A study of the performance of a generalized exceedance algorithm for detecting noise events caused by road traffic

Brown, A.L.; De Coensel, Bert · 2018 · Crossref

DOI: 10.1016/j.apacoust.2018.03.031

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Summary

This study addresses the lack of a standardized method for detecting and counting noise events in road traffic, a critical gap in noise management that hinders the assessment of human responses such as annoyance and sleep disturbance. While conventional indicators like $L_{Aeq}$ measure energy exposure, they fail to capture discrete noise events—distinct sound peaks caused by individual vehicles or vehicle groups—that may drive adverse health effects. The authors aim to evaluate the performance of a generalized exceedance algorithm for event detection, determining which parameter sets yield valid and reliable counts across diverse traffic conditions. The research employs a rigorous modeling approach using the Noysim2 simulation tool, which couples microscopic traffic simulation (Aimsun) with a vehicle-specific noise emission model and ISO 9613 sound propagation. The study simulated one-hour noise time histories for an exhaustive set of 500 unique scenarios, varying speed limits (60 or 100 km/h), traffic demand (5 to 5000 vehicles/h), heavy vehicle percentages (0% to 100%), and receiver distances (7.5 to 120 m). Each scenario was repeated 30 times to account for stochastic variability, resulting in 15,000 simulation runs. The generalized detection algorithm identifies events when instantaneous sound levels exceed a threshold defined by a background level plus an emergence value, incorporating additional criteria for minimum event duration and minimum time gaps between events. The study focused on unshielded locations and analyzed the macrostructure metric of the total number of detected noise events. The results demonstrate that the reliability and validity of noise event counts are highly sensitive to the algorithm’s parameters. By testing a wide range of parameter combinations, the authors identified that many configurations produce invalid or unreliable counts, particularly in scenarios with high traffic flow where short vehicle headways complicate detection. Through the elimination of non-viable parameter sets and the examination of redundancy among remaining ones, the study narrowed the field to a small number of representative parameter sets. These selected sets provide consistent and robust event counts across the simulated population of acoustic conditions, effectively distinguishing discrete noise events from the continuous traffic noise background. The significance of this work lies in establishing a rigorous foundation for event-based noise indicators. By identifying reliable parameter sets for the generalized exceedance algorithm, the study provides a practical tool for measuring noise events in road traffic. This advancement supports the development of supplementary noise indicators that account for the microstructure of noise signals, potentially improving the prediction of human health responses and informing more effective noise management policies beyond traditional energy-equivalent measures.

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