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Urban road-based risk prediction is a crucial yet challenging aspect ofresearch in transportation safety. While most existing studies emphasizeaccurate prediction, they often overlook the importance of model uncertainty.In this paper, we introduce a novel Spatial-Temporal Zero-Inflated NegativeBinomial Graph Neural Network (STZINB-GNN) for road-level traffic riskprediction, with a focus on uncertainty quantification. Our case study,conducted in the Lambeth borough of London, UK, demonstrates the superiorperformance of our approach in comparison to existing methods. Although thenegative binomial distribution may not be the most suitable choice for handlingreal, non-binary risk levels, our work lays a solid foundation for futureresearch exploring alternative distribution models or techniques. Ultimately,the STZINB-GNN contributes to enhanced transportation safety and data-drivendecision-making in urban planning by providing a more accurate and reliableframework for road-level traffic risk prediction and uncertaintyquantification.

Uncertainty Quantification in the Road-level Traffic Risk Prediction by  Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural  Network(STZINB-GNN)