  Intelligent Transportation Systems (ITSs) providing vehicle-related
statistical data are one of the key components for future smart cities. In this
context, knowledge about the current traffic flow is used for travel time
reduction and proactive jam avoidance by intelligent traffic control
mechanisms. In addition, the monitoring and classification of vehicles can be
used in the field of smart parking systems. The required data is measured using
networks with a wide range of sensors. Nevertheless, in the context of smart
cities no existing solution for traffic flow detection and vehicle
classification is able to guarantee high classification accuracy, low
deployment and maintenance costs, low power consumption and a
weather-independent operation while respecting privacy. In this paper, we
propose a radiobased approach for traffic flow detection and vehicle
classification using signal attenuation measurements and machine learning
algorithms. The results of comprehensive measurements in the field prove its
high classification success rate of about 99%.
