Urban rail transit passenger flow DRN-BiLSTM combined forecasting model under catastrophic weather conditions

Abstract:

To effectively address the impact of catastrophic weather on urban rail transit passenger flow organization and to explore the rules of passenger flow changes under such weather conditions, this paper conducts a prediction study on urban rail transit network passenger flow. Based on a deep residual network (DRN) and bidirectional long short-term memory (BiLSTM), a DRN-BiLSTM prediction model incorporating catastrophic weather features is constructed. The model′s performance is evaluated using metrics such as mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), and its passenger flow prediction effectiveness is verified and analyzed. The results show that compared to traditional LSTM and BiLSTM, when inputting catastrophic weather features, the DRN-BiLSTM model reduces MSE by 22.10% and 21.96%, RMSE by 10.54% and 10.46%, MAE by 3.20% and 3.95%, and increases R² by 5.01% and 2.12%, respectively. By optimizing model parameters with the grid search method, the model training loss is reduced by 36%. Practical verification demonstrates that the DRN-BiLSTM combined model constructed in this paper can effectively capture deep data features and significantly improve the accuracy of passenger flow prediction.