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The use of artificial intelligence to predict road traffic noise

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Doolan, BL (2008) The use of artificial intelligence to predict road traffic noise. Research Master thesis, University of Tasmania.

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Abstract

This research has been motivated by the fact that present road traffic noise prediction models
have not improved significantly since their development in the 1970s and 1980s, although
road traffic noise nuisance is a significant and growing issue in Australia and elsewhere.
This thesis reviews the nature of road traffic noise, its measurement, and interpretation of
noise levels in terms of noise nuisance. It then examines the principal noise propagation
influences that are described by road traffic noise prediction models such as ST AM SON and
TNOISE, and outlines how these quasi-empirical models produce noise level predictions.
Present road traffic noise prediction models are essentially pattern recognition tools, but
while they perform satisfactorily for very simple situations, accurate noise prediction in more
complex situations is beyond their ability. However, artificial intelligence pattern recognition
tools have proven their power and usefulness in a variety of applications in recent years, and
this thesis examines the hypothesis that a neural network approach to predicting road traffic
noise offers a way to move forward in noise impact assessment.
A simple two-layer feed-forward neural network architecture is found to be able to easily
mimic present road traffic noise prediction models, with tangent-sigmoidal transfer functions
specified for the input layer of 20-30 neurons, and a linear transfer function specified for the
single output neuron. A priori rescaling of input values to roughly match the requirements of
the transfer function facilitates the neural network training using a backpropagation
algorithm with momentum and adaptive learning. Ways of avoiding the problem of
overfitting are discussed.
A case study based on a 1993 noise impact assessment project is presented that demonstrates
that a neural network can easily be trained from fairly limited field data to satisfactorily
predict road traffic noise in site-specific situations, and the case study was one in which a
model such as STAMSON or TNOISE is not able to perform well. The effort and expertise
needed for this exercise is comparable to an air emission dispersion modelling exercise, a
conclusion that should prove of great interest to road and environment authorities.
The thesis then proposes a strategy whereby grid-based neural networks can be developed to
enable road traffic noise prediction in complex situations. The methodology is explained
with the aid of a barrier adjustment calculation. The development of such a model for a sitespecific
situation is quite straightforward, but there is also clear potential to develop a
generic 2-dimension modelling capability. The basic approach to this parallels the modelling
strategy of present noise prediction models, but with reference sound levels and adjustments
referred to a grid, and determined using neural networks.

Item Type: Thesis (Research Master)
Additional Information:

Copyright 2008 the Author-The University is continuing to endeavour to trace the copyright owner(s) and in the meantime this item has been reproduced here in good faith. We would be pleased to hear from the copyright owner(s)

Date Deposited: 20 Aug 2014 02:33
Last Modified: 15 Sep 2017 01:06
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