%0 Journal Article
%A LUO Dan
%A SHE Hongyan
%A WANG Lei
%A ZHAO Dong
%A NING Jing
%T A Road-Level Traffic Accident Risk Prediction Method
%D 
%R 10.13190/j.jbupt.2021-142
%J Journal of Beijing University of Posts and Telecommunications
%P 72-78
%V 45
%N 2
%X Existing deep-learning-based methods always divide the predicted region into grids which does not conform to the natural form of accidents,while accidents generally occur on roads. Aiming at the problem of road-level accident risk prediction, an urban traffic accident risk prediction model scale-reduced attention based on graph convolution network (SA-GCN) is proposed. First, the model effectively combines historical long-term and short-term risks, external weather features and a gated graph convolution structure to capture spatial-temporal correlations, and then an attention mechanism is applied to obtain dynamic representations of spatial-temporal features. After that, to solve the problem of the sparseness and spatial heterogeneity of accident data, a scale reduction module which uses the accident risk of the coarse-grained area after clustering, is designed to guide the accident risk prediction at road level. Experimental results on real traffic datasets performance measurement system show that the SA-GCN model performs better than six baseline models, and achieves 11% higher prediction accuracy than the state-of-the-art model.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2021-142