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Author:

Zhang Liguo (Zhang Liguo.) (Scholars:张利国) | Xu Chao (Xu Chao.) | Yu Liqian (Yu Liqian.)

Indexed by:

CPCI-S

Abstract:

The Aw-Rascle (AR) model has attracted more and more attention, because it solves the problem of the anisotropic property of traffic flow which is proposed by Daganzo. The AR model possesses an increasing function of the density which be called as the pressure function. The calibration of the pressure function is a primary work in the research of the model application. In this paper, we calibrate the pressure function of the AR model via flow-density diagram data. Our approach is to rewrite the Lighthill- Whitham-Richards (LWR) model to the form of velocity evolution equation which based on flow-density diagram and then contrast with the velocity term of AR model. Finally, we employ the PeMS detector data as flow-density diagram data to construct the pressure function, and apply the NGSIM trajectory data to be the initial and time-dependent boundary data. Via these two data sets, we predict the traffic state at interior space in the near future using the calibrated AR model and classical AR model respectively, and compare the predicted data with real traffic data to verify the feasibility and accuracy of this calibration.

Keyword:

Pressure function LWR Flow-density diagram ARZ AR

Author Community:

  • [ 1 ] [Zhang Liguo]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Xu Chao]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Yu Liqian]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100124, Peoples R China

Reprint Author's Address:

  • 张利国

    [Zhang Liguo]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100124, Peoples R China

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Source :

PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016

ISSN: 2161-2927

Year: 2016

Page: 9234-9239

Language: English

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: 6

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