A verification method for identifying critical segments considering highly correlated characteristics of traffic flow

Zundong Zhang, Mengyao Zhu, Jeff Ban () and Yifan Zhang
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Zundong Zhang: College of Electrical and Control Engineering, North China University of Technology, Beijing, China†Department of Civil and Environmental Engineering, University of Washington, USA
Mengyao Zhu: College of Electrical and Control Engineering, North China University of Technology, Beijing, China
Jeff Ban: #x2020;Department of Civil and Environmental Engineering, University of Washington, USA
Yifan Zhang: College of Electrical and Control Engineering, North China University of Technology, Beijing, China

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 03, 1-13

Abstract: Since critical segments on a transportation network vary over time and are determined by the nature of traffic systems, the identification of critical segments is the basis for realizing area-wide traffic coordination control and regional traffic state optimization. For decades, the identification of critical segments of dynamic traffic flow networks has attracted wide attention. In recent years, some important advances have been made in the related research on the identification of critical segments using the theory of percolation which validates the impact of critical segments by increasing the speed value of critical segments. However, most of them failed to take into account highly correlated characteristics between adjacent segments, which causes identification results cannot be validated effectively and efficiently. In this paper, we improve the existing critical segments identification methods by considering the highly correlated characteristics. A verification method based on ego-networks is proposed that improves the ego-networks speed of critical segments to verify the accuracy of identification results. The experiment shows the method can verify the validity of critical segments recognition results more accurately.

Keywords: Critical segment identification; percolation theory; ego-networks; result validation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0129183120500485

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