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Uncovering transportation networks from traffic flux by compressed sensing

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Listed:
  • Si-Qi Tang
  • Zhesi Shen
  • Wen-Xu Wang
  • Zengru Di

Abstract

Transportation and communication networks are ubiquitous in nature and society. Uncovering the underlying topology as well as link weights, is fundamental to understanding traffic dynamics and designing effective control strategies to facilitate transmission efficiency. We develop a general method for reconstructing transportation networks from detectable traffic flux data using the aid of a compressed sensing algorithm. Our approach enables full reconstruction of network topology and link weights for both directed and undirected networks from relatively small amounts of data compared to the network size. The limited data requirement and certain resistance to noise allows our method to achieve real-time network reconstruction. We substantiate the effectiveness of our method through systematic numerical tests with respect to several different network structures and transmission strategies. We expect our approach to be widely applicable in a variety of transportation and communication systems. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Si-Qi Tang & Zhesi Shen & Wen-Xu Wang & Zengru Di, 2015. "Uncovering transportation networks from traffic flux by compressed sensing," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(8), pages 1-7, August.
    • Handle: RePEc:spr:eurphb:v:88:y:2015:i:8:p:1-7:10.1140/epjb/e2015-60234-y
    DOI: 10.1140/epjb/e2015-60234-y
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    Cited by:

    1. Pandey, Pradumn Kumar & Badarla, Venkataramana, 2018. "Reconstruction of network topology using status-time-series data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 573-583.

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    Keywords

    Statistical and Nonlinear Physics;

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