铁道科学与工程学报2026，Vol.23Issue(3)：1083-1095,13.DOI:10.19713/j.cnki.43-1423/u.T20250772
基于CLTAttention的大型客站多进站口客流协同预测方法
CLTAttention-based approach for collaborative passenger flow forecasting across multiple entrances in large railway stations
摘要
Abstract
Modern large railway stations commonly adopt multiple entrance configurations,but significant spatial heterogeneity exists in passenger distribution across different gates.Current management systems primarily rely on ticketing data to obtain aggregate passenger volume,lacking precise predictions of time-varying passenger distribution at individual entrances.This can lead to static resource allocation,causing localized congestion during peak hours and reduced passenger throughput efficiency.To address these limitations,this paper proposes a collaborative prediction framework for multi-gates passenger flow that enhances forecasting accuracy and enables dynamic resource optimization.By integrating multi-source data including historical ticket sales(simulated from historical boarding counts),train schedules,and gate transit records,this study constructed a comprehensive dataset encompassing spatiotemporal flow patterns,periodic characteristics,and peak-hour features.The proposed CLTAttention model synergistically combined the local feature extraction capability of Convolutional Neural Networks(CNN),the temporal modeling strength of Long Short-Term Memory(LSTM)networks,and the adaptive weight allocation of temporal attention mechanisms to achieve spatiotemporal collaborative prediction.Dynamic weight visualization was incorporated to enhance model interpretability.Experimental results from a major railway station demonstrate CLTAttention's superior performance.Achieving a 4.0%reduction in MAE,6.4%improvement in RMSE,and 0.938 R2 score compared to benchmark models.The visualization component can effectively reveal underlying spatiotemporal patterns in passenger distribution.The prediction outputs facilitate data-driven dynamic resource allocation strategies,providing both theoretical foundations and practical solutions for station operation optimization.This research can contribute an effective multi-gates passenger flow prediction system that significantly improves forecasting accuracy in large railway stations,with substantial practical implications for congestion mitigation and resource utilization optimization during peak operational periods.关键词
大型铁路客站/卷积神经网络/长短期记忆网络/时间注意力机制/多进站口客流预测Key words
major railway stations/convolutional neural network(CNN)/long short-term memory network(LSTM)/temporal attention mechanism/multi-gates passenger flow prediction分类
交通工程引用本文复制引用
张馨予,史天运,李昊光,李超..基于CLTAttention的大型客站多进站口客流协同预测方法[J].铁道科学与工程学报,2026,23(3):1083-1095,13.基金项目
中国国家铁路集团有限公司系统性重大项目(P2024X001) （P2024X001）
