Using recorded sound spectra profile as input data for real-time short-term urban road-traffic-flow estimation
Using recorded sound spectra profile as input data for real-time short-term urban road-traffic-flow estimation
Road traffic has a heavy impact on the urban sound environment, constituting the main source of noise and widely dominating its spectral composition. In this context, our research investigates the use of recorded sound spectra as input data for the development of real-time short-term road traffic flow estimation models. For this, a series of models based on the use of Multilayer Perceptron Neural Networks, multiple linear regression, and the Fisher linear discriminant were implemented to estimate road traffic flow as well as to classify it according to the composition of heavy vehicles and motorcycles/mopeds. In view of the results, the use of the 50–400 Hz and 1–2.5 kHz frequency ranges as input variables in multilayer perceptron-based models successfully estimated urban road traffic flow with an average percentage of explained variance equal to 86%, while the classification of the urban road traffic flow gave an average success rate of 96.1%.
road traffic, sound spectra, estimation models, multilayer perceptron neural networks, fisher linear discriminant, multiple linear regression
270-279
Torija, Antonio J.
6dd0d982-fcd6-42b6-9148-211175fd3287
Ruiz, Diego P.
ab9eb00f-171c-417f-8304-5105e41cbd03
1 October 2012
Torija, Antonio J.
6dd0d982-fcd6-42b6-9148-211175fd3287
Ruiz, Diego P.
ab9eb00f-171c-417f-8304-5105e41cbd03
Torija, Antonio J. and Ruiz, Diego P.
(2012)
Using recorded sound spectra profile as input data for real-time short-term urban road-traffic-flow estimation.
Science of the Total Environment, 435-436, .
(doi:10.1016/j.scitotenv.2012.07.014).
Abstract
Road traffic has a heavy impact on the urban sound environment, constituting the main source of noise and widely dominating its spectral composition. In this context, our research investigates the use of recorded sound spectra as input data for the development of real-time short-term road traffic flow estimation models. For this, a series of models based on the use of Multilayer Perceptron Neural Networks, multiple linear regression, and the Fisher linear discriminant were implemented to estimate road traffic flow as well as to classify it according to the composition of heavy vehicles and motorcycles/mopeds. In view of the results, the use of the 50–400 Hz and 1–2.5 kHz frequency ranges as input variables in multilayer perceptron-based models successfully estimated urban road traffic flow with an average percentage of explained variance equal to 86%, while the classification of the urban road traffic flow gave an average success rate of 96.1%.
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Accepted/In Press date: 2 July 2012
e-pub ahead of print date: 1 August 2012
Published date: 1 October 2012
Keywords:
road traffic, sound spectra, estimation models, multilayer perceptron neural networks, fisher linear discriminant, multiple linear regression
Organisations:
Acoustics Group
Identifiers
Local EPrints ID: 386671
URI: http://eprints.soton.ac.uk/id/eprint/386671
ISSN: 0048-9697
PURE UUID: 4a4c04a3-e138-41d3-8395-ec2106ba1a12
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Date deposited: 03 Feb 2016 11:19
Last modified: 14 Mar 2024 22:36
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Author:
Antonio J. Torija
Author:
Diego P. Ruiz
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