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.