Multi-view residual spatio-temporal topology adaptive graph convolutional network for urban road traffic accident prediction with multi-source risks

The densification of urban habitats and car-oriented urban planning have led to a growing number of traffic accidents, causing severe casualties, economic losses, and threats to sustainable urban development. Predicting traffic accidents, especially in dense urban areas, is therefore essential for improving public safety and minimizing risks. However, accident sparsity, complex spatiotemporal correlations among diverse risk factors, and external environmental influences make prediction highly challenging. To address these issues, this study proposes a Multi-View Residual Spatio-Temporal Topology Adaptive Graph Convolutional Network (MV-RSTTAG) that integrates heterogeneous risks from road, area, traffic, and external perspectives. By combining topology-adaptive graph convolution, residual connections, and attention-based fusion, the model effectively captures multi-scale spatiotemporal dependencies. Experiments on New York City data (January–June 2016) show that MV-RSTTAG consistently outperforms baseline models across multiple metrics. Efficiency analysis demonstrates millisecond-level inference latency and favorable scalability, supporting real-time deployment. Overall, the model provides an interpretable and efficient framework for intelligent traffic accident risk prediction and urban safety management. • Propose MV-RSTTAG to integrate road and area features for accident prediction. • Introduce deep residual TAG block to capture road network structural info efficiently. • Use attention mechanism and focal loss to enhance feature fusion and handle imbalance. • Analyze TAG’s strengths in capturing complex road network structures effectively. • Show MV-RSTTAG’s superior performance via comparative and ablation experiments.