Lin, Min-Bin (Karlsruhe Institute of Technology), Lazarova-Molnar, Sanja (Karlsruhe Institute of Technology), Vinel, Alexey (Karlsruhe Institute of Technology)

Review on Road Traffic Noise Modeling: Embarking on a Machine Learning Odyssey

Scheduled for presentation during the Regular Session "Traffic prediction and estimation IV" (ThBT7), Thursday, September 26, 2024, 15:50−16:10, Salon 15

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Road traffic noise (RTN) is a substantial environmental pollutant, implicated in various detrimental effects on public health. Regulatory initiatives have emerged to address RTN, aiming to integrate noise mitigation measures into vehicle design and road infrastructure. A precise and robust noise estimation is significant, serving as basis for effective and reliable assessment towards different environmental scenarios. This paper reviews recent research findings on road traffic noise modeling (RTNM), encompassing common linear regression approaches, emerging machine learning (ML) applications, as well as basic concepts for RTN. It specifically highlights the statistical challenges and modeling considerations such as uncertainties and multivariate analysis.