华东交通大学学报2026，Vol.43Issue(2)：38-44,7.
基于卷积神经网络和注意力机制的交通事故智能预测方法
Intelligent Prediction Method for Traffic Accidents Based on Convolutional Neural Network and Attention Mechanism
摘要
Abstract
In order to solve the problems that traditional research methods often have limitations in dealing with high-dimensional and complex data features in complex environments,and it is difficult to achieve high-preci-sion and robust prediction,a traffic accidents severity intelligent prediction method based on convolutional neu-ral network and attention mechanism is proposed.A multi-scale feature extraction model with attention fusion,which is called channel and multi-head attention network(CMHANet),is constructed to make full use of the ad-vantages of convolution and attention mechanism.The convolution layer is used to effectively extract spatial fea-tures in the data,while the channel attention mechanism can weight and enhance important features,suppress un-important features,and emphasize the focus on key data points.In addition,in order to capture the complex de-pendencies between different features,a multi-head attention mechanism is also introduced.Finally,experiments are conducted on the US-Accidents dataset.The experimental results show that the prediction framework with this model as the backbone achieves improvements in F1-score,precision,recall and accuracy.While improving the effect of feature extraction and association modeling for high-dimensional and complex data,the proposed model also provides a new idea for intelligent prediction of traffic accidents.关键词
卷积神经网络/注意力机制/交通事故/严重程度预测Key words
convolutional neural network/attention mechanism/traffic accidents/severity prediction分类
信息技术与安全科学引用本文复制引用
严丽平,徐嘉悦,吴康来,唐仁杰,宋凯..基于卷积神经网络和注意力机制的交通事故智能预测方法[J].华东交通大学学报,2026,43(2):38-44,7.基金项目
国家自然科学基金项目(62362031,62262022) （62362031,62262022）
江西省自然科学基金项目(20224BAB202021) （20224BAB202021）
