Abstract
Techniques for remote sensing of noise are rapidly evolving, allowing for automatic and unsupervised monitoring of individual noise events. In the H2020 project NEMO, noise remote sensing was developed and applied to detect individual loud vehicles in normal road or rail traffic. Potential applications include urban vehicle access restrictions (UVAR) such as in low emission zones, based on real world emissions rather than type approval data. The city of Amsterdam is also experimenting with influencing driving behaviour and raising road user awareness to reduce the number of high noise emitters. Amsterdam uses dynamic roadside signalling for this, which shows a message "too loud" to noise vehicles passing by. New tests are now performed with a more advanced acoustic camera to localize and track the individual vehicle, connected to license plate and speed detectors. This paper describes how NEMO results are used to achieve an actual reduction of high emitters in Amsterdam. First results of experiments are presented along with technical challenges that need to be overcome. These challenges include reduction of measurement uncertainty as well as uncertainty as to what vehicle a high noise event belongs to.
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