  In many urban areas, traffic load and noise pollution are constantly
increasing. Automated systems for traffic monitoring are promising
countermeasures, which allow to systematically quantify and predict local
traffic flow in order to to support municipal traffic planning decisions. In
this paper, we present a novel open benchmark dataset, containing 2.5 hours of
stereo audio recordings of 4718 vehicle passing events captured with both
high-quality sE8 and medium-quality MEMS microphones. This dataset is well
suited to evaluate the use-case of deploying audio classification algorithms to
embedded sensor devices with restricted microphone quality and hardware
processing power. In addition, this paper provides a detailed review of recent
acoustic traffic monitoring (ATM) algorithms as well as the results of two
benchmark experiments on vehicle type classification and direction of movement
estimation using four state-of-the-art convolutional neural network
architectures.
