A framework for transportation infrastructure cost prediction: a support vector regression approach

Abstract

ABSTRACT Highway agencies and Departments of Transportation (DOTs) often face a challenge in estimating annual maintenance expenditures (AMEXs) to allocate available funds to various divisions in their jurisdictions. Few AMEX models were developed in the past, which are based on traditional statistical methods. This study introduces a support vector regression (SVR) approach to predict AMEX using data on the U.S. rural interstate highway pavement performance. Results show that the best SVR model is based on the Gaussian kernel and sequential minimal optimization algorithm. The developed SVR model can reliably predict the AMEX at the state level with a root mean squared error of 1.28. Entropy analysis results indicate the existence of mutual information (MI) between all the considered variables and the AMEX, with the highest MI value of about 79% between the AMEX and the number of lane miles with IRI values greater than 1.5 m/km.