Next-Generation Traffic Monitoring with Distributed Acoustic Sensing Arrays and Optimum Array Processing

Distributed Acoustic Sensing (DAS) is a recent fibreoptic sensing technology that can turn glass fibre telecommunication cables into arrays of thousands of sensors. Among its numerous applications in seismology and engineering, DAS offers great potential for traffic monitoring. While individual vehicles (cars) can be clearly seen in the recordings of roadside DAS systems, the extraction and analysis of these signals is less straightforward, particularly when the traffic is dense. In this contribution, we propose two beamforming models for the detection of vehicles that exploit the spatio-temporal density of DAS measurements. We demonstrate the potential of these techniques to detect individual vehicles and extract their velocity with high resolution.