Short-Term Forecast of OD Passenger Flow Based on Ensemble Empirical Mode Decomposition

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The development of metro systems can be a good solution to many problems in urban transport and promote sustainable urban development. A metro system plays an important role in urban public transit, and the passenger-flow forecasting is fundamental to assisting operators in establishing an intelligent transport system (ITS). In order to accurately predict the passenger flow of urban metros in different periods and provide a scientific basis for schedule planning, a short-term metro passenger-flow prediction model is constructed by integrating ensemble empirical mode decomposition (EEMD) and long short-term memory neural network (LSTM) to solve the problem that the existing empirical mode decomposition (EMD) is prone to modal aliasing. According to the processed metro-card data, the time series of historical OD data of metro passenger flow is obtained. After EEMD modal decomposition, several intrinsic mode functions sub-items and residual items are obtained. Then, an LSTM network is constructed for prediction. The time step of the network is decided according to the partial autocorrelation functions. The prediction results of intrinsic mode function (IMF) and residual items are integrated to obtain prediction results. The station is classified according to the land types around the station, and the model is tested by using the metro automatic fare collection (AFC) data. In order to test the actual prediction, a different number of training set samples are selected to predict. The measured data of the next day is continuously added to the original training set to compare the prediction accuracy. The results show that the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the EEMD-LSTM model are better than the EMD-LSTM in predicting the OD value of commercial–residential stations and scenic–residential stations. Compared with the EMD-LSTM model, the EEMD-LSTM model showed an average reduction by 3.112% in MAPE values and 15.889 in RMSE, indicating that the EEMD-LSTM has higher prediction accuracy, and EEMD-LSTM model has higher accuracy in short-term metro passenger-flow prediction. The average MAPE for the 35-to-42-day historical data sample decreased from 13.02% to 10.39% with a decreasing trend. It shows that the prediction accuracy keeps improving as the training set samples increase.