A Method for Predicting Traffic Accidents Based on an Ensemble Empirical Mode Decomposition and an Optimized LSTM Model

Accurate prediction of road traffic accidents is essential to improve traffic safety effectively. Due to the frequent non-linear, fluctuating, and nonperiodic characteristics of accident data, existing algorithms have the problem of poor prediction performance. Therefore, a method for traffic prediction that uses a long short-term memory network (LSTM) combined with ensemble empirical mode decomposition (EEMD) and particle swarm optimization (PSO) is proposed. Based on a single model, the EEMD is first used to break down the noise of accident data and obtain multiple subsequences and a residual. Based on LSTM optimized by PSO, the temporal feature information extracted from the data is predicted under the optimal network structure of LSTM. Then, the prediction results of each subsequence and residual are summed to obtain the final prediction result. The results show that, compared with the EMD-PSO-LSTM, PSO-LSTM, EEMD-LSTM, and LSTM, the e_rmse of EEMD-PSO-LSTM is reduced by 8.7%, 48.3%, 53.1%, and 57.6%, respectively. Meanwhile, the e_mape is reduced by 12.4%, 36.9%, 50.6%, and 61.2%, respectively. Compared with the PSO-LSTM, the e_rmse of the EEMD-PSO-LSTM is reduced by 60.2%, the e_mape is reduced by 12.4%, and the r² is increased by 0.616 5. The PSO Introduced to optimize neural networks can help improve prediction performance. Compared with the EEMD-LSTM, the e_rmse of the EEMD-PSO-LSTM is reduced by 53.1%, the e_mape is diminished by 50.6%, and the r² is climbed to 0.807 8. The results can improve the prediction accuracy of traffic accidents and help relevant departments effectively improve road traffic safety.