatial big data and advanced spatiotemporal statistical methods. His work lies at the intersection of health geography, GIScience, and urban studies. He has published over ten peer-reviewed articles in leading journals, including Nature Communications, Environmental Impact Assessment Review, Social Science & Medicine, and Applied Geography.

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.