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

TL;DR

This paper introduces a novel graph neural network model that predicts urban traffic risk levels while quantifying uncertainty, demonstrated through a case study in London, enhancing transportation safety and decision-making.

Contribution

The paper presents a new spatial-temporal zero-inflated negative binomial GNN that incorporates uncertainty quantification into traffic risk prediction, filling a gap in existing models.

Findings

Outperforms existing methods in risk prediction accuracy

Provides reliable uncertainty estimates for traffic risk levels

Demonstrates effectiveness in a real-world London case study

Abstract

Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often overlook the importance of model uncertainty. In this paper, we introduce a novel Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) for road-level traffic risk prediction, with a focus on uncertainty quantification. Our case study, conducted in the Lambeth borough of London, UK, demonstrates the superior performance of our approach in comparison to existing methods. Although the negative binomial distribution may not be the most suitable choice for handling real, non-binary risk levels, our work lays a solid foundation for future research exploring alternative distribution models or techniques. Ultimately, the STZINB-GNN contributes to enhanced transportation safety and data-driven decision-making in urban planning by providing a more accurate and reliable framework for road-level traffic risk prediction and uncertainty quantification.