Traffic Noise Modelling using Integrated Conformal Prediction Based Uncertainty Estimation with Machine Learning Algorithms

Surinder Deswal¹, Mahesh Pal², Prateek Bhardwaj³, Danesh Selwal⁴, Prakriti Bisht⁵

^1,2 Professor, Department of Civil Engineering, National Institute of Technology Kurukshetra, India.

³ Research Scholar, Department of Civil Engineering, National Institute of Technology Kurukshetra, India.

^4,5 BTech Students, Department of Civil Engineering, National Institute of Technology Kurukshetra, India.   

Abstract

Traffic noise models (TNMs), whether analytical or based on machine learning (ML), face inherent uncertainties due to variability in input parameters. Accurately estimating and integrating these uncertainties is essential for enhancing urban traffic noise predictions and supporting effective policy-making and noise mitigation strategies. This study introduces an innovative approach that utilises integrated conformal prediction (CP) based uncertainty estimation combined with ML algorithms for urban traffic noise modelling, allowing for user-defined probability assessment. Three CP-based approaches — splitCP, CV+, and conformal quantile regression (CQR) —were integrated with two ML algorithms: Probabilistic Gradient Boosting Machines (PGBM) and Categorical Boosting (CatBoost). This integration aimed to create a statistically and probabilistically robust model for predicting urban traffic noise levels. The study utilised a dataset comprising 228 field measurements of equivalent noise levels (L_eq) in relation to corresponding hourly traffic flow (Q) of different types/categories of vehicles. To optimise the user-defined parameters essential for both ML algorithms, the AutoSampler package from Optuna, a Python-based framework for hyperparameter optimisation, was used. The performance of the CP-based ML models was evaluated using –  mean predicted interval width and effective coverage criteria for conformal prediction; and sharpness, CRPS and NLL for probabilistic prediction. The results suggest the potential of both the ML algorithms with the CV+ based CP approach due to a superior balance between a smaller mean interval width and higher effective coverage. However, considering the outcome of probabilistic prediction criteria in conjunction with the CP estimates, the PGBM with CV+ based CP approach offers a better option for uncertainty estimates, as confidence intervals or risk quantification matters more in TNMs for policy strategies and decisions. Furthermore, the study outlines practical applications that can equip policymakers with accurate and reliable predictions of urban traffic noise levels and facilitate them in effective urban noise mitigation planning.     

Keywords: Traffic noise model (TNM), Machine learning (ML), Uncertainty, Conformal prediction (CP), Probabilistic prediction, CatBoost, PGBM  

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