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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)
Verlagsversion Forschungsdaten DOI
Open Access Hybrid
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter 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
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 1361-9209
e-ISSN 1879-2340
Zeitschrift Transportation Research Part D: Transport and Environment
Quellenangaben Band: 149, Heft: , Seiten: , Artikelnummer: 105063 Supplement: ,
Verlag Elsevier
Verlagsort The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, England
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of Epidemiology (EPI)
POF Topic(s) 30205 - Bioengineering and Digital Health
30202 - Environmental Health
Forschungsfeld(er) Enabling and Novel Technologies
Genetics and Epidemiology
PSP-Element(e) G-530001-001
G-504000-001
G-504000-007
Förderungen 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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