Selecting Noise Source and Traffic Representations that Capture Road Traffic Noise Dynamics Near Traffic Signals

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

DOI: 10.3813/aaa.918148

archive: archived pipeline: cataloged verified

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Summary

This study addresses the challenge of accurately estimating road traffic noise dynamics near traffic signals in urban environments. While previous research focused on classical noise descriptors like $L_{Aeq}$, this paper extends the analysis to specific descriptors that capture noise variations at the scale of individual traffic signal cycles. The motivation is to improve the assessment of urban noise quality by accounting for traffic dynamics, which significantly influence noise levels. The authors aim to determine the most relevant representations for noise sources and traffic models that ensure accurate estimation of these dynamic descriptors across various receiver locations and distances from the road. The methodology involves coupling dynamic traffic models with noise emission laws and sound propagation calculations. Three traffic representations were tested: a macroscopic conservation law model (MCL), a macroscopic car-following model (MCF), and a microscopic car-following model (mCF). For noise source representation, the study compared the reference vehicle line source (VLS) against aggregated fixed line sources of varying lengths (7m, 14m, and 28m). Experiments were conducted on a simulated 700m one-lane road section with a traffic signal, testing receivers located upstream, in front of, and downstream of the signal at distances of 5.5m, 10m, and 15m. Both saturated and unsaturated traffic conditions were evaluated to assess the impact of queue formation and discharge on noise patterns. The results indicate that the choice of noise source representation significantly affects accuracy depending on the receiver's distance from the road. At 15m from the road, even 28m line sources provided estimates within 1 dB(A) of the reference for most descriptors. However, at closer distances (5.5m and 10m), larger line sources introduced significant errors, particularly for statistical descriptors and high-level noise peaks. Specifically, 14m line sources ensured estimation errors below 2 dB(A) for all descriptors if traffic dynamics were precisely described, while 7m line sources were required to maintain errors below 1 dB(A) for high-level descriptors at 5.5m. Regarding traffic models, the macroscopic conservation law model (MCL) proved insufficient for estimating statistical descriptors close to the road, with deviations exceeding 3.5 dB(A) in some cases. In contrast, both macroscopic and microscopic car-following models effectively highlighted noise dynamics triggered by traffic signals, though microscopic models captured heterogeneity and stochastic effects more explicitly. The significance of this work lies in establishing precise modeling conditions for evaluating urban traffic management policies. By identifying that 14m line sources are sufficient for capturing noise dynamics with acceptable error margins, the study offers a balance between computational efficiency and accuracy. Furthermore, the finding that macroscopic car-following models are relevant for highlighting noise dynamics suggests that complex microscopic simulations may not always be necessary for noise assessment, provided the traffic dynamics are adequately represented. This contributes to more efficient and accurate tools for assessing the acoustic impact of urban traffic interventions.

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• traffic density