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Modelling road traffic Noise under heterogeneous traffic conditions using the graph-theoretic approach.

Authors
  • Gilani, Towseef Ahmed1
  • Mir, Mohammad Shafi2
  • 1 Department of Civil Engineering, National Institute of Technology, Srinagar, J&K, 190006, India. [email protected] , (India)
  • 2 Transportation & Planning Section, Department of Civil Engineering, National Institute of Technology, Srinagar, J&K, 190006, India. , (India)
Type
Published Article
Journal
Environmental Science and Pollution Research
Publisher
Springer-Verlag
Publication Date
Mar 11, 2021
Identifiers
DOI: 10.1007/s11356-021-13328-4
PMID: 33704641
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

A traffic noise system involves several subsystems like road traffic subsystem, human subsystem, environment subsystem, traffic network subsystem, and urban prosperity subsystem. The study's main aim was to develop road traffic noise models using a graph theory approach involving the parameters related to road traffic subsystem. The road traffic subsystem variables selected for the modeling purposes included vehicular speed, traffic volume, carriageway width, number of heavy vehicles, and number of honking events. The interaction of the selected variables considered in the form of permanent noise function is given in the matrix form. Eigenvalues and corresponding eigenvectors are calculated for removing any human judgmental error. The permanent noise function matrix was then updated using the eigenvectors, which was ultimately utilized for obtaining the permanent noise index. Data regarding the selected variables were collected for three months, and the noise parameters included in the study were equivalent noise level (Leq,1h), maximum noise level (L10,1h), and background noise level (L90,1h). A logarithmic transformation was applied to the permanent noise index and linear regression models were developed for Leq,1h , L90,1h , and L10,1h respectively. The models were validated using the data collected from the same locations for nine months. The models were found to provide satisfactory results, although the results were somewhat overestimated. The method can prove beneficial for estimating future noise levels, given the expected changes in values for the independent variables considered in the study.

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