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Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system

  • Ahmed Abdulkareem Ahmed
  • Biswajeet PradhanEmail author
Article

Abstract

This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.

Keywords

Traffic noise Noise prediction Noise propagation Neural networks Remote sensing GIS LiDAR 

Notes

Acknowledgments

The authors acknowledge and appreciate the provision of airborne laser scanning data (LiDAR), satellite images and logistic support by the PLUS Berhad. Thanks to anonymous reviewers for their valuable feedback which helped us to improve the quality of the manuscript.

Funding information

In addition, the second author, Biswajeet Pradhan, gratefully acknowledges the financial support from the UPM-PLUS industry project grant. This research is supported by the UTS under grant numbers 321740.2232335 and 321740.2232357.

Supplementary material

10661_2019_7333_MOESM1_ESM.docx (2.6 mb)
ESM 1 (DOCX 2667 kb)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia

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