Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction

• URL: http://arxiv.org/abs/2204.02650v1
• Date: Wed, 6 Apr 2022 08:07:40 GMT
• Title: Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction
• Authors: Peng Xie, Minbo Ma, Tianrui Li, Shenggong Ji, Shengdong Du, Zeng Yu, Junbo Zhang
• Abstract summary: Different metro stations, e.g. transfer and non-transfer stations, have unique traffic patterns. It is challenging to model complex-temporal dynamic relation of metro stations.
• Score: 10.300311879377734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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