EMD-Informer Method for Prediction of Daily Passenger Flow of High-speed Railways
The rational prediction of rail passenger flow is an important basis for the decision-making of the transportation organization scheme.Based on historical high-speed rail ticket data,as well as empirical mode decomposition(EMD)and attention mechanism in machine deep learning,an EMD-Informer combined prediction method for daily passenger flow of high-speed railways was developed.First,the EMD method was used to decompose the passenger flow sequence to obtain railway line modal components with periodic characteristics and intrinsic feature.With the modal components trained and predicted by the Informer model,the internal regulation of passenger flow data and the essential features of flow data sequence were captured by the multi-head attention mechanism.Then the high-accuracy predictive value of high-speed rail passenger flow could be obtained by the reconfiguration of the predictive values of modal components.Meanwhile,according to a large number of experiments and the characteristics of problems,hyperparameter setting rules were formulated which could be used in actual situation.The numerical analysis of Beijing-Shanghai high-speed railway shows that,compared with existing typical prediction methods,the EMD-Informer method has significantly smaller errors in both single-step and multi-step passenger flow prediction.
high-speed railwayprediction of passenger flowempirical mode decompositionattention mechanismIn-former model
万方数据
维普
