Abstract
This paper aims to summarize the findings of research concerning the application of neural networks in traffic noise prediction. Noise is an environmental agent, regarded as a stressful stimulus. Noise exposure causes changes at different levels in living beings, such as the cardiovascular, endocrine and nervous system. Study of traffic noise prediction models began in 1950s to predict a single vehicle sound pressure level at the road side. After that, several traffic noise prediction models such as FHWA, CORTN, STOP and GO, MITHRA, ASJ etc. were developed depending upon various parameters and conditions. Complexity of error identification by means of classical approaches has led to researchers and designers to explore the possibility of neural solution to the problem of traffic noise prediction. Present study is focused on review of various neural network models developed for road traffic noise prediction.
Highlights
Noise is one of the environmental pollutants encountered in daily life
Noise pollution has become a major concern of communities living in the vicinity of major highway corridors
Main areas of concern are related to air and noise pollution
Summary
Noise is one of the environmental pollutants encountered in daily life. Noise pollution has become a major concern of communities living in the vicinity of major highway corridors. In the view of rapid development, it is essential to study highway noise with respect to various causative factors. Main areas of concern are related to air and noise pollution. Noise levels are showing an alarming rise and the level exceeds the prescribed levels in most areas. Investigations in several countries in the past few decades have shown that exposure to noise, such as occurs when living in close proximity to busy roads or highways, has adverse effect on human health [2,3,4,5,6,7]
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