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Research Article Open access CC BY 4.0

Daytime Vehicular Traffic Noise Analysis and Modeling at Selected Roads in Port Harcourt

V. E. Amah, N. U. Udeh

Advances in Research · pp. 8–17 · Published 18 Apr 2023

10.9734/air/2023/v24i5952

Abstract

Using linear regression techniques, the daytime vehicle traffic noise levels at four significant crossings in Port Harcourt were analyzed and predicted. These are the crossroads of Rumuokwuta, Agip, 1st Artillery, and GRA. These crossroads are often quite active with both traffic and pedestrian activity. The sound levels, relative humidity and wind speed along with temperature were all measured using a sound level meter, relative humidity meter, and anemometer, respectively. Data on traffic volume was also gathered throughout the sample period. This was carried out for a total of 10 hours each day beginning at 7 am throughout the course of three days at each site. For the different sites, noise indices including equivalent noise level (Leq), statistical measure (Ln), noise climate (NC), traffic noise index (TNI), and noise pollution level (NPL) were calculated. With the exception of the GRA junction, statistical analysis reveals that there is no change in the data recorded at the various times of the day (p>0.05). The estimated indices from all sites were compared, and there was no discernible difference (p>0.05). Agip had the lowest TNI at 44.04 dB(A), while Rumuokwuta junction had the highest at 49.7 dB(A). The maximum Leq of 74.9 dB(A) was observed at 1st Artillery while the minimum of 74.1 dB(A) was recorded at Agip. GRA junction recorded the maximum NPL as 76.99 dB(A), while Rumuokwuta had the lowest as 76.3 dB(A). A linear model idea for forecasting noise from independent variables, including the atmospheric conditions and traffic count, was calibrated using multiple linear regression modeling. The coefficient of determination was discovered to vary between 0.5 and 0.95 after dimension reduction by the plotting of standardized charts.

Traffic noise regression noise indices modeling statistical analysis

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