  A traffic monitoring system (TMS) is an integral part of Intelligent
Transportation Systems (ITS). It is an essential tool for traffic analysis and
planning. One of the biggest challenges is, however, the high cost especially
in covering the huge rural road network. In this paper, we propose to address
the problem by developing a novel TMS called DeepWiTraffic. DeepWiTraffic is a
low-cost, portable, and non-intrusive solution that is built only with two WiFi
transceivers. It exploits the unique WiFi Channel State Information (CSI) of
passing vehicles to perform detection and classification of vehicles. Spatial
and temporal correlations of CSI amplitude and phase data are identified and
analyzed using a machine learning technique to classify vehicles into five
different types: motorcycles, passenger vehicles, SUVs, pickup trucks, and
large trucks. A large amount of CSI data and ground-truth video data are
collected over a month period from a real-world two-lane rural roadway to
validate the effectiveness of DeepWiTraffic. The results validate that
DeepWiTraffic is an effective TMS with the average detection accuracy of 99.4%
and the average classification accuracy of 91.1% in comparison with
state-of-the-art non-intrusive TMSs.
