A study on vehicle noise prediction model at lower speed

The traffic noise assessment is an important part of an Environmental Assessment (EA) for highway infrastructure projects; however, its accuracy highly depends on the precision of traffic noise prediction models. In this study, vehicle noise emission values are expressed in terms of a power unit component and a rolling noise component. First, laboratory experiments are conducted to collect the vehicle speed and its corresponding power unit noise emission data. Then the linear model, logarithmic model, and quadratic curve model are developed to find the regression fitting. The results show that the quadratic curve model has the best fitting degree, so it is used in the vehicle noise prediction model. Second, the rolling noise model proposed by Hamet and Jean - Francois is introduced to develop the rolling noise emission using the method of energy superposition. A case study shows that the proposed revised model can significantly improve the accuracy of low phase noise prediction. In general, the proposed model obtains much better prediction results than that obtained from the highway construction project environmental impact assessment guideline, with the average precision increased by more than 20%.