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Author:

Luo, Wei (Luo, Wei.) | Sun, Lishan (Sun, Lishan.) | Li, Yiqiao (Li, Yiqiao.) | Zhang, Yajun (Zhang, Yajun.) | Rong, Jian (Rong, Jian.) (Scholars:荣建)

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EI Scopus

Abstract:

Under the conditions of large population in China, the original level of service couldn't meet the needs of subway station managers. From the interdisciplinary perspective of physiology, medical science, and engineering, this study conducted a series field experiment to investigate the influence of passenger flow density on the pedestrian physiology. By quantitatively analyzing density and fluctuating rate of heart rates, a significant finding is that pedestrians' physiological characteristics are influenced by the traffic density in corridor, and the FRHR is high associated with density, presenting a linear correlation. In the end of paper, the LOS is divided into six levels, caring on the refined classification for higher density, in line with China's national conditions. Research results could provide a theoretical foundation and application reference for subway transit designer and manager. © 2018 IEEE.

Keyword:

Intelligent systems Intelligent vehicle highway systems Subway stations Underpasses Managers Heart Subways Railroads

Author Community:

  • [ 1 ] [Luo, Wei]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • [ 2 ] [Sun, Lishan]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • [ 3 ] [Li, Yiqiao]University of California, Irvine (UC Irvine), Irvine, United States
  • [ 4 ] [Zhang, Yajun]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • [ 5 ] [Rong, Jian]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China

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Source :

Year: 2018

Volume: 2018-November

Page: 1939-1943

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 9

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