Accounting for traffic dynamics improves noise assessment: Experimental evidence

This paper compares three traffic representations for urban traffic noise assessment: (i) a coarse static calculation based on mean speeds and flow rates, (ii) a refined static calculation based on mean kinematics patterns, (iii) a whole dynamic noise estimation model that considers vehicle propagation on the network. The three methodologies are applied on real traffic situations and compared to on-field noise levels. Representation (i) is not refined enough to guarantee a precise noise assessment. Representation (ii) can be sufficient for L Aeq estimation in most of cases. However, representation (iii) improves noise estimation since it considers vehicle interactions on the network. Moreover, it allows for specific descriptors to be estimated with a great accuracy, like the L Aeq,1s distributions or the mean noise pattern that reproduces every traffic cycle. Finally, the dynamic noise estimation appears to be still consistent if the model is fed with data averaged on 2-h period.