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Real-Time Passenger Flow Anomaly Detection Considering Typical Time Series Clustered Characteristics at Metro Stations

Auteur(s): (Postdoctoral, Dept. of Computer Science and Technology, Key Laboratory of Embedded System and Service Computing, Ministry of Education, College of Transportation Engineering, Tongji Univ., Shanghai 201804, China; Key Laboratory of Road and Traffic Enginee)
(Associate Professor, College of Transportation Engineering, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji Univ., 4800 Cao’an Rd., Jiad)
ORCID (Director, USDOT Center for Advanced Multimodal Mobility Solutions and Education; Professor, Dept. of Civil and Environmental Engineering, Univ. of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223 (corresponding author))
(Master Candidate, College of Transportation Engineering, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji Univ., 4800 Cao’an Rd., Jiading)
(Senior Engineer, Shanghai No. 4 Metro Operation Co., Ltd., 288 North Zhongshan Rd., Zhabei District, Shanghai 200070, China.)
Médium: article de revue
Langue(s): anglais
Publié dans: Journal of Transportation Engineering, Part A: Systems, , n. 4, v. 146
Page(s): 04020015
DOI: 10.1061/jtepbs.0000333
Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1061/jtepbs.0000333.
  • Informations
    sur cette fiche
  • Reference-ID
    10580429
  • Publié(e) le:
    08.03.2021
  • Modifié(e) le:
    08.03.2021
 
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