Short-term Passenger Inbound Flow Prediction for Urban Rail Transit Based on CNN-LSTM
Accurately and effectively short-term passenger inbound flow prediction at urban rail transit stations is crucial for supporting safer and more efficient operation of urban rail transit.Based on the characteristics of Convolutional Neural Networks(CNN)and Long Short-Term Memory networks(LSTM),a short-term inbound passenger flow prediction method is proposed using a combined CNN-LSTM model.By analyzing data from a typical station,the patterns and influencing factors of inbound passenger flow during regular commuting and exhibition events are revealed,while also incorporating external features such as date types and exhibition activities.Finally,a case study is conducted at the target station,and the results are compared with two benchmark models.The CNN-LSTM combination model achieves higher accuracy across MAE,RMSE,and WMAPE metrics,confirming its precision and practical value in predicting inbound passenger flow for such stations.
Urban rail transitShort-term passenger flow predictionFeature engineeringMachine learningCombined model
NETL
NSTL
万方数据
