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OA

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.