Identification and Correlation of Noise Hotspots in Metro Underground with Physical Track Characteristics for Sustainable Transport Planning

Millions of commuters depend on the London Underground as their primary mode of transportation in the city. Despite its historical significance, the metro’s aging infrastructure contributes to persistent noise pollution. Noise pollution undermines environmental and societal value, which are key pillars of sustainability. This study focuses on the identification and analysis of track noises present on the Northern Line of the London Underground between Camden Town and South Wimbledon. Robust data collection involves onboard noise recordings during multiple train journeys using the MOTIV mobile application. The noise data are meticulously analysed using Fast Fourier Transform (FFT) to break down complex noise recordings into constituent frequencies, allowing for accurate quantification of noise levels. Noise hotspots are graphically represented to highlight areas with disproportionately high noise levels. Correlation analysis of track geometry and noise levels reveals that tighter curves and larger cant heights often coincide with increased noise levels, with a horizontal radius of 353 m and a cant of 79 mm linked to the highest impact noise recorded at 95.98 dB. The findings offer actionable insights for targeted noise mitigation and track maintenance, emphasizing the importance of optimizing track design to reduce noise pollution and support sustainable transport infrastructure.

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Publication Details

Journal
Sustainability
Published
2025-02-22
DOI
https://doi.org/10.3390/su17051880
Primary Topic
Railway Engineering and Dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

Identification and Correlation of Noise Hotspots in Metro Underground with Physical Track Characteristics for Sustainable Transport Planning

Sakdirat Kaewunruen, Junhui Huang, Mohamad Ali Ridho Bin Khairul Anuar, Nishanth Muniasamy
Sustainability
Railway Engineering and Dynamics
article

Identification and Correlation of Noise Hotspots in Metro Underground with Physical Track Characteristics for Sustainable Transport Planning

Sakdirat Kaewunruen, Junhui Huang, Mohamad Ali Ridho Bin Khairul Anuar, Nishanth Muniasamy
article en

Abstract

Millions of commuters depend on the London Underground as their primary mode of transportation in the city. Despite its historical significance, the metro’s aging infrastructure contributes to persistent noise pollution. Noise pollution undermines environmental and societal value, which are key pillars of sustainability. This study focuses on the identification and analysis of track noises present on the Northern Line of the London Underground between Camden Town and South Wimbledon. Robust data collection involves onboard noise recordings during multiple train journeys using the MOTIV mobile application. The noise data are meticulously analysed using Fast Fourier Transform (FFT) to break down complex noise recordings into constituent frequencies, allowing for accurate quantification of noise levels. Noise hotspots are graphically represented to highlight areas with disproportionately high noise levels. Correlation analysis of track geometry and noise levels reveals that tighter curves and larger cant heights often coincide with increased noise levels, with a horizontal radius of 353 m and a cant of 79 mm linked to the highest impact noise recorded at 95.98 dB. The findings offer actionable insights for targeted noise mitigation and track maintenance, emphasizing the importance of optimizing track design to reduce noise pollution and support sustainable transport infrastructure.

SustainabilityVol. 17(5)
Transport for London (GB), University of Birmingham (GB)
European Commission
Openalex Percentile: Top 97%
Railway Engineering and Dynamics
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Identification and Correlation of Noise Hotspots in Metro Underground with Physical Track Characteristics for Sustainable Transport Planning — Sakdirat Kaewunruen, Junhui Huang, et al. · Sustainability (2025) | TGRS Research Map | TGRS