Traffic estimation in road networks via compressive sensing

Abstract

The recent advances of compressive sensing (CS) have witnessed a great potential of traffic condition estimation in road networks. In this paper, we propose a traffic estimation approach that applies compressive sensing technique to achieve a city-scale traffic estimation with only a small number of vehicle probes. In particular, we construct a new type of random matrix for CS which can significantly reduce the number of vehicle probes for traffic estimation. Furthermore, we also propose a novel representation matrix to better exploit the correlations of road network to improve the accuracy of traffic estimation. We analyze the incoherence between random measurement matrix and sparsity representation basis. Finally, we validate the effectiveness of the proposed approach through extensive simulations by real-world dataset.