  A wide variety of sensor technologies are recently being adopted for traffic
monitoring applications. Since most of these technologies rely on wired
infrastructure, the installation and maintenance costs limit the deployment of
the traffic monitoring systems. In this paper, we introduce a traffic
monitoring approach that exploits physical layer samples in vehicular
communications processed by machine learning techniques. We verify the
feasibility of our approach with extensive simulations and real-world
experiments. First, we simulate wireless channels under realistic traffic
conditions using a ray-tracing simulator and a traffic simulator. Next, we
conduct experiments in a real-world environment and collect messages
transmitted from a roadside unit (RSU). The results show that we are able to
predict different levels of service with an accuracy above 80% both on the
simulation and experimental data. Further, the proposed approach is capable of
estimating the number of vehicles with a low mean absolute error on both data.
Our approach is suitable to be deployed alongside the current monitoring
systems. It doesn't require additional investment in infrastructure since it
relies on existing vehicular networks.
