TANG Weiwen1,2, GUO Shengnan1,2, CHEN Wei1,2, LIN Youfang1,2, WAN Huaiyu1,2
1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044; 2. Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044
Abstract：Exploring the law of accident occurrence from historical traffic accident data and realizing accurate road level traffic accident risk prediction can improve the travel safety and efficiency effectively. However, road level traffic accident risk prediction is faced with great challenges due to the influence of multiple factors, such as weather and traffic state, the complex temporal and spatial correlation between traffic accidents and the sparsity of accident data. To address these issues, a two-level and multi-view spatial-temporal graph neural network by incorporating temporal knowledge graph(STGN-TKG) is proposed. Firstly, a traffic accident temporal knowledge graph is constructed for the first time, and diachronic embedding for traffic accident temporal knowledge graph is designed to mine the high-order and dynamic correlation between multi-source influencing factor data. Then, a spatial graph convolution attention module and a temporal representation module are employed to fully model the complex spatial-temporal correlations between traffic accidents from two levels and multiple views. Finally, an accident risk propagation strategy is proposed to alleviate the zero-inflated issue. The experimental results on two real-world road level traffic accident risk datasets show that STGN-TKG achieves superior performance on the road level accident risk prediction task.
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2023, Vol. 36 
