MDAS-GNN: Multi-Dimensional Spatiotemporal GNN with Spatial Diffusion for Urban Traffic Risk Forecasting

TL;DR

MDAS-GNN is a novel multi-dimensional spatiotemporal graph neural network that effectively predicts urban traffic accident risks by capturing complex spatial and temporal dependencies, outperforming existing models in accuracy and robustness.

Contribution

The paper introduces MDAS-GNN, a new GNN framework that integrates multi-dimensional risk factors with spatial diffusion and multi-head temporal attention for improved traffic risk forecasting.

Findings

Achieves lower prediction errors across various time horizons.

Outperforms baseline methods in urban accident risk prediction.

Reduces errors by up to 40% with multi-dimensional features.

Abstract

Traffic accidents represent a critical public health challenge, claiming over 1.35 million lives annually worldwide. Traditional accident prediction models treat road segments independently, failing to capture complex spatial relationships and temporal dependencies in urban transportation networks. This study develops MDAS-GNN, a Multi-Dimensional Attention-based Spatial-diffusion Graph Neural Network integrating three core risk dimensions: traffic safety, infrastructure, and environmental risk. The framework employs feature-specific spatial diffusion mechanisms and multi-head temporal attention to capture dependencies across different time horizons. Evaluated on UK Department for Transport accident data across Central London, South Manchester, and SE Birmingham, MDASGNN achieves superior performance compared to established baseline methods. The model maintains consistently low prediction errors across short, medium, and long-term periods, with particular strength in long-term forecasting. Ablation studies confirm that integrated multi-dimensional features outperform singlefeature approaches, reducing prediction errors by up to 40%. This framework provides civil engineers and urban planners with advanced predictive capabilities for transportation infrastructure design, enabling data-driven decisions for road network optimization, infrastructure resource improvements, and strategic safety interventions in urban development projects.