What question did this study set out to answer?

The central aim is to develop a reliable model for predicting traffic noise on four-lane Indian highways.

March 29, 2026Open Access

Development of a Traffic Noise Prediction Model for Four-Lane Indian Highways

Puntos clave

The central aim is to develop a reliable model for predicting traffic noise on four-lane Indian highways.
Used traffic data from National Highways under free-flow conditions.
Measured vehicular speed with a radar gun and documented traffic volume via videography.
Analyzed octave spectrum characteristics of traffic acoustics.
Compiled 460 data sets at 15-minute intervals, covering various traffic periods.
Applied multiple regression techniques to predict equivalent noise levels (LAeq).
Developed model shows heavy vehicles as the most significant contributor to traffic noise.
Speed also emerged as a critical variable impacting noise levels.
The new model was compared with the CORTN model and other empirical models, showing competitive results.
Six different scenarios for noise prediction were developed based on collected data.

Resumen

Heterogeneous traffic flow significantly influences the noise level in the surrounding area of highways.The traffic composition in India comprises vehicles with different sizes, speeds, and operating characteristics.Noise pollution is recognized as a major environmental stressor affecting human health.This study predicts traffic noise using traffic data from National Highways with free-flow traffic conditions.A radar gun measures vehicular speed, and videography documents traffic volume.Furthermore, the octave spectrum characteristics of traffic acoustics were analyzed.A number of 460 data sets were meticulously compiled at 15minute intervals across weekdays and weekends, encompassing peak and off-peak periods.This data was used to predict equivalent noise levels (LAeq) using multiple regression techniques.The developed model is compared with the Calculation of Road Traffic Noise (CORTN) model and other recent empirical models.A comparative significance of the input variables is evaluated.Heavy vehicles are observed as the most significant variable, followed by speed.Six scenarios were developed to predict the traffic noise.The results of this study will help to predict and reduce traffic noise in mixed traffic flow conditions.

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Cite This Study

Parida et al. (Thu,) studied this question.

synapsesocial.com/papers/69c8c2fcde0f0f753b39d726 https://doi.org/https://doi.org/10.30638/eemj.2026.001

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