Modelling road traffic noise and human exposure: from simulation to application
Time: Fri 2025-09-19 09.30
Location: F3, Lindstedtvägen 26
Language: English
Subject area: Vehicle and Maritime Engineering
Doctoral student: Sacha Baclet , Fordonsteknik och akustik
Opponent: Associate Professor Claudio Guarnaccia, University of Salerno
Supervisor: Associate professor Romain Rumpler, Fordonsteknik och akustik; Peter Göransson, Fordonsteknik och akustik
QC 2025-08-27
Abstract
Noise pollution is a leading environmental risk, causing widespread annoyance, sleep disturbance and an increased burden of disease. In Europe alone, more than 100 million people are affected by noise pollution, especially in urban areas. Road traffic is, by far, the largest contributor to this urban noise exposure. Despite extensive regulatory efforts, including the EU's Environmental Noise Directive, current approaches to environmental noise assessment remain limited in their capacity to capture the true complexity of road traffic noise pollution and its impacts on exposed populations. In particular, prevailing methodologies often rely on static assessments, using time-averaged indicators which ignore the highly dynamic nature of road traffic noise exposure in cities, and adopt a source-centric approach, focussing more on where the noise is emitted rather than who is exposed to it.
The present thesis aims to address these limitations by advancing the methodological chain for road traffic noise exposure assessment. It introduces and validates a suite of new tools and approaches for near-real-time, dynamic, and population-centric noise modelling, based on both advanced simulation and empirical data. Central to this work is the use of microscopic traffic simulations, calibrated using real-time sensor data, to generate temporally and spatially resolved representations of urban traffic. These simulations form the basis for more realistic modelling of noise emissions, enabling the assessment of exposure at both individual and population scales.
A significant contribution of this thesis is the improvement of noise emission models to explicitly account for the unique characteristics of electric vehicles during acceleration. These advancements address a key limitation in existing models, which tend to underestimate the noise emissions of electric cars and fail to properly reflect the shifting acoustic landscape resulting from vehicle electrification. The thesis also proposes and validates dynamic and event-based noise indicators, such as the number of noise events, which have demonstrated strong associations with health-relevant outcomes like sleep disturbance. These indicators are complemented by innovative visualisation techniques, including animated dynamic noise maps and receiver-centric sensitivity maps, that place the population at the centre of exposure assessment and facilitate more effective communication with stakeholders.
The proposed methodologies have been validated through case studies in several European cities, including Tartu (Estonia), Munich (Germany), and Stockholm (Sweden), demonstrating their applicability to real scenarios. Furthermore, the integration of dynamic simulations with real-world measurements and the demonstration of near-real-time noise mapping lay the groundwork for next-generation digital twins and dynamic noise management strategies in urban environments.
In conclusion, this thesis advances the field of environmental acoustics by delivering a comprehensive and flexible framework for dynamic noise exposure assessment, supporting healthier, fairer, and more sustainable cities.