Research on Traffic Crash Prediction Based on CNN-LSTM Model

Accurate prediction of the number of traffic crashes can prejudge road traffic safety risks and fully ensure the efficient operation of the transportation system. However, existing traffic crash prediction is based on data from cities outside China. The data of 25,628 traffic crashes in a Chinese megacity from 2012 to 2013 were collected, and a traffic crash prediction model based on a convolutional neural network (CNN) and long short memory network (LSTM) was proposed. Firstly, the feature extraction and superposition of crash data were carried out based on the CNN model. Secondly, the LSTM model extracted time series features to predict traffic crashes. Finally, the CNN-LSTM composite model, GM (1,1), ARIMA model, and BP neural network model were comprehensively compared. The CNN-LSTM combined model had a higher prediction accuracy than the other three. The research conclusion helps better determine the characteristics and laws of traffic crashes in megacities to provide a reliable decision-making reference for traffic management.