@article {Fitzell:2019:0736-2935:557,
title = "Prediction of Statistical Noise Metrics for Road Traffic",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2019",
volume = "259",
number = "9",
publication date ="2019-09-30T00:00:00",
pages = "557-568",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2019/00000259/00000009/art00080",
author = "Fitzell, Robert",
abstract = "Objective: To improve current methods of prediction of road traffic noise impact levels. Methods: A method of predicting road traffic noise using A-weighted statistical levels has been developed, primarily focussed on modelling individual source position and noise emission characteristics.
The validity of the model has been verified by field studies involving concurrent vehicle flow and noise level surveying. Results: Results using the model have been predicted under a range of traffic flow conditions for observation positions nominally 15 metres from the nearest carriageway.
Statistical metrics and equivalent energy levels have been predicted for observation periods of 1 hour duration, with a very satisfactory level of accuracy better than 3dB(A) when compared against measurement observation. Conclusion: The method produces a more informed prediction describing
the potential impact on a community from a road project when compared with energy equivalent level predictions alone. Implication: The methodology could be utilised for assessment of any stochastic and/or physically mobile noise generating system, such as a railway, an open-cut mine, construction
site, industry or carpark. The method could be enhanced using narrow band prediction.",
}