Dynamic estimation of urban traffic noise: Influence of traffic and noise source representations

Can, Arnaud; Leclercq, Ludovic; Lelong, Joël · 2008 · Crossref

DOI: 10.1016/j.apacoust.2007.05.014

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Summary

This study addresses the need for dynamic traffic noise prediction models that account for the temporal variations of urban traffic, rather than relying on static, steady-state assumptions. The authors investigate how different representations of traffic flow and noise sources influence the estimation of acoustic descriptors, specifically equivalent continuous noise levels ($L_{Aeq}$) and statistical levels ($L_5, L_{10}, L_{50}, L_{90}$). The research aims to identify a modeling approach that accurately captures noise dynamics while remaining computationally efficient. The methodology involves coupling three distinct traffic models with four noise source representations. The traffic models include a first-order Macroscopic Conservation Law (MCL) model, a Macroscopic Car-Following (MCF) model, and a Microscopic Car-Following (mCF) model. These are tested against four urban traffic scenarios on a 700-meter single-lane road, varying flow rates (900 and 1440 veh/h) and the presence of traffic signals. Noise emissions are calculated using standard laws for rolling and propulsion noise, with propagation simplified to geometric attenuation. The noise source representations tested include individual vehicle line sources, individual vehicle point sources, and aggregated grid-based line or point sources. The results demonstrate that the choice of traffic behavior rule has a minimal impact on noise estimation. While the MCL model smoothed out noise dynamics in free-flow scenarios compared to the MCF model, both macroscopic models produced $L_{Aeq}$ estimates nearly identical to the more complex mCF model. Differences in statistical levels between macroscopic and microscopic behavior rules were generally under 1 dB(A). The study found that an individualized vehicle representation combined with a macroscopic behavior rule (MCF) is sufficient for accurate noise descriptor estimation. Regarding noise source aggregation, grid-based representations were significantly more computationally efficient than individual vehicle tracking. Specifically, a grid of line sources was found to be more relevant than a grid of point sources, and using larger cells did not substantially compromise the accuracy of the noise descriptors. The significance of this work lies in establishing that high-fidelity microscopic traffic simulations are not necessary for accurate urban noise prediction. Instead, a hybrid approach using macroscopic traffic rules with individualized vehicle tracking and aggregated line-source noise representations offers an optimal balance between accuracy and computational cost. This finding supports the development of efficient dynamic noise models for urban planning and environmental assessment, where capturing traffic dynamics is crucial for evaluating soundscapes.

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• traffic density