Monitoring Urban Traffic Dynamics at Fine Spatiotemporal Resolution Using Distributed Acoustic Sensing and Deep Learning

2026-09-23

AI Summary

This study addresses the challenge of achieving real-time, meter-level spatial and second-level temporal resolution in urban traffic monitoring. To overcome this limitation, this work integrates distributed acoustic sensing (DAS) with deep learning to transform subsurface fiber-optic vibration signals into spatiotemporal representations for vehicle trajectory inference. Furthermore, a hybrid training strategy combining synthetic data with manual annotations is proposed to enhance model generalization. The resulting system establishes an efficient urban traffic observation framework that significantly improves vehicle detection accuracy under high-noise and congested conditions. Ultimately, it enables precise analysis of fine-grained traffic flow dynamics, congestion evolution, and event-driven variations, offering a robust solution for high-resolution urban mobility sensing.

Abstract

Mapping the distribution of traffic dynamics at high spatiotemporal resolution is a fundamental question in transportation research. Distributed acoustic sensing (DAS), an innovative seismic observation tool, emerges as a promising solution for real-time urban traffic monitoring at high spatial and temporal scales. Distributed acoustic sensing repurposes existing underground fiber-optic cables as dense, continuous sensor arrays, enabling passive and privacy-preserving monitoring of roadway traffic activity at meter-level spatial and second-level temporal resolution. This study examines whether integrating DAS and deep learning models can serve as a continuous and efficient urban traffic observatory for revealing urban traffic dynamics (i.e. traffic volume and congestion, event-driven changes) at high spatiotemporal resolution. Using a DAS deployment along a roadway network in the City of College Station, Texas, USA, this study develops a deep learning-empowered analytical framework that converts raw ground vibration waveforms into spatiotemporal representations, detects vehicle trajectory, and infers traffic states from aggregated traffic volume and speed. A hybrid training strategy combining synthetic and manually annotated DAS images is used to improve vehicle detection under noisy and congested conditions, with model outputs further aggregated to characterize system-level traffic dynamics.

Problem

• Urban traffic dynamics
• Spatiotemporal resolution
• Distributed acoustic sensing
• Traffic monitoring

Innovation

• Distributed Acoustic Sensing
• Deep Learning
• Urban Traffic Monitoring
• Spatiotemporal Resolution
• Hybrid Training Strategy