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Staab, J.* ; Weigand, M.* ; Schady, A.* ; Droin, A.* ; Cea, D. ; Dallavalle, M. ; Nikolaou, N. ; Valizadeh, M. ; Wolf, K. ; Wurm, M.* ; Lakes, T.* ; Taubenböck, H.*

National road traffic noise estimation with ensemble learning and multimodal geodata.

Transport. Res. Part D-Transport. Environ. 149:105063 (2025)
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Open Access Hybrid
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The European Noise Directive mandates the mapping of noise – high, continuous sound pressure levels considered to be a major health threat. However, the strictest rulesets apply to specific regions only and the majority of residential areas are unmapped. Transfer learning was deployed to close spatial data gaps between the official, strategic road traffic noise maps. The three most suitable hyperparameter configurations achieved weighted Kappa values (a measure of ordinal agreement) ranging between 0.889 and 0.956 during repeated cross-validation. The best model achieved an overall classification accuracy of 90.7 % when tested against held-out samples. 7.8 % of predictions exhibited minor deviations within ± 5 dB(A). The model was subsequently deployed to predict road traffic noise across Germany at 10 x 10 Meter resolution for 2017. The results suggest a total of 13.1 million people exposed to yearly averaged road traffic noise (Lden) above 55 dB(A) and stress need for improved noise policies.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Ensemble Learning ; Environmental Health ; Multimodal Geodata ; Noise Exposure ; Noise Pollution ; Random Forest ; Road Traffic Noise ; Urban Noise Mapping; Electric Vehicle Noise; Environmental Noise; Air-pollution; Hypertension; Prediction; Annoyance; Exposure; Associations; Propagation; Strategies
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 1361-9209
e-ISSN 1879-2340
Journal Transportation Research Part D: Transport and Environment
Quellenangaben Volume: 149, Issue: , Pages: , Article Number: 105063 Supplement: ,
Publisher Elsevier
Publishing Place The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, England
Reviewing status Peer reviewed
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of Epidemiology (EPI)
POF-Topic(s) 30205 - Bioengineering and Digital Health
30202 - Environmental Health
Research field(s) Enabling and Novel Technologies
Genetics and Epidemiology
PSP Element(s) G-530001-001
G-504000-001
G-504000-007
Grants Helmholtz Association's Initiative and Networking Fund (INF)
DBU
DOI 10.1016/j.trd.2025.105063
WOS ID 001604470200001
Scopus ID 105019199666
Erfassungsdatum 2025-10-27
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