Traffic Accident Risk Prediction via Multi-View Multi-Task Spatio-Temporal Networks

Senzhang Wang, Jiaqiang Zhang, Jiyue Li, Hao Miao, Jiannong Cao

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Abnormal traffic incidents such as traffic accidents have become a significant health and development threat with the rapid urbanization of many countries. The challenges of accurate traffic risk forecasting are three-fold. First, traffic accident data in some areas of a city is sparse, especially for a fine-grained prediction, which may cause the zero inflation problem during model training. Second, the spatio-temporal correlations of the traffic accidents are rather complex and non-linear, which is difficult to capture by existing shallow models like regression. Third, the occurrence of traffic accidents can be significantly affected by various context features including weather, POI and road network features. To address the above challenges, this paper proposes a Multi-View Multi-Task Spatio-Temporal Networks (MVMT-STN) model to forecast fine- and coarse-grained traffic accident risks of a city simultaneously. Specifically, to address the data sparsity issue in a fine-grained prediction, we adopt a multi-task learning framework to jointly forecast both fine- and coarse-grained traffic accident risks by considering their spatial associations. We conduct extensive experiments over two large real traffic accident datasets. The results show that MVMT-STN improves the performance of traffic accident risk prediction in both fine- and coarse-grained prediction by a large margin compared with existing state-of-the-art.
Original languageEnglish
Pages (from-to)1-14
JournalIEEE Transactions on Knowledge and Data Engineering
Publication statusPublished - Dec 2021


  • accidents
  • Correlation
  • Urban areas
  • Roads
  • Data models
  • predictive models
  • Representation learning
  • Traffic accident forecasting
  • Multi-task learning
  • spatio-Temporal data
  • Graph neural networks


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