Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) that transforms city-wide fiber-optic cables into a large-scale strain sensing array has shown the potential to revolutionize urban traffic monitoring by providing a fine-grained, scalable, and low-maintenance monitoring solution. However, there are challenges that limit DAS's real-world usage: noise contamination and interference among closely traveling cars. To address the issues, we introduce a self-supervised U-Net model that can suppress background noise and compress car-induced DAS signals into high-resolution pulses through spatial deconvolution. To guide the design of the approach, we investigate the fiber response to vehicles through numerical simulation and field experiments. We show that the localized and narrow outputs from our model lead to accurate and highly resolved car position and speed tracking. We evaluate the effectiveness and robustness of our method through field recordings under different traffic conditions and various driving speeds. Our results show that our method can enhance the spatial-temporal resolution and better resolve closely traveling cars. The spatial deconvolution U-Net model also enables the characterization of large-size vehicles to identify axle numbers and estimate the vehicle length. Monitoring large-size vehicles also benefits imaging deep earth by leveraging the surface waves induced by the dynamic vehicle-road interaction.