Uncovering transportation networks from traffic flux by compressed sensing

Si-Qi Tang, Zhesi Shen (), Wen-Xu Wang and Zengru Di

The European Physical Journal B: Condensed Matter and Complex Systems, 2015, vol. 88, issue 8, 1-7

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

Keywords: Statistical and Nonlinear Physics (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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DOI: 10.1140/epjb/e2015-60234-y

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