Optimal Traffic Sensor Location for Origin–Destination Estimation Using a Compressed Sensing Framework

A series of flow estimation problems, especially origin–destination estimation, involves optimally locating sensors on a transportation network to measure traffic counts. As compressed sensing (CS) provides a new method to solve the estimation problem, its sensor location strategy needs to be researched in order to facilitate the reconstruction. This paper first points out that the accurate flow recovery is difficult by introducing a necessary condition, and then categorizes the location determination into two cases: sensor number with restriction and without restriction. For both cases, we elucidate their theoretical foundations of locating methods and propose an algorithm based on column coherence minimization, which optimally facilitates the reconstruction for CS framework. Numerical experiments indicate that the selected sensor locations fit the flow recovery and the proposed algorithm, compared with other methods, can lead to a slightly better result under certain observations.

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