Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction
This work addresses metro flow prediction for urban planning and operations, representing an incremental improvement through a novel hybrid method.
The paper tackles urban metro flow prediction by developing a spatio-temporal dynamic graph relational learning model (STDGRL) to capture unique station patterns and complex dynamic relationships, achieving superior performance over 11 baselines across multiple city datasets.
Urban metro flow prediction is of great value for metro operation scheduling, passenger flow management and personal travel planning. However, it faces two main challenges. First, different metro stations, e.g. transfer stations and non-transfer stations, have unique traffic patterns. Second, it is challenging to model complex spatio-temporal dynamic relation of metro stations. To address these challenges, we develop a spatio-temporal dynamic graph relational learning model (STDGRL) to predict urban metro station flow. First, we propose a spatio-temporal node embedding representation module to capture the traffic patterns of different stations. Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix. Finally, we provide a transformer-based long-term relationship prediction module for long-term metro flow prediction. Extensive experiments are conducted based on metro data in Beijing, Shanghai, Chongqing and Hangzhou. Experimental results show the advantages of our method beyond 11 baselines for urban metro flow prediction.