Informer-based OD passenger flow prediction for high-speed rail transit
ZHANG Tao
Abstract：To address the challenges of long-term OD passenger flow forecasting for high-speed rail trains,characterized by large data volumes and high prediction complexity,this study proposes a pre-diction method based on the Informer model.First,the connotation of the OD passenger flow forecast-ing problem is defined,and a research framework encompassing data acquisition,data processing,and flow prediction is developed.Second,historical data from the passenger train timetable system and the passenger transportation big data platform are collected to extract key features influencing OD passen-ger flow.Then,an Informer-based prediction model is constructed,leveraging a decoder structure em-bedded with a probabilistic sparse self-attention mechanism to capture the long-range dependencies within OD flow data across different trains.The model assigns higher attention weights to critical time points and generates predicted passenger flow trends through the decoder.Finally,a case study using high-speed rail trains on the Shanghai Hongqiao-Beijing South segment of the Beijing-Shanghai HSR line validates the effectiveness of the proposed method.Results demonstrate that the Informer model achieves superior prediction accuracy compared to the Transformer,Gated Recurrent Unit(GRU),and Long Short-Term Memory(LSTM)models,improving training set accuracy by 2.11％,6.97％,and 6.79％,and test set accuracy by 1.42％,7.19％,and 8.24％,respectively.The results of this study provide a robust data-driven basis for refined passenger transport planning in high-speed rail op-erations and offer valuable reference for optimizing high-speed rail passenger service strategies.
Keywords：high-speed railwaypassenger flow predictiondeep learningInformer model
Publication Date：2025-06-30
Online Publishing Date：2025-08-21(First online date of this platform, not the publication date of the document)
Pages：10( 90-99 )