INSTR2023: The 9th International Symposium on Transport Network Resilience

The 9th International Symposium on Transport Network Resilience (INSTR) was held at InterContinental Grand Stanford Hong Kong from Dec 13-14, 2023. Four keynote speeches were delivered by Prof. Hani Mahmassani (Northwestern University), Prof. Yu-Chiun Chiou (National Yang Ming Chiao Tung University), Prof. Fumitaka Kurauchi (Gifu University), and Prof. William H.K. Lam (The Hong Kong Polytechnic University) on Dec 13 morning. In this symposium, more than 80 presentations were given in 29 parallel sessions, covering the analysis, planning, design, control, and management of transport networks. Prof. Michael Bell (Convenor of INSTR2023) and Dr. Jintao Ke (Co-chair of INSTR2023) gave closing speeches, followed by a welcoming remark by Prof. Nour-Eddin El Faouzi and Dr. Angelo Furno (Co-chairs of INSTR2026).

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.

The Inaugural ITS Research Forum on Nov 11, 2025, 2:30 PM

Date: November 11, 2025 (Tuesday)

Time: 14:30 – 17:30

Venue: HW612B, 6/F Haking Wong Building, HKU

Programme:

14:30 - 14:35 Opening speech & introduction by Prof WY Szeto

14:35 - 15:20 "Urban Mobility, Air Pollution, and Environmental Justice"

by Professor Junshi XU

15:20 - 16:05 "Representative Agents for Traffic Modeling: From Fully LLM-Driven to LLM

Guided Learning" by Professor Jiayang LI

16:05 - 16:20 Break

16:20 - 17:30 Brainstorming and discussion on research collaborations /

ITS initiatives (led by Professor WY Szeto)

All categories of ITS fellows are welcome. The abstracts of the talks and the bios of Prof. Xu and Prof. Li can be checked below.

Urban Mobility, Air Pollution, and Environmental Justice

Professor Junshi XU

Department of Geography, The University of Hong Kong

Abstract:

Urban mobility is key to building equitable and sustainable cities, especially for populations facing socioeconomic disadvantage, transport poverty, and high exposure to traffic-related air pollution (TRAP). This seminar introduces the PEACE framework (People oriented, Equitable, Accessible, Community focused, and Environmental), which integrates engineering, data science, and social perspectives to address urban mobility and environmental justice challenges. Drawing on Canadian research that applied environmental sensing, machine learning, and computer vision to examine TRAP and its unequal distribution, the seminar also reflects on community engagement and institutional initiatives in Toronto that revealed the challenges faced by disadvantaged groups and identified pathways for local interventions. New insights from Hong Kong extend these approaches through high-resolution sensing and vehicle-class-resolved modelling, showing how goods vehicles and buses contribute to disparities in exposure, particularly among low-income and public housing residents. The seminar demonstrates how integrated methods can inform targeted emission mitigation and equitable transport policies, advancing more inclusive, resilient, and sustainable cities.

Bio:

Dr. Junshi Xu is an Assistant Professor in the Department of Geography at The University of Hong Kong, where he also serves as Deputy Program Director of the Master of Transportation Policy and Planning (MTPP). He earned his Ph.D. in Transportation Engineering from the University of Toronto in 2020.

His research integrates environmental sensing, machine learning, and computer vision to investigate transport-related air pollution and environmental justice. He has led and contributed to projects in Canada and Hong Kong that combine high-resolution mobile monitoring, vehicle-class-resolved modelling, and community engagement to uncover disparities in exposure, particularly among disadvantaged groups. These studies aim to inform equitable policies and advance sustainable urban mobility solutions.

Dr. Xu has published over 45 peer-reviewed articles in journals such as Environmental Science & Technology, Environmental Pollution, Science of the Total Environment, and Transportation Research Part D. He served on the Transportation Research Board’s Committee on Air Quality and Greenhouse Gas Mitigation, is Executive Guest Editor for Transportation Research Part D, and has been Guest Editor for the Journal of Atmosphere and the Journal of Computational Urban Science.

Representative Agents for Traffic Modeling:

From Fully LLM-Driven to LLM-Guided Learning

Professor Jiayang LI

Department of Data and Systems Engineering, The University of Hong Kong

Abstract:

Large language models (LLMs) are increasingly used as behavioral proxies for self-interested travelers in agent-based traffic models. Although more flexible and generalizable than conventional models, the practical use of these approaches remains limited by scalability due to the cost of calling one LLM for every traveler. Moreover, it has been found that LLM agents often produce unstable day-to-day dynamics. To address these challenges, we propose to model each homogeneous traveler group facing the same decision context with a single representative LLM agent who behaves like the population's average, maintaining and updating a mixed strategy over routes that coincides with the group's aggregate flow proportions. Each day, the LLM reviews the travel experience and flags routes with positive reinforcement that they hope to use more often, and an interpretable update rule then converts this judgment into strategy adjustments using a tunable (progressively decaying) step size. The representative-agent design improves scalability, while the separation of reasoning stabilizes learning. In classic traffic assignment settings, we find that the proposed approach converges rapidly to the user equilibrium. In richer settings with income heterogeneity, multi-criteria costs, and multi-modal choices, the generated dynamics remain stable and interpretable. Moreover, it reproduces behavioral patterns well-documented in psychology and economics, for instance, the decoy effect in toll and non-toll road selection and the greater willingness-to-pay for convenience among higher-income travelers.

Bio:

Prof. Jiayang Li is an Assistant Professor in the Department of Data and Systems Engineering and a Fellow of the Institute of Transport Studies at The University of Hong Kong. Prof. Li earned his Ph.D. in transportation systems analysis from Northwestern University in 2024. Before that, he obtained his B.S. in mathematics from Tsinghua University in 2019. Prof. Li's research aims to bring together optimization, game theory, and machine learning to address operations research challenges, particularly within transportation and mobility systems. His work has been published across diverse fields, including leading transportation journals (Transportation Science and Transportation Research Part B) as well as top machine learning conferences (NeurIPS and ICML).