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CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.

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第一作者： Lu,Zeng

第一单位： School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.;State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China.;Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341000, China.

作者： Lu,Zeng ^[1] ; Zinuo,Li ^[2] ; Jie,Yang ^[3] ; Xinyue,Xu ^[4]

作者单位： School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.;State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China.;Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341000, China. ^[1] School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China. ^[2] School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.;Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341000, China. ^[3] State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China. ^[4]

关键词 CEEMDAN-IPSO-LSTM combination model complete ensemble empirical mode decomposition with adaptive noise improved particle swarm optimization long-short term memory neural network short-term passenger flow prediction urban rail transit

主题词人类(Humans);运输(Transportation);公共卫生(Public Health);错|牙合|(Malocclusion)

DOI 10.3390/ijerph192416433

PMID 36554314

发布时间 2023-01-24

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