TRAFFIC NOISE MODELLING AT THE CENTRAL BUSINESS DISTRICT OF ONDO TOWN, NIGERIA

Abstract

The recognition of road traffic noise as one of the main sources of environmental pollution has led to the formulation of models that enable the prediction of noise levels from fundamental variables. Traffic noise prediction models are required as aids in the design of roads and sometimes in the assessment of existing, or envisaged changes in, traffic noise conditions. In this research, traffic noise and other key parameters (traffic composition, traffic volume and speed) critical to traffic noise at the Central Business District of Ondo town were investigated. Data on traffic composition, traffic volume, speed and traffic noise levels were obtained and fitted to the Calculation of Road Traffic Noise (CoRTN) model which is a standard traffic noise prediction model. Further analysis was carried out on data collected to develop a Multiple Linear Regression (MLR) model for traffic noise prediction at the study area. Data collected showed that the measured noise levels in equivalent noise level (Leq) for the study locations ranged between 68 dB(A) and 76 dB(A); these traffic noise levels exceed World Health Organization (WHO) and Federal Highway Administration (FHWA) permissible limit of 55 dB(A) and 60 dB(A) respectively. Other results revealed that both the (CoRTN) and (MLR) models showed efficient predictive capability when compared to measured traffic noise levels with an acceptable coefficient of determination (R2) values of 0.943 and 0.963 respectively. The difference between the measured traffic noise levels and predicted MLR noise levels ranged from - 0.44 dB(A) to 1.48 dB(A), with a mean difference of 0.37 dB(A). Also, the differences between the measured traffic noise levels and predicted CoRTN noise levels were in the range of - 0.51 dB(A) to 2.09 dB(A) with mean difference of 1.19 dB(A); these values are adjudged satisfactory since it is within the +/- 3.0 (dB)A allowed by Federal Highway Administration (FHWA). Hence, the model is robust and reliable in predicting the traffic noise level for the study area.