  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 to address this challenge through continuous
sensing and without exposing personal information. We propose a deep learning
method for processing DAS signals and achieve 92% vehicle classification
accuracy and 92-97% in occupancy detection based on DAS data collected under
controlled conditions.
