Abstract
Abstract Noise became one of the main environmental indicators of the quality of urban life. The aim of this study was to develop a traffic noise model for an arterial road in Amman, the capital of Jordan, which was being subjected to a persistent increase in traffic and its related issues. The characteristics of the traffic and its relevant noise were analysed to develop a noise prediction model using an adaptive neuro-fuzzy inference system (ANFIS). A geographic information system (GIS) was then implemented to model the current noise along the arterial road. The results of ANFIS model showed that traffic delays and the percentage of heavy vehicles were the main causes of traffic noise generation within the urban area. However, the developed ANFIS model can simulate traffic noise with a relatively low root mean square error. Furthermore, this study will help to improve the perception about traffic noise and its development along arterial roads.
Highlights
Traffic congestion has become a serious "urban illness", giving rise to problems such as travel time delays, increase in traffic accidents as well as traffic noise
The results of adaptive neuro-fuzzy inference system (ANFIS) model showed that traffic delays and the percentage of heavy vehicles were the main causes of traffic noise generation within the urban area
The study analysed some negative implications of traffic on the noise level along an arterial road Queen Rania Al-Abdullah (QRA) Street in Amman city, Jordan
Summary
Traffic congestion has become a serious "urban illness", giving rise to problems such as travel time delays, increase in traffic accidents as well as traffic noise. Traffic noise is a major threat to human health, especially in urban areas and/or where there is high traffic volume and speed [1]. It affects most of the community such as homes, places of worship, patients in hospitals and students at schools. As developing countries have chronic problems related to data availability and accuracy, a noise model will help them to save on time and resources as well as minimise costs
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