计算机与现代化Issue(4)：12-18,7.DOI:10.3969/j.issn.1006-2475.2025.04.003
基于图注意力与图卷积网络的交通事故预测方法
Traffic Accident Prediction Method Based on Graph Attention and Graph Convolutional Network
摘要
Abstract
Traffic accidents result in significant losses to individuals and society.To enhance the accuracy of traffic accident pre-diction,a traffic accident prediction method based on graph attention and graph convolutional networks(GAGC)is proposed.Firstly,the method extracts complex edge feature information in the road network through an edge feature extraction module.Then,it introduces a graph attention layer to enable the network quickly focusing on nodes with frequent accidents,and uses overlapping graph attention layers to reduce information loss during feature information transmission.It also employs Dropout and Batch Normalization(BN)to balance feature importance and improve the generalization and robustness of the model.Experimen-tal results show that GAGC achieves good results,and the model can fully consider the geospatial features in complex road net-works,with better performance than five baseline models in terms of F1 index,AUC,and MAP.The ablation experiment further verifies the effectiveness and reliability of the GAGC model designed in this study.关键词
图卷积网络/图注意力/道路交通/交通事故预测Key words
graph convolutional network/graph attention/road traffic/traffic accident prediction分类
计算机与自动化引用本文复制引用
张金松,袁一博,马玉鑫..基于图注意力与图卷积网络的交通事故预测方法[J].计算机与现代化,2025,(4):12-18,7.基金项目
中央高校基本科研业务费项目(3132024302) （3132024302）
教育部人文社会科学研究项目(21YJC630066) （21YJC630066）
