Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction

• URL: http://arxiv.org/abs/2309.05072v4
• Date: Sat, 27 Jul 2024 10:40:53 GMT
• Title: Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction
• Authors: Xiaowei Gao, Xinke Jiang, Dingyi Zhuang, Huanfa Chen, Shenhao Wang, Stephen Law, James Haworth,
• Abstract summary: We introduce the Stemporal Zero-Inflated Tweedie Graph Neural Network STZITZTDGNN -- the first uncertainty-aware graph deep learning model in road traffic accident prediction for multisteps. Our study demonstrates that STIDGNN can effectively inform targeted road monitoring, thereby improving urban road safety strategies.
• Score: 6.570852598591727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traffic accidents present substantial challenges to human safety and socio-economic development in urban areas. Developing a reliable and responsible traffic accident prediction model is crucial to addressing growing public safety concerns and enhancing the safety of urban mobility systems. Traditional methods face limitations at fine spatiotemporal scales due to the sporadic nature of highrisk accidents and the predominance of non-accident characteristics. Furthermore, while most current models show promising occurrence prediction, they overlook the uncertainties arising from the inherent nature of accidents, and then fail to adequately map the hierarchical ranking of accident risk values for more precise insights. To address these issues, we introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Network STZITDGNN -- the first uncertainty-aware probabilistic graph deep learning model in roadlevel traffic accident prediction for multisteps. This model integrates the interpretability of the statistical Tweedie family model and the expressive power of graph neural networks. Its decoder innovatively employs a compound Tweedie model,a Poisson distribution to model the frequency of accident occurrences and a Gamma distribution to assess injury severity, supplemented by a zeroinflated component to effectively identify exessive nonincident instances. Empirical tests using realworld traffic data from London, UK, demonstrate that the STZITDGNN surpasses other baseline models across multiple benchmarks and metrics, including accident risk value prediction, uncertainty minimisation, non-accident road identification and accident occurrence accuracy. Our study demonstrates that STZTIDGNN can effectively inform targeted road monitoring, thereby improving urban road safety strategies.

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