Objective:Efficient and accurate short-term passenger flow forecasting is a critical prerequisite for urban rail transit operation and management.To enhance the precision of short-term passenger flow forecasts,a combined CEEMDAN-LSTM model was proposed based on time series clustering.Methods:Using the DTW(Dynamic Time Warping)distance as a metric,the Kmeans algorithm was employed to categorize the passenger flow time series.On this basis,the CEEMDAN algorithm was applied to decompose the time series,mitigating sample noise interference.Subsequently,the decomposed components were fed into the LSTM(Long Short-term Memory)model for prediction.Results:The prediction errors of the CEEMDAN-LSTM model under three types of passenger flow time series were smaller than those of the other four baseline models and could effectively reflect the trend of short-term passenger flow.The prediction accuracy and timeliness of the prediction model that considerd time series clustering is better than that of the prediction model under no classification.Conclusion:The model was empirically analyzed using short-term inbound passenger flow data at Hefei South Railway Station subway and compared with other four forecasting models,CEEMDAN-LSTM model had higher prediction accuracy and could effectively reflect the changing trend of actual passenger flow curve.
维普
万方数据