Abstract
This paper targets at predicting public transport in-out crowd flows of different regions together with transit flows between them in a city. The main challenge is the complex dynamic spatial correlation of crowd flows of different regions and origin-destination (OD) paths. Different from road traffic flows whose spatial correlations mainly depend on geographical distance, public transport crowd flows significantly relate to the region’s functionality and connectivity in the public transport network. Furthermore, influenced by commuters’ time-varying travel patterns, the spatial correlations change over time. Though there exist many works focusing on either predicting in-out flows or OD transit flows of different regions separately, they ignore the intimate connection between the two tasks, and hence lose efficacy. To solve these limitations in the literature, we propose a Graph spAtio dynamIc Network (GAIN) to describe the dynamic non-geographical spatial correlation structures of crowd flows, and achieve holistic prediction for in-out flows of each region together with OD transit flow matrix between different regions. In particular, for spatial correlations, we construct a dynamic graph convolutional network for the in-out flow prediction. Its graph structures are dynamically learned from the prediction of OD transit flow matrix, whose spatial correlations are further captured via a multi-head graph attention network. For temporal correlations, we leverage three blocks of gated recurrent units, which capture minute-level, daily-level and weekly-level temporal correlations of crowd flows separately. Experiments on real-world datasets are used to demonstrate the efficacy and efficiency of GAIN.
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Acknowledgments
The authors would like to show their great appreciation to the Metro Corporation for sharing this passenger flow data, and in the protection of privacy, all data have been desensitized. This work was supported in part by the NSFC Grant 71901131, 71932006 and 71931006, in part by the ASFC Grant 2020Z063058001, in part by the Tsinghua GuoQiang Research Center Grant 2020GQG1014 and Tsinghua University Intelligent Logistics & Supply Chain Research Center Grant THUCSL20182911756-001, in part by the Ministry of Education, Singapore, AcRF Tier 2 Funding Grant MOE2019-T2-2-116, and in part by the Hong Kong RGC GRF Grant 16216119 and 16201718.
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He, B., Li, S., Zhang, C., Zheng, B., Tsung, F. (2021). Holistic Prediction for Public Transport Crowd Flows: A Spatio Dynamic Graph Network Approach. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12975. Springer, Cham. https://doi.org/10.1007/978-3-030-86486-6_20
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