A machine learning approach for traffic-noise annoyance assessment

Volume: 156, Pages: 262 - 270
Published: Dec 1, 2019
Abstract
In this study, models for predicting traffic-noise annoyance based on noise perception, noise exposure levels, and demographics were developed. By applying machine-learning techniques, in particular artificial neural networks (ANN), support vector machines (SVM) and multiple linear regressions (MLR), the traffic-noise annoyance models were obtained, and the error rates compared. A traffic noise map and the estimation of noise exposure for the...
Paper Details
Title
A machine learning approach for traffic-noise annoyance assessment
Published Date
Dec 1, 2019
Volume
156
Pages
262 - 270
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