Noise Pollution Analysis Using Geographic Information System, Agglomerative Hierarchical Clustering and Principal Component Analysis in Urban Sustainability (Case Study: Tehran)

In this study, a new approach has been used with SPSS and MATLAB analysis to study urban road traffic noise distribution mapping, to obtain the representative road traffic noise maps. The observation has been performed at a high traffic highway. The factors influencing noise level (traffic, road width, slope, and residential or administrative-commercial land) use were surveyed and recorded for each point and their local and time dependencies were computed. According to the analysis, the maximum value of goodness of fit index for the traffic and noise level relationship was 0.64, followed by 0.489 for the percentage of residential land use. The result of this study showed that the vehicle speed, width of the road, and the land use can affect different sound levels emitted by moving vehicles on road. The model predicts that by increasing one vehicle per hour, an increase in noise level by 0.002 dB will happen.

Automated summary

A new approach using SPSS and MATLAB analysis was used to study urban road traffic noise distribution mapping in this study, in order to obtain representative noise maps. The study was conducted on a high traffic highway, where factors such as traffic volume, road width, slope, and land use were surveyed and recorded for each point, and their local and time dependencies were calculated. The analysis showed that the relationship between traffic volume and noise level had a maximum goodness of fit index of 0.64, followed by 0.489 for the percentage of residential land use. The study found that vehicle speed, road width, and land use can impact the sound levels emitted by moving vehicles on the road, with the model predicting a 0.002 dB increase in noise level for every additional vehicle per hour.