Edge Computing in Distributed Acoustic Sensing: An Application in Traffic Monitoring

TL;DR

The proposed workflow effectively counts vehicles and estimates their speed with only tens of seconds latency, enabling real-time traffic monitoring on the edge, and successfully detected structural responses in the bridge, highlighting its potential use in structural health monitoring.

Abstract

Distributed acoustic sensing (DAS) technology leverages fiber optic cables to detect vibrations and acoustic events, which is a promising solution for real-time traffic monitoring. In this paper, we introduce a novel methodology for detecting and tracking vehicles using DAS data, focusing on real-time processing through edge computing. Our approach applies the Hough transform to detect straight-line segments in the spatiotemporal DAS data, corresponding to vehicles crossing the Astfjord bridge in Norway. These segments are further clustered using the Density-based spatial clustering of applications with noise (DBSCAN) algorithm to consolidate multiple detections of the same vehicle, reducing noise and improving accuracy. The proposed workflow effectively counts vehicles and estimates their speed with only tens of seconds latency, enabling real-time traffic monitoring on the edge. To validate the system, we compare DAS data with simultaneous video footage, achieving high accuracy in vehicle detection, including the distinction between cars and trucks based on signal strength and frequency content. Results show that the system is capable of processing large volumes of data efficiently. We also analyze vehicle speeds and traffic patterns, identifying temporal trends and variations in traffic flow. Real-time deployment on edge devices allows immediate analysis and visualization via cloud-based platforms. In addition to traffic monitoring, the method successfully detected structural responses in the bridge, highlighting its potential use in structural health monitoring.

Figures (17)

• Figure 1: The Åstfjord bridge is located 85 km from Trondheim, Norway.
• Figure 2: (a) The Åstfjord bridge spans 735 meters. (b) The fiber cable installation in the inspection walkway under the bridge surface was done by in February 2023.
• Figure 3: A 60-second sample of data (strain rate) from the Åstfjord bridge in the morning on 5 October 2023. The X-axis represents channel index, and the Y-axis represents time in UTC. Coherent strain rate data along a straight line indicate a vehicle crossings during this period (08:24:09 - 08:24:40).
• Figure 4: The fast Fourier transform converts time domain to frequency domain. The orange line represents the frequency spectrum of a large vehicle (truck). The blue line is that of a the small vehicle. The black line represents background noise. Part A of the spectrum is the quasi-static deformation signals ($<$1Hz) and part B is the vehicle-induced surface waves (15 - 25 Hz) yuan2021urbanliu2024characterizing. We focus on the low frequency A for identifying car movement.
• Figure 5: with various cutoff thresholds: (a) $f_c=2$, (b) $f_c=1$, (c) $f_c=0.5$, and (d) $f_c=0.1$. With $f_c=2$ the data still contains unnecessary high frequency content. With $f_c=0.1$, it starts blurring out the car's position. The optimal cutoff frequency is fine-tuned using a loss function together with an optimization algorithm.