Research and Implementation of Urban Traffic Accident Risk Prediction in Dynamic Road Network
Accident risk prediction of traffic accidents through graph convolution networks is a research hotspot in the transpor-tation field.However,the existing researches on using graph convolution networks for accident risk prediction lack semantic adja-cency 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 compre-hensive description of the traffic state from time and space two dimensions,while the edges show the abstract adjacency relation-ship 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 in-troduces 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.
Traffic accident risk predictionGraph neural networkSpatio-Temporal data mining
NETL
NSTL
万方数据
