Research on Traffic Accident Prediction Algorithm Based on Convolution Neural Network and Deep-Insight

Traffic accident prediction is one of the important ways to improve road safety and realize route planning. In order to improve the accuracy of the traffic accident prediction model, this paper establishes a traffic accident prediction model based on convolutional neural network, uses Deepinsight data reconstruction method to improve the input of model data, and studies the impact of Deepinsight data reconstruction on model performance. Firstly, the accident information of a certain expressway in Guangdong from 2020 to 2022 is selected as the data base of the study, and the data set is established based on the time of the accident, the vehicle speed, traffic density, the proportion of large vehicles on the road section and its upstream and downstream sections as the influencing factors. Secondly, use the data reconstruction method of Deepinsight to reconstruct the datasets. Finally, the data sets before and after reconstruction are input into the model for training and testing, and the model is evaluated and compared through three prediction performance indicators to verify the impact of Deepinsight data reconstruction on the model performance. The research results show that after the reconstruction of Deepinsight data, the total accuracy of the model is improved by 3.84%, and the sensitivity and accuracy of the model for accident samples are improved by 5.38% and 2.91% respectively. On the whole, Deepinsight data reconstruction can improve the accuracy of model prediction.