Umudike Journal of Engineering and Technology

Michael Okpara University of Agriculture, Umudike


Ihemeje, J.
Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria

Onyelowe, K. C.
Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria;Department of Mechanical and Civil Engineering, Kampala International University-Western Campus, Kampala, Uganda.

Ebid, A. M.
Deaprtment of Civil Engineering, Faculty of Engineering, Future University in Egypt, Egypt


The traffic noise big data collected from studying traffic situations in Port-Harcourt Nigeria selected trunks A and C roads sub-sectioned as flexible pavements locations 1, 2, and 3 and flexible and rigid pavements locations 4 and 5 respectively has been analyzed by using the multi-linear regression (MLR) technique. Traffic noise is an acoustic hazard affecting mostly people living closest to the roadway pavement. The solution of such a high degree of discomfort on roadside dwellers deserves serious study. This work considered traffic parameters like distance between dwellers and the roadway, traffic count, vehicular speed, traffic periods, etc. in modeling the traffic noise intensity (TNI) of the selected road. The average peak traffic noise for location 1 obtained at various distances of 5m, 10m and 15m from the centre of the roadway are 85.59dB, 84.93dB and 83.97dB respectively, for location 2 are 86.52dB, 85.34dB and 84.26dB respectively, for location 3 are 84.38dB, 83.88dB and 83.32dB respectively, for location 4 are 85.16dB, 84.56dB and 83.55dB respectively, for location 5 Trunk C Flexible Pavement are 55.46dB, 54.36dB and 53.99dB respectively and for Trunk C Rigid Pavement are 60.58dB, 59.58dB and 58.96dB respectively. The traffic noise values for location 1-4 had higher noise intensity and same range, it was categorized as Trunk A flexible pavement and classified as heavy-trafficked routes while location 5 (Trunk C) had lower noise intensity and same range which was classified as light-trafficked routes. MLR predicted the TNI with R2 (0.2015, 0.2110, 0.1894, 0.2203, 0.2275, 0.1983, 0.4398, 0.4398, 0.3907, 0.3952, 0.3427, 0.3355, 0.3149, 0.1505, 0.1526, 0.1441, 0.002, 0.0012, 0.001) values for the model along the selected routes. From the result, the distance of noise measurement from the centre of the roadway of Trunk C flexible pavement with the most significant p-value of 0.804145, the equivalent traffic volume and traffic speed had p-values of 0.014782 and 3.22E-50 respectively whereas that of Trunk C rigid pavement with the most significant p-value of 0.872625, the equivalent traffic volume and traffic speed had p-values of 0.265025 and 3.67E-61 respectively. The noise level increased more on rigid pavements than that of flexible pavements, which is attributed to more voids on rigid pavements and the higher frictional noise due to increased frictional force between the vehicle tires and road surfaces with the grip being more in rigid pavements. At the end of the exercises, it was observed that ARIMA (R2 greater 90%) performed better than MLR even with the technical advantage of determining noise difference between interfering points using the auto-correlation factor (ACF) and the partial auto-correlation factor (PACF).

Keywords: MLR; Noise Intensity; Traffic Volume; Model Prediction; Rigid and Flexible Pavement; Pavement Traction
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Wednesday, June 08, 2022

Vol. 8 No. 1, June 2022

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