The invention discloses a short-term subway pull-in passenger flow volume prediction method, which comprises the following steps of A, acquiring pull-in passenger flow volume data of a plurality of different stations within a specified time range to obtain an initial pull-in data set; b, acquiring a primary residual sequence of each piece of initial data in the initial pull-in data set; c, acquiring a cubic decomposition subsequence of a primary residual sequence of each piece of initial data in the initial pull-in data set; d, taking all primary decomposition subsequences of the initial data in the obtained initial data set except the first subsequence and a tertiary decomposition subsequence of the initial data as a data set of an LSTM neural network, and training to obtain a trained LSTM neural network; and E, taking the pull-in passenger flow data of the to-be-predicted station in Q days before the to-be-predicted date as initial data, and performing passenger flow prediction by using the trained LSTM neural network. According to the method, the short-term subway pull-in passenger flow volume can be accurately predicted.

    本发明公开了一种短期地铁进站客流量预测方法,包括以下步骤:A:采集若干不同站点在指定时间范围内的进站客流量数据,得到初始进站数据集;B:获取初始进站数据集中的每一个初始数据的一次残差序列;C:获取初始进站数据集中的每一个初始数据的一次残差序列的三次分解子序列;D:将获得的除第一个子序列以外的初始数据集中初始数据的所有一次分解子序列,以及初始数据的三次分解子序列,作为LSTM神经网络的数据集,训练得到训练后的LSTM神经网络;E:将待预测站点在待预测日期前Q天的进站客流量数据作为初始数据,利用训练后的LSTM神经网络进行客流量预测。本发明能够较为准确地对短期地铁进站客流量进行预测。


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    Titel :

    Short-term subway station entering passenger flow volume prediction method


    Weitere Titelangaben:

    一种短期地铁进站客流量预测方法


    Beteiligte:
    LYU LINGLING (Autor:in) / HU DELAI (Autor:in) / CHANG RUI (Autor:in) / WANG YAHUI (Autor:in) / BAI LEI (Autor:in) / ZHOU YU (Autor:in) / ZHANG HONGTAO (Autor:in) / AN SIWEI (Autor:in) / YANG DELONG (Autor:in) / ZHUO RUIQIN (Autor:in)

    Erscheinungsdatum :

    18.07.2023


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung



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