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

Autor(en): (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.)
Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Journal of Transportation Engineering, Part A: Systems, , n. 4, v. 146
Seite(n): 04020015
DOI: 10.1061/jtepbs.0000333
Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1061/jtepbs.0000333.
  • Über diese
    Datenseite
  • Reference-ID
    10580429
  • Veröffentlicht am:
    08.03.2021
  • Geändert am:
    08.03.2021
 
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