Accounting for traffic dynamics improves noise assessment: Experimental evidence

Can, Arnaud; Leclercq, Ludovic; Lelong, Joël; Defrance, Jérôme · 2009 · Crossref

DOI: 10.1016/j.apacoust.2008.09.020

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Summary

This study addresses the limitations of static traffic representations in urban noise assessment, motivated by the need for precise models to evaluate traffic management policies such as speed reduction and signal coordination. While classical static models assume smooth, homogeneous traffic flow, they fail to capture the dynamic variations caused by traffic signals, leading to inaccuracies in urban environments. The paper compares three methodologies: (i) a coarse static calculation based on mean speeds and flow rates, (ii) a refined static calculation incorporating mean kinematics patterns for stopped vehicles, and (iii) a dynamic noise estimation model that simulates individual vehicle propagation and interactions on the network. The experimental design involved simultaneous traffic and acoustic measurements on a major urban arterial in Lyon, France, over a two-hour period. Data were collected at five specific points representing typical urban scenarios, including locations near traffic signals and bus stations. The noise estimation process utilized fixed emission laws for light vehicles and buses, coupled with the NMPB-Routes-96 propagation model. The dynamic model, SYMUBRUIT, predicted vehicle positions, speeds, and accelerations at one-second intervals, accounting for queue formation, discharge, and bus movements. The study evaluated the models' ability to estimate both classical energetic descriptors (LAeq) and specific dynamic descriptors, such as LAeq,1s distributions and mean noise patterns. Results demonstrated that the coarse static model was insufficient, overestimating LAeq by a mean of 3.1 dB(A) because it ignored vehicle deceleration and stopping. The refined static model improved accuracy, reducing the mean error to 2.2 dB(A), but still failed to capture queue dynamics and specific noise fluctuations. In contrast, the dynamic model achieved high precision, with LAeq errors below 2 dB(A) across all points and under 1 dB(A) near traffic signals. Furthermore, the dynamic model accurately reproduced specific descriptors, including the bimodal LAeq,1s distributions corresponding to green and red signal phases, and the mean noise pattern. Crucially, the study found that the dynamic model remained consistent and accurate even when fed with aggregated data averaged over two-hour periods, rather than detailed per-cycle data, validating its practical applicability. The significance of this work lies in demonstrating that accounting for traffic dynamics is essential for precise urban noise assessment, particularly for capturing noise variability linked to traffic signals. The findings suggest that dynamic models are superior for evaluating traffic management strategies and estimating specific noise descriptors that better reflect human annoyance. Moreover, the robustness of the dynamic model with aggregated input data implies that high-precision noise assessment is feasible without requiring exhaustive, real-time traffic data collection, facilitating broader implementation in urban planning and noise mitigation projects.

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• traffic density