Distributed Acoustic Sensing for Traffic Monitoring via Cross-Modal Supervision

Abstract

Continuous traffic monitoring is critical for accurate structural load assessment and fatigue life estimation of highway bridges. However, conventional vision-based methods suffer from limitations such as line-of-sight restrictions, susceptibility to adverse weather and lighting conditions, and limited spatial coverage. Distributed acoustic sensing (DAS) offers a robust alternative by repurposing existing telecommunications dark fiber into dense, kilometer-scale sensor arrays. Nevertheless, interpreting the complex DAS signals generated by vehicular traffic remains challenging due to overlapping dynamic signatures and the scarcity of ground-truth data for model training. To overcome this, we present a cross-modal (vision-to-optic) supervision framework that transforms existing fiber infrastructure into a traffic monitoring system. We deployed a synchronized camera–DAS testbed along a roadway segment served by dark fiber. Video data is processed using modern computer vision foundation models SAM3 to automatically extract vehicle trajectories and classifications. These camera-derived labels supervise a deep sequence learning model trained on the corresponding DAS strain data. Once trained, the fiber-optic system independently achieves accurate vehicle detection, classification, localization, and speed estimation. Finally, we demonstrate how these continuous, DAS-derived traffic metrics can be directly translated into dynamic load profiles, providing a scalable, continuous monitoring solution for bridge fatigue and structural health assessment.