Potentiality of tree variables as predictors in pavement roughness progression rate modelling

Abstract

This paper presents statistical evidence of roadside vegetation’s contribution to pavement roughness progression rates. Detailed statistical and regression analysis of the roadside vegetation data collected via satellite imageries and road roughness data collected via high speed road profiler was performed. Elaborative investigation on interaction between roadside vegetation and waveband roughness progression has provided a clear indication of tree variable’s contribution on road deterioration. Statistical parameters such as moderate Pearson correlation coefficient (r) values, low mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) values, high Willmott’s index of agreement (d) were obtained for training and validation datasets, that depicted the potentiality of tree variables as predictors in pavement roughness progression rate modelling. Statistical evidence showed that effect of trees on road deterioration was more noticeable on long wavelength roughness progression rates. This can be justified via prevailing soil moisture interaction in expansive soil deposits subjected to moisture withdrawal of deciduous trees in arid climate conditions. Overall, the findings of this paper exemplify on the necessity of considering the presence of roadside vegetation in road deterioration analysis, and suggesting the scope of improvement for prediction performance.