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- W3131336666 abstract "The objective of this study is to identify the contributing factors related to noise levels. Noise and traffic data were collected at three locations in the city of Surat, India. Equivalent Continuous A-weighted noise level (dB) and classified traffic volume count were collected for a total of sixteen hours on mid-block sections (comprising of accelerating, cruising, and braking zone). Noise and traffic volume data were aggregated at 5mins, 15mins, 30mins, and 1-hour intervals. Also, predictor variables such as vehicle composition, average speed, road characteristics, landuse along the road, and the presence of dedicated BRTS lane were considered for this research study. A total of twelve multilinear regression (MLR) and twelve artificial neural network (ANN) models were developed, validated, and compared. Models developed for prediction of noise level aggregated at 5 minutes and 1-hour were observed to be the best-fitted model for MLR model and ANN model, respectively. The results indicate that noise mitigation measures should be implemented on roads with higher traffic volume, percentage of two-wheelers, and roads with BRTS lane." @default.
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- W3131336666 date "2021-01-05" @default.
- W3131336666 modified "2023-09-27" @default.
- W3131336666 title "Identifying Causal Factors Contributing to Noise Levels on Urban Arterials" @default.
- W3131336666 cites W2010973889 @default.
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- W3131336666 doi "https://doi.org/10.1109/comsnets51098.2021.9352895" @default.
- W3131336666 hasPublicationYear "2021" @default.
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