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<p>Road traffic noise remains as one of the most persistent environmental stressors in urban areas, with mixed traffic compositions and inadequate infrastructural buffers aggravating acoustic exposure for roadside residents. Although previous studies have examined overall traffic flow and speed effects, limited attention has been given to understanding how vehicles’ fuel type and physical barriers jointly influence noise propagation under real-world conditions. The current study aims to quantify the relationship between vehicle fuel type, frequency-dependent noise emission, and the attenuation performance of compound walls in urban traffic environments. Field measurements were conducted along a mid-block section of the Hyderabad-Warangal highway using Class-1 sound level meters, synchronized video monitoring, and environmental sensors. One-third octave band data was analysed to classify vehicles using a Euclidean distance-based frequency comparison, while regression modelling was used to predict equivalent sound levels from traffic speed, volume, and composition. Results showed that diesel vehicles produced higher sound pressure levels than petrol vehicles, particularly within the frequency range of 125 to 2000&#xa0;Hz, corresponding to stronger combustion and mechanical excitation. The compound wall achieved an average LAeq reduction of 9.5&#xa0;dB (13.7%), compared with 5.0&#xa0;dB (7.3%) under open-gate conditions, with all reductions statistically significant (<i>p</i> &lt; 0.05). The developed regression model exhibited a high predictive accuracy (R2 = 0.986), demonstrating the combined influence of vehicle and traffic parameters on urban acoustic levels. Overall, the integration of frequency-domain vehicle classification with field-validated barrier performance analysis provides a novel and practical framework for data-driven noise mitigation and evidence-based infrastructure planning, enabling more sustainable acoustic management in rapidly urbanizing cities.</p>

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Assessing the influence of vehicular fuel type on urban road traffic noise using frequency-domain acoustic monitoring

  • 期刊论文

  • 发表日期：

    2026年4月11日

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  • Nallavelli Srinidhi Reddy,
  • Venkaiah Chowdary,
  • Boddu Sudhir Kumar

摘要

Road traffic noise remains as one of the most persistent environmental stressors in urban areas, with mixed traffic compositions and inadequate infrastructural buffers aggravating acoustic exposure for roadside residents. Although previous studies have examined overall traffic flow and speed effects, limited attention has been given to understanding how vehicles’ fuel type and physical barriers jointly influence noise propagation under real-world conditions. The current study aims to quantify the relationship between vehicle fuel type, frequency-dependent noise emission, and the attenuation performance of compound walls in urban traffic environments. Field measurements were conducted along a mid-block section of the Hyderabad-Warangal highway using Class-1 sound level meters, synchronized video monitoring, and environmental sensors. One-third octave band data was analysed to classify vehicles using a Euclidean distance-based frequency comparison, while regression modelling was used to predict equivalent sound levels from traffic speed, volume, and composition. Results showed that diesel vehicles produced higher sound pressure levels than petrol vehicles, particularly within the frequency range of 125 to 2000 Hz, corresponding to stronger combustion and mechanical excitation. The compound wall achieved an average LAeq reduction of 9.5 dB (13.7%), compared with 5.0 dB (7.3%) under open-gate conditions, with all reductions statistically significant (p < 0.05). The developed regression model exhibited a high predictive accuracy (R2 = 0.986), demonstrating the combined influence of vehicle and traffic parameters on urban acoustic levels. Overall, the integration of frequency-domain vehicle classification with field-validated barrier performance analysis provides a novel and practical framework for data-driven noise mitigation and evidence-based infrastructure planning, enabling more sustainable acoustic management in rapidly urbanizing cities.