Abstract: Accident risk prediction of traffic accidents through graph convolution networks is a research hotspot in the transportation field.However,the existing researches on using graph convolution networks for accident risk prediction lack semantic adjacency in graph construction and unable to perform adaptive learning of graph weights.To address these problems,a data-driven,multi-granularity and multi-view spatio-temporal topology graph is constructed based on multi-source traffic big data to realize the accurate modeling of spatio-temporal correlation and dependency in traffic network.The nodes on the graph provide a comprehensive description of the traffic state from time and space two dimensions,while the edges show the abstract adjacency relationship between roadways from geography and semantics two perspectives.Then,a dynamic spatio-temporal graph network based on the spatio-temporal topology graph is designed to achieve accurate prediction of roadway-level traffic accident risk.The model introduces spatial graph network layers with multi-headed attention mechanism to learn spatial correlations,while temporal learning units based on 1-D dilated convolution are used to capture short-time dependencies and long-time periodicity.According to large-scale experiments carried out on real traffic data in Beijing area,our method achieves the recall of 0.899 and the F-1 Score of 0.860.Meanwhile,there are also improvements in other indicators comparing to mainstream methods.

Key words: Traffic accident risk prediction, Graph neural network, Spatio-Temporal data mining