Urban rail transit short-term pull-in passenger flow volume prediction method

Urban rail transit short-term pull-in passenger flow volume prediction method

The invention provides an urban rail transit short-term pull-in passenger flow volume prediction method, comprising the following steps: constructing a Wave-LSTM prediction model, and training the Wave-LSTM prediction model by using a training data set to obtain a historical pull-in quantity data se...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: WU JIANJUN, LAN HUIFENG, XUE QIUCHI, YIN HAODONG, QU YUNCHAO, SUN HUIJUN, ZUO XUTAO, YANG XIN
Format: Patent
Sprache:chi ; eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:The invention provides an urban rail transit short-term pull-in passenger flow volume prediction method, comprising the following steps: constructing a Wave-LSTM prediction model, and training the Wave-LSTM prediction model by using a training data set to obtain a historical pull-in quantity data sequence of a to-be-predicted station; decomposing the historical pull-in quantity data sequence by utilizing the selected wavelet function and the decomposition times to obtain a reconstructed pull-in quantity data sequence; and inputting the reconstructed inbound amount data sequence into a trainedWave-LSTM prediction model, outputting a corresponding predicted inbound amount data sequence by the Wave-LSTM prediction model, and adding all predicted inbound amount data sequences to obtain predicted inbound amount passenger flow data of the to-be-predicted station in all time periods. According to the urban rail transit short-term pull-in passenger flow volume prediction method, the advantages of a mathematical micro