A Distributed Acoustic Sensor System for Intelligent Transportation using Deep Learning

Intelligent transport systems (ITS) are pivotal in the development of sustainable and green urban living. ITS is data-driven and enabled by the profusion of sensors ranging from pneumatic tubes to smart cameras. This work explores a novel data source based on optical fibre-based distributed acoustic sensors (DAS) for traffic analysis. Detecting the type of vehicle and estimating the occupancy of vehicles are prime concerns in ITS. The first is motivated by the need for tracking, controlling, and forecasting traffic flow. The second targets the regulation of high occupancy vehicle lanes in an attempt to reduce emissions and congestion. These tasks are often conducted by individuals inspecting vehicles or through the use of emerging computer vision technologies. The former is not scale-able nor efficient whereas the latter is intrusive to passengers' privacy. To this end, we propose a deep learning technique to analyse DAS signals, which are correlated to the weight and other physical features of vehicles, to achieve load detection through continuous sensing with privacy protection. We propose a deep learning method for processing DAS signals, achieving 86% vehicle classification accuracy and 94-98% in occupancy detection based on DAS data collected under controlled conditions.