Student Seminar by Mr. Runqiu Hu on Jun 30, 2025 2PM

Title: Repositioning in bike sharing systems with broken bikes considering on-site repairs

Speaker: Mr. Runqiu Hu (Department of Civil Engineering)

Date: Jun 30, 2025 (Monday)

Time: 2:00 pm - 3:00 pm

Venue: Room 612B, 6/F, Haking Wong Building, The University of Hong Kong

About the talk: This research introduces a novel approach to bike-sharing system operations by simultaneously considering both vehicle-based repositioning and on-site repairs of broken bikes. While existing studies have typically assumed broken bikes can only be repaired at depots after collection, this research recognizes that bike-sharing operators also dispatch repairers for on-site repairs, satisfying demand without vehicle repositioning. A mixed-integer linear programming model is developed for a static bike repositioning problem combining vehicle-based delivery/collection with labor-based on-site repairs, aimed at minimizing the total cost of user dissatisfaction and carbon emissions within a specified time budget. For efficient solution of this complex problem, a hybrid algorithm is proposed incorporating Genetic Search with Adaptive Diversity Control and a novel Station Budget Constrained heuristic, which limits time spent at each station based on benefit-cost ratios. Computational experiments demonstrate that the algorithm obtains optimal solutions for small instances and outperforms commercial solvers on larger networks with less computational time. The cost-effectiveness of deploying repairers is examined, revealing diminished effectiveness with longer repair times and lower percentages of broken bikes. These findings highlight the need for dynamic repairer allocation based on the system's actual damage level, suggesting preventive maintenance strategies can reduce both broken bikes and repair time. The study contributes to the understanding of how on-site repairs can be integrated with traditional repositioning methods in bike-sharing systems.

About the speaker: Mr. Runqiu Hu is currently a Ph.D. student in the Department of Civil Engineering at The University of Hong Kong, supervised by Prof. W.Y. Szeto. He received his bachelor’s degree in Cybersecurity from the Department of Computer Science, Nanjing University of Posts and Telecommunications in 2018 and master’s degree in Computer Technology from the School of Computer Science and Engineering, Southeast University, China, in 2021. His research interests include shared mobility systems, transportation optimization, multi-objective optimization, and the application of artificial intelligence in transportation engineering. Runqiu has publications in several journals including Transportation Research Part E, Transportation Research Part D, IEEE Internet of Things Journal, and IEEE Access. His work focuses on bike-sharing system operations, electric vehicle range anxiety analysis, and intelligent transportation optimization. During his master’s study, he developed expertise in knowledge representation and reasoning, particularly in non-monotonic reasoning for decision support systems using answer set programming and finding reasoning paths for the explainable AI. His current research explores the integration of operations research techniques with transportation engineering challenges, with a specific emphasis on developing efficient algorithms for complex optimization problems in shared mobility contexts.

Student Seminar by Ms. Fangyi Ding on May 23, 2025 10AM

Title: A Graph Deep Learning Model for Station Ridership Prediction in Expanding Metro Networks

Speaker: Ms. Fangyi Ding (Department of Urban Planning and Design)

Date: May 23, 2025 (Friday)

Time: 10:00 am - 11:00 am

Venue: Room 612B, 6/F, Haking Wong Building, The University of Hong Kong

About the talk: Due to their reliability, efficiency, and environmental friendliness, metro systems have become a crucial solution to transportation challenges associated with urbanization. Many countries have constructed or expanded their metro networks over the past decades. During the planning stage, accurately predicting station ridership post-expansion, particularly for new stations, is essential to enhance the effectiveness of infrastructure investments. However, station-level metro ridership prediction under expansion scenarios (MRP-E) has not been thoroughly explored, as most advanced models currently focus on short-term predictions. MRP-E presents significant challenges due to the absence of historical data for newly built stations and the dynamic, complex spatiotemporal relationships between stations during expansion phases. In this study, we propose a Metro-specific Multi-Graph Attention Network model (Metro-MGAT) to address these issues. Our model leverages multi-sourced urban context data and network topology information to generate station features. Multi-relation graphs are constructed to capture the spatial correlations between stations, and an attention mechanism is employed to facilitate graph encoding. The model has been evaluated through realistic experiments using multi-year metro ridership data from Shanghai, China. The results validate the superior performance of our approach compared to existing methods, particularly in predicting ridership at new stations.

About the speaker: Fangyi Ding is currently a PhD candidate in the Department of Urban Planning and Design at the University of Hong Kong (HKU). She holds a master's degree from Tongji University and a bachelor's degree from the Harbin Institute of Technology. Her research interests lie in transportation demand forecasting, network modeling, and travel behavior analysis, with a particular focus on public transit and shared mobility services. Her work has been published in leading academic journals and conferences, including Transportation Research Part D, the Journal of Transport Geography, the Transportation Research Board (TRB) Annual Meeting, and ACM SIGSPATIAL, among others.

Student Seminar by Mr. Kaihang Zhang on Feb 27, 2025 4PM

Title: Tactical Operations of Service Region Dimensioning, Bundling, and Matching for On-Demand Food Delivery Services

Speaker: Mr. Kaihang Zhang (Department of Civil Engineering)

Date: Feb 27, 2025 (Thursday)

Time: 4:00 pm - 5:00 pm

Venue: Room 612B, 6/F, Haking Wong Building, The University of Hong Kong

About the talk: On-demand food delivery (OFD) services have experienced a significant surge in popularity in recent years, which poses various challenges for service operators. To address these challenges, we discuss an analytical model that captures the complex interplay of the OFD system by considering factors such as adjustable service region size and order bundling. We investigate how key decision variables, including maximum delivery distance and bundling ratio, affect the system's endogenous variables and two critical system performance metrics: customer total waiting time and order throughput. Our analysis yields several intriguing managerial insights. First, the maximum delivery distance has a non-monotonic impact on the customer accumulation time, delivery time, and total waiting time, and there is a “win-win” situation in which increasing the maximum delivery distance benefits both the customer total waiting time and order throughput. Second, order bundling is crucial under high customer demand to ensure adequate food delivery supply, but it is less desirable under low customer demand due to increased detour distances in delivery. We further explore strategies for minimizing customer total waiting time (by setting small service regions and bundling ratios) and order throughput (by establishing larger service regions). Recognizing the partial conflict between these two objectives, we identify the Pareto-efficient frontier that serves as a guideline for service operators in balancing these competing goals.

About the speaker: Kaihang Zhang is currently a PhD candidate at The University of Hong Kong supervised by Dr. Jintao Ke. He is a student member of INFORMS, ITS of HKU, and the recipient of Hong Kong PhD Fellowship and HKU Presidential Scholarship. He has been developing economic analytical models and network flow models throughout his PhD study at HKU on the operations for on-demand urban mobility systems. During his time at HKU, he worked as a visiting research student at LIMOS, University of Michigan, working on the development of data-driven analytical model for food delivery services. He has Bachelor’s Degrees from Zhejiang University and UIUC, and a Master’s Degree from UC Berkeley.