The wearing courses (top layers) of the streets in towns are usually of asphalt concrete, paving stones or cement concrete blocks type. They must provide good load capacity, high friction, comfortable and quiet ride. The problem of traffic noise reduction by use of proper road surfaces is not very often addressed in Poland. Due to legislation and technical progress during the last decades, a substantial reduction of the power unit noise emitted by cars and trucks has been achieved. It is observed that nowadays even at low speeds typical for urban driving the dominating noise comes from the tyres. The paper compares typical road surfaces that are used in towns from the point of view of their influence on the traffic noise.

The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and unevenness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V 4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.