AUTHORS: Johannes Masino, Benjamin Wohnhas, Michael Frey, Frank Gauterin

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ABSTRACT: Traffic noise has large consequences on the appreciation of the living quality close to roads and is considered as a health problem today. It leads to speech interference, sleep disturbances, and general annoyance. The major contributor to traffic noise is tire/road noise. Many studies show the influence of different road types or vehicles on tire/road noise or on the noise inside the vehicle. This study focuses on the contribution of the overdrive of different road features on the tire/road noise for various velocities. Experimental measurements based on the ISO-13325 Coast-By Method are performed to determine the relative Sound Pressure Level of road features compared to a normal Asphalt road in good condition. The results show, that road features cause at least 4 dB higher Sound Pressure Level than the reference Asphalt road for the investigated velocities, except for manhole cover at 8:3 m=s. Therefore, road features and damages have a significant contribution to tire/road noise and on human health. To identify and predict such road features, we present a method based on vehicle sensors and data mining methods. The sensors are an inertial sensor placed at the centre of gravity of the vehicle and a sound pressure sensor in the tire cavity. The sensors combined with the data analysis method represent a strong system to comprehensively and automatically identify and predict road features, the road infrastructure condition and subsequently road segments with a high value in tire/road noise. With the output of the presented method maintenance and repairs can be done efficiently, which contributes to lower tire/road noise and less disturbance of residents.

KEYWORDS: Tire/Road Noise, Vehicle Vibration, Tire Vibration, Road Features, Data Mining, Vehicle Sensor, Experimental Study

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