Subway short-time pull-in passenger flow prediction system based on double-layer decomposition and deep learning and implementation method

• Main Authors: JIA MEICHUN, ZHANG NING, ZHANG YIXIN, ZHANG YIRAN, WANG JIAN, WU JUAN, LU SAIJIE, LI DAOQUAN, LI JIAJING, XU JIANZHOU, HE YUEQI, WANG HONGBO, LIN LEI
• Format: Patent
• Language: Chinese; English
• Published: 11.02.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention discloses a subway short-time pull-in passenger flow prediction system based on double-layer decomposition and deep learning and an implementation method thereof. The method comprises five steps of data preparation, passenger flow time sequence decomposition, EMD algorithm double-layer decomposition, correlation analysis and grouping, model training and prediction result output. Noise interference is effectively suppressed, the prediction precision of the subway short-time passenger flow is improved, and a theoretical basis is provided for improving the operation efficiency and the service level of urban rail transit. 本发明公开了基于双层分解和深度学习的地铁短时进站客流预测系统及实施方法，通过数据准备、客流时间序列分解、EMD算法双层分解、相关性分析和分组、模型训练及预测结果输出五个步骤，利用STL算法和EMD算法双层分解原始客流序列的方法，有效抑制了噪声干扰，提高了地铁短时客流的预测精度，为提高城市轨道交通运营效率和服务水平提供了理论基础。