Student Seminar by Mr. Yuzhen Feng on Mar 26, 2026, 3:30PM

Title: Resource-Constrained User Equilibrium

Speaker: Mr. Yuzhen Feng (The Hong Kong Polytechnic University)

Date: Mar 26, 2026 (Thursday)

Time: 3:30 pm - 4:30 pm

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

Registration Link: https://forms.cloud.microsoft/r/552KT3frUg

ITS Student Committee will provide a beverage for registered participants.

Abstract: We study user equilibrium with resource constraints (UERC) where users face strict budget on energy, time, risk, etc., motivated by applications in urban mobility, perishable logistics, and itinerary planning. UERC unifies user equilibrium and the resource‑constrained shortest path problem by allowing both travel costs and resource consumption to depend on congestion, making path feasibility endogenous. We formulate UERC as a quasi‑variational inequality and prove that a UERC exists when every user has at least one feasible path regardless of congestion; otherwise, deciding existence is NP-hard. We establish uniqueness in networks with parallel links between a single origin-destination pair, even with heterogeneous budgets among users, and show counterexamples where minimal generalizations destroy uniqueness. When cost equals resource consumption on every link, UERC coincides with classical user equilibrium and thus inherits its uniqueness of link flows. When cost and resource consumption differ but their ratio is uniformly bounded above and below, both the price of anarchy and unfairness (the max-to-min ratio of equilibrium costs experienced by the same user type) are bounded; without such comparability, both can be unbounded. We also reveal a paradoxical comparative statics: increasing users' budgets can raise total system cost by allowing more users to already congested routes. Computationally, we develop a penalty‑based algorithm with column generation that repeatedly solves a resource‑constrained shortest path subproblem, and prove convergence under the same sufficient condition for existence. Experiments on benchmark road networks demonstrate the computational scalability of our algorithm and quantify the impact of endogenous feasibility on equilibrium outcomes.

Bios: Mr. Yuzhen Feng is currently a third-year Ph.D. student in Transportation at The Hong Kong Polytechnic University, supervised by Dr. Wei Liu. His recent work is mainly related to transportation network modeling, optimization, and equilibrium. He received his B.Mgt. degree in Information Management and Information System from the School of Economics and Management at Tongji University in 2023. During his undergraduate studies, he also worked with Prof. Xiaolei Wang on dynamic en-route ride-pooling. He is a recipient of the Hong Kong Ph.D. Fellowship.

Student Seminar by Mr. Zian Wang Peter on Mar 11, 2026, 1PM

Title: Toward Unified Risk Field Modeling for Interactive Autonomous Driving: A Comparative Study and Future Directions

Speaker: Mr. Zian Wang Peter (Department of Data and Systems Engineering)

Date: Mar 11, 2026 (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/WssbpUgZaj

ITS Student Committee will provide a beverage for registered participants.

Abstract: Quantifying interaction risk in mixed traffic remains a fundamental challenge for autonomous driving systems. Traditional approaches rely on low-dimensional metrics such as time-to-collision (TTC), which struggle to capture the spatially distributed and temporally evolving nature of hazards in complex driving scenarios. Recent advances have proposed modeling risk as a continuous field over space and time, offering richer representations that naturally encode spatial uncertainty, multi-agent interactions, and environmental context. However, systematic comparisons of such risk field formulations remain limited, and no unified theoretical foundation has emerged to guide their design.

This talk consists of two parts. In the first half, we present a comparative study of representative risk field models from the literature. Using real-world bird's-eye-view (BEV) datasets spanning diverse interaction scenarios including highway merging, occlusion, and stop-and-go traffic. We then evaluate these models through a unified set of field-level metrics assessing spatial coherence, temporal consistency, and behavioral alignment with human driving decisions. Our analysis reveals that different modeling assumptions yield distinct spatial risk structures, with significant implications for downstream planning and prediction tasks.

In the second half, we discuss emerging directions toward unified field modeling that integrates physics-informed principles with data-driven methods like PINNs. We briefly introduce how partial differential equation (PDE)-based formulations, widely used in macroscopic traffic flow theory, can be adapted for microscopic risk propagation in autonomous driving contexts. Preliminary results on specifically designed robustness benchmarks will be shared, along with open discussions in bridging traffic flow modeling with real-time motion planning for intelligent transportation systems.

Bios: Mr. Zian Wang Peter is a first-year MPhil student in the Department of Data and Systems Engineering at the University of Hong Kong, supervised by Prof. Chen Sun. He received the BEng degree in Electronic and Information Engineering from The Hong Kong Polytechnic University in 2025, where he was advised by Prof. Ivan Ho Wang-hei with experiences on embedded systems, vehicular technology, and robotics. His research now focuses on integration of data-driven methods with risk-aware frameworks for improved prediction and safe planning in autonomous driving, and interdisciplinary topics within intelligent transportation systems.

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.