Evaluation and ANN modelling of traffic noise on BRTS corridors with different median vegetation: a case study of Surat city (western India)
By: Someya, K
.
Contributor(s): Tandel, B. N.
Publisher: USA Springer 2022Edition: Vol.103(4), Dec.Description: 1325-1339p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series ASummary: The study focuses on traffic noise pollution propagation concerning vegetation. The study area chosen was Surat city (Gujarat). With the increasing population, the congestion in the bustling city has led to the increased importance of public transport (BRTS). Three BRTS corridors of Surat city have been selected for the study. The chosen corridors were such that there was dense, medium and no median vegetation on the three different BRTS corridors. Noise levels, as well as traffic surveys, were done on three sites during the daytime. The noise levels were found to be higher than acceptable limits given by CPCB. Noise levels were fluctuating irrespective of the simultaneous traffic count. There is an observed abatement of noise levels due to vegetation on the median of the road. A critical advantage of a vegetative barrier on the median of the road is not only based on the actual importance of noise reduction (in decibels) but may also be psychological and aesthetic purposes. Study findings show that there is significantly less noise [4.1 dB (A) less] in BRTS corridors with vegetation as compared to corridor with no vegetation. However, no significant variation in noise reduction can be seen in BRTS corridor with dense vegetation as compared to medium vegetation. For development of noise model using artificial neural network the traffic volume, vegetation dimension and distance from median vegetation were used as the input data and one output of noise level (Leq) was considered. The model's performance was tested using the coefficient of correlation (R) and mean squared error. It was observed that there was less variation in the observed and predicted noise levels, with R values ranging from 0.854 to 0.955.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
|
School of Engineering & Technology Archieval Section | Not for loan | 2023-0365 |
The study focuses on traffic noise pollution propagation concerning vegetation. The study area chosen was Surat city (Gujarat). With the increasing population, the congestion in the bustling city has led to the increased importance of public transport (BRTS). Three BRTS corridors of Surat city have been selected for the study. The chosen corridors were such that there was dense, medium and no median vegetation on the three different BRTS corridors. Noise levels, as well as traffic surveys, were done on three sites during the daytime. The noise levels were found to be higher than acceptable limits given by CPCB. Noise levels were fluctuating irrespective of the simultaneous traffic count. There is an observed abatement of noise levels due to vegetation on the median of the road. A critical advantage of a vegetative barrier on the median of the road is not only based on the actual importance of noise reduction (in decibels) but may also be psychological and aesthetic purposes. Study findings show that there is significantly less noise [4.1 dB (A) less] in BRTS corridors with vegetation as compared to corridor with no vegetation. However, no significant variation in noise reduction can be seen in BRTS corridor with dense vegetation as compared to medium vegetation. For development of noise model using artificial neural network the traffic volume, vegetation dimension and distance from median vegetation were used as the input data and one output of noise level (Leq) was considered. The model's performance was tested using the coefficient of correlation (R) and mean squared error. It was observed that there was less variation in the observed and predicted noise levels, with R values ranging from 0.854 to 0.955.
Click on an image to view it in the image viewer
There are no comments for this item.