What question did this study set out to answer?

The aim is to develop a machine learning-based framework for predicting traffic noise in a campus setting, considering complex spatial dynamics.

April 12, 2026Applied IntelligenceOpen Access

Machine learning-based 2D and 3D traffic noise modelling in campus environments: a framework for vertical façade exposure assessment

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Authors

KAKhaled Yousef AlmansiUniversity of Technology Malaysia UUU. UjangUniversity of Technology Malaysia SASuhaibah AzriUniversity of Technology Malaysia

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Implication

Demonstrates a machine learning framework predicting traffic noise exposure in campus settings, suggesting effective planning tools for urban environments.

Key Points

The aim is to develop a machine learning-based framework for predicting traffic noise in a campus setting, considering complex spatial dynamics.
Utilized multilayer perceptron (MLP) and extreme gradient boosting (XGB) for noise prediction.
Applied the traditional CoRTN model for comparison and validation.
Analyzed data based on traffic, topographic, and land use factors.
Employed SHAP and global sensitivity analysis for model interpretation.
The XGB model achieved a high performance with R² = 0.95, MAE = 0.91, MSE = 2.75.
Identified road proximity as the main predictor of noise exposure.
High-resolution 2D maps reflected spatial patterns consistent with observed data.
In 3D modeling, façade-level noise exposure decreased from 55% at ground level to under 8% by the fourth floor.

Cite This Study

Almansi et al. (2026) studied this question.

synapsesocial.com/papers/69db36e64fe01fead37c4d0f https://doi.org/10.1007/s10489-026-07221-1

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Implication

Demonstrates a machine learning framework predicting traffic noise exposure in campus settings, suggesting effective planning tools for urban environments.

Machine learning-based 2D and 3D traffic noise modelling in campus environments: a framework for vertical façade exposure assessment

Discussion

Member takes

Implication

Key Points

Cite This Study

Machine learning-based 2D and 3D traffic noise modelling in campus environments: a framework for vertical façade exposure assessment

Discussion

Member takes

Implication

Key Points

Cite This Study

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