The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1181–1188, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1181-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1181–1188, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1181-2020

  14 Aug 2020

14 Aug 2020

DIFFERENT SCALES OF URBAN TRAFFIC NOISE PREDICTION

M. I. Zafar, S. Bharadwaj, R. Dubey, and S. Biswas M. I. Zafar et al.
  • Dept. of Chemical Engineering and Engineering Sciences, Rajiv Gandhi Institute of Petroleum Technology Jais, Amethi, Uttar Pradesh, India

Keywords: Noise, LiDAR, Total Station, Urban Traffic, Different Scales, Prediction

Abstract. Noise pollution is an important problem. Places around the road or railway corridor can get serious noise hazards in the outdoor environment. The problem of noise is dynamic and varies from one location to another. It becomes more challenging due to the varying nature of noise sources (e.g., bus, truck, tempo, etc.) that differ in frequency spectra of audible noises. It is required to characterize the noise environment for an area, which requires noise measurement and use it for noise prediction. An attempt has been made to predict the noise levels in the form of noise maps. Noise prediction requires information on terrain data, noise data (of sources) and a model to predict noise levels around the noise sources. With the variation in terrain data, noise data, and use of prediction model the performance of prediction can vary. Thus, the study is conducted at three different locations i.e., (i) Ratapur Road crossing, Rae Bareli (ii) Bahadurpur Road crossing, at Jais, and (iii) RGIPT Academic Block close to the railway track. The three studies indicated how the performance of prediction can vary with changes in the quality of terrain data, noise sampling, and schemes of noise modeling. Generally, with a better quality of terrain data (comprehensive and precise), better prediction can be possible. Similarly, more focused and event-specific noise recording, modeling can provide more detailed time-specific noise mapping, which is not possible otherwise with customary average noise recording technique. However, detailed and comprehensive modeling warrants complex and bigger data handling.