Traffic Flow and Speed Monitoring Based On Optical Fiber Distributed Acoustic Sensor

TL;DR

This paper introduces a novel traffic flow monitoring method using optical fiber distributed acoustic sensors, employing advanced algorithms for vehicle trajectory extraction, demonstrating improved accuracy over classical methods in various scenarios.

Contribution

The paper presents an innovative vehicle trajectory extraction algorithm using optical fiber DAS, enhancing traffic monitoring accuracy and reliability in diverse environments.

Findings

Outperforms classical Hough and Radon transform methods in vehicle trajectory extraction

Enables accurate traffic flow statistics with low-cost acoustic sensors

Effective in highway, tunnel, and city scenarios

Abstract

In the realm of intelligent transportation systems, accurate and reliable traffic monitoring is crucial. Traditional devices, such as cameras and lidars, face limitations in adverse weather conditions and complex traffic scenarios, prompting the need for more resilient technologies. Thispaperpresentstrafficflowmonitoringmethodusingopticalfiber-baseddistributedacoustic sensors(DAS).Aninnovativevehicletrajectoryextractionalgorithmisproposedtoderivetraffic flow statistics. In the processing of optical fiber waterfall diagrams, Butterworth low-pass filter and peaks location search algorithm are employed to determine the entry position of vehicles. Subsequently, line-by-line matching algorithm is proposed to effectively track the trajectories. Experiments were conducted in highway, tunnel and city scenarios. Visualizations show that ourapproachnotonlyextractsvehicletrajectoriesmoreaccuratelythantheclassicalHoughand Radon transform-based methods and MUSIC beamforming algorithm, but also facilitates the calculation of traffic flow information using the low-cost acoustic sensors. It provides a new reliable means for traffic flow monitoring which can be integrated with existing methods like vision-based method.