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Author:

Qiao, Jing (Qiao, Jing.) | Sun, Li-Shan (Sun, Li-Shan.) | Zeng, Wei (Zeng, Wei.) (Scholars:曾薇) | Rong, Jian (Rong, Jian.) (Scholars:荣建)

Indexed by:

EI Scopus

Abstract:

Road traffic is a random and dynamic system while it is difficult to describe the traffic flow characteristics correctly, through the existing non-probability parameters. Evaluating the road network with such parameters will produce difficulty in traffic system management and planning. This paper guided by the theories of probability and traffic flow with primary consideration of random arrival of vehicle, aiming at the shortcomings of the past evaluation method, the unblocked thresholds in the urban roads of different levels are determined according to field survey of large numbers of drivers. On this basis, the parameter values of the BPR and the regression function are calibrated combined with road conditions in Tianjin of China and this paper gets the probability model of unblocked flow based on the random arrival of vehicle. Besides, the model is verified with simulation software Paramics. It is anew idea about the calculation method of unblocked reliability. © 2013 Asian Network for Scientific Information.

Keyword:

Street traffic control Traffic surveys Probability Roads and streets Computer software Function evaluation Reliability Vehicles

Author Community:

  • [ 1 ] [Qiao, Jing]Beijing University of Technology, Key Lab of Traffic Engineering, Beijing, 100124, China
  • [ 2 ] [Sun, Li-Shan]Beijing University of Technology, Key Lab of Traffic Engineering, Beijing, 100124, China
  • [ 3 ] [Zeng, Wei]Tianjing Municipal Engineering Design and Research Institute, 300051, China
  • [ 4 ] [Rong, Jian]Beijing University of Technology, Key Lab of Traffic Engineering, Beijing, 100124, China

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Source :

Information Technology Journal

ISSN: 1812-5638

Year: 2013

Issue: 24

Volume: 12

Page: 8657-8662

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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