Research on the road traffic accident prediction based on SARIMA-LSTM model

This study explores the necessity and importance of improving the accuracy of road traffic accident (RTAS) prediction in terms of human casualties caused by road traffic accidents globally. Taking China as an example, we analysed the increase in RTAS due to population and economic development in the last decade. By comparing the performance of three models, SARIMA, LSTM and SARIMA-LSTM, in RTAS prediction in Jilin Province, we find that the SARIMA-LSTM model significantly outperforms the SARIMA and LSTM models alone. For example, using 2019 data, the RMSE, MAE, and MAPE of the SARIMA-LSTM model improved by 51.79%, 50.84%, and 37.17%, compared with SARIMA, and by 27.51%, 21.85%, and 18.08%, compared with the LSTM model. This indicates that the hybrid model has higher prediction accuracy and adaptability when dealing with complex time series problems. This study provides a new perspective for RTAS prediction and demonstrates that combining traditional time series models with deep learning models can effectively improve the accuracy and robustness of prediction.