A model of traffic accident prediction based on convolutional neural network

Road traffic accident is a serious threat to human life and safety of living environment. In this paper, a new road traffic accident prediction model (TAP-CNN) is established by using traffic accident influencing factors, such as traffic flow, weather, light to build a state matrix to describe the traffic state and CNN model. This paper uses samples to test the accuracy of the new model. The experimental results show that the TAP-CNN model is more effective than the traditional neural network model to predict the traffic accident. It provides a reference for the forecast of the traffic accident.