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<p>The integration of metro systems into urban transport networks, alongside persistent road traffic, has amplified concerns about cumulative noise pollution in metropolitan environments. This study presents a novel statistical analysis of environmental noise at metro stations (MSs) and traffic curb areas (TCAs), aiming to decode distinct acoustic signatures. Through 1/3-octave band analysis, metro operations were found to elevate ambient noise levels by 1.5–4.4&#xa0;dB(A), with a dominant spectral peak consistently emerging at 800&#xa0;Hz a unique identifier of metro-induced noise. Road traffic exhibited <i>L</i><sub>eq</sub> values between 75.4 and 82.0&#xa0;dB(A) and background levels (L<sub>90</sub>) of 70.5–78.0&#xa0;dB(A), with honking peaks concentrated between 400 and 3150&#xa0;Hz and idling engine noise spanning 400–1600&#xa0;Hz. A predictive noise model was developed using multivariate regression, incorporating key acoustic contributors such as honking frequency, heavy-vehicle percentage, and road typology. The model demonstrated robust performance (adjusted <i>R</i><sup>2</sup> = 0.81), offering a reliable tool for forecasting urban noise dynamics. This integrative approach, combining real-time spectral profiling with predictive analytics, enables precise identification of high-risk acoustic zones and informs strategic mitigation planning. The study underscores the critical need for frequency-resolved noise monitoring in rapidly urbanizing corridors and advocates for evidence-based acoustic interventions. By decoding the spectral footprints of metro and traffic noise, this research advances urban noise management frameworks toward smarter, more sustainable city soundscapes.</p>

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Spectral characterization and predictive modelling of urban traffic and metro rail noise

  • 期刊论文

  • 发表日期：

    2026年1月26日

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  • Vijaya Laxmi,
  • Chimurkar Navinya,
  • Harish C. Phuleria

摘要

The integration of metro systems into urban transport networks, alongside persistent road traffic, has amplified concerns about cumulative noise pollution in metropolitan environments. This study presents a novel statistical analysis of environmental noise at metro stations (MSs) and traffic curb areas (TCAs), aiming to decode distinct acoustic signatures. Through 1/3-octave band analysis, metro operations were found to elevate ambient noise levels by 1.5–4.4 dB(A), with a dominant spectral peak consistently emerging at 800 Hz a unique identifier of metro-induced noise. Road traffic exhibited Leq values between 75.4 and 82.0 dB(A) and background levels (L90) of 70.5–78.0 dB(A), with honking peaks concentrated between 400 and 3150 Hz and idling engine noise spanning 400–1600 Hz. A predictive noise model was developed using multivariate regression, incorporating key acoustic contributors such as honking frequency, heavy-vehicle percentage, and road typology. The model demonstrated robust performance (adjusted R2 = 0.81), offering a reliable tool for forecasting urban noise dynamics. This integrative approach, combining real-time spectral profiling with predictive analytics, enables precise identification of high-risk acoustic zones and informs strategic mitigation planning. The study underscores the critical need for frequency-resolved noise monitoring in rapidly urbanizing corridors and advocates for evidence-based acoustic interventions. By decoding the spectral footprints of metro and traffic noise, this research advances urban noise management frameworks toward smarter, more sustainable city soundscapes.