Short-term high-speed rail passenger flow forecasting integrated extended empirical mode decomposition with multivariate and bidirectional support vector machine

Abstract

High-speed rail (HSR) short-term passenger flow forecasting is of great significance for dynamically adjusting operation plans and optimizing transportation resource allocation. For this reason, this paper proposes an innovative complete ensemble empirical mode decomposition with adaptive noise integrated with multivariate and bidirectional support vector machine (CEEMDAN-MBSVM) method with four key steps. First, we analyze the correlations between multiple origin–destination (OD) passenger flows and select strongly correlated ODs incorporated with their opposite OD for joint bidirectional forecasting. Second, we decompose the original passenger flow time series by using period division technique of CEEMDAN, which yield multiple intrinsic mode functions (IMFs) and a residual trend term (RES). Then we apply MBSVM to predict the IMFs of each OD and use trend extrapolation to forecast the RES. Finally, we reconstruct the predicted IMFs and RES to obtain the final bidirectional HSR OD daily passenger flows. Subsequently, we conduct a comprehensive validation exercise and significance testing, using real data from Beijing-Shanghai HSR Line, against seven prediction methods. In particular, for five selected ODs, benchmarking against EEMD-MSVM method, the best performer among the six existing models, our model reduces the minimum mean absolute percentage error (MAPE) by 1.30 % to 4.97 % and benchmarking against ARIMA model, the worst performer among the six existing models, our model reduces the MAPE by 11.57 % to 22.72 %. This research has clearly demonstrated the value of leveraging bidirectional OD data on improving short-term passenger flow forecasting.