Subway passenger flow is affected by many factors,and accurate passenger flow prediction data facili-tates to the formulation of more efficient traffic control schemes and passenger flow control schemes.In order to improve the accuracy of passenger flow prediction,a short-term subway passenger flow prediction method based on multidimensional predictable features and temporal convolutional network-long short-term memory(TCN-LSTM)has been proposed.Considering the influence of external factors,the prediction accuracy was improved and the fea-ture space was reduced to overcome the initial overly-complex model caused by redundant feature data.The long short-term memory(LSTM)network layer input was constructed by integrating the time series features of passenger flow extracted from the temporal convolutional network(TCN)and the set of predictable feature states.The LSTM network layer input was used to learn the long-term and short-term dependence of passenger flow and external influ-encing factors,so as to achieve short-term passenger flow prediction under multiple scenarios such as working days,holidays and different weather conditions.Based on the Automatic Fare Collection System(AFC)data of a subway station in a southwest city,the short-term passenger flow prediction results of ARIMA,TCN,LSTM and TCN-LSTM models were compared.The overall mean absolute error(MAE)value of the TCN-LSTM method was 27％-48％lower than the other methods,and the mean squared error(MSE)value was 13％-35％lower,and the mean absolute percentage error(MAPE)value decrease by 2.8％-6.7％,which indicates that the TCN-LSTM model gives a better prediction of passenger flow.In addition,comparative experiments show that incorporating the extracted predictable feature data significantly reduces the prediction error evaluation metrics of the TCN-LSTM model on the test set.Thus the proposed method can effectively improve the prediction accuracy of short-term subway passenger flow.
维普
万方数据