  A wide variety of sensor technologies are nowadays used 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 dedicated short-range communications (DSRC)
signals sent in a vehicular network and machine learning techniques. We verify
the feasibility of the proposed approach with extensive simulations and
real-world experiments at an intersection. We first 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
DSRC messages transmitted from a roadside unit (RSU). The results show that we
are able to separate different traffic intensities with an accuracy of 96.3\%
and 87.6\% on the simulation and experimental data, respectively. We also
estimate the number of vehicles on the road with a weighted mean absolute
percentage error (WMAPE) of 10.7\% and 19.7\% on simulation and experimental
data, respectively. The proposed approach is suitable to be deployed alongside
the current monitoring systems to improve the performance of the systems
without requiring additional investment in infrastructure.
