4.5 Article

Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS

Journal

ENERGIES
Volume 14, Issue 16, Pages -

Publisher

MDPI
DOI: 10.3390/en14165095

Keywords

traffic noise modelling; land use regression model; machine learning; GIS; LiDAR

Categories

Funding

  1. Centre for Advanced Modelling & Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney
  2. King Saud University, Riyadh, Saudi Arabia [RSP-2021/14]
  3. UPM-PLUS industry project

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This study utilized machine learning and statistical regression methods, combined with geographical information systems, to estimate the equivalent continuous sound pressure level during peak daily periods along the New Klang Valley Expressway in Shah Alam, Malaysia. The results highlighted the superior performance of the machine learning (random forest) models compared to the statistical regression-based models.
This study estimates the equivalent continuous sound pressure level (L-eq) during peak daily periods ('rush hour') along the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia, using a land use regression (LUR) model based on machine learning, statistical regression, and geographical information systems (GIS). The research utilises two types of soft computing methods including machine learning (i.e., decision tree, random frost algorithms) and statistical regression (i.e., linear regression, support vector regression algorithms) to determine the best approach to create a prediction L-eq map at the NKVE in Shah Alam, Malaysia. The selection of the best algorithm is accomplished by considering correlation, correlation coefficient, mean-absolute-error, mean-square-error, root-mean-square-error, and mean absolute percentage error. Traffic noise level was monitored using three sound level meters (TES 52A), and a traffic tally was done to analyse the traffic flow. Wind speed was gauged using a wind speed meter. The study relied on a variety of noise predictors including wind speed, digital elevation model, land use type (specifically, if it was residential, industrial, or natural reserve), residential density, road type (expressway, primary, and secondary) and traffic noise average (L-eq). The above parameters were fed as inputs into the LUR model. Additional noise influencing factors such as traffic lights, intersections, road toll gates, gas stations, and public transportation infrastructures (bus stop and bus line) are also considered in this study. The models utilised parameters derived from LiDAR (Light Detection and Ranging) data, and various GIS (Geographical Information Systems) layers were extracted to produce the prediction maps. The results highlighted the superior performances by the machine learning (random forest) models compared to the statistical regression-based models.

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