Data-Driven Predictive Modeling of Highway Construction Cost Items

Abstract

The highway network is an economically necessary form of transportation that has a significant impact on the quality of the life of the citizens who use it. Cost overruns in highway projects have been a universal occurrence that jeopardize the development, maintenance, and expansion of this vital infrastructure. Incorrect cost estimations can drive decision makers to pass ineffective policies that have played a large role in the cost overruns of transportation construction projects. The existing prediction models in the literature are limited in one or multiple areas of modeling approach, inputs, and model development robustness. In this research, a model was developed to accurately predict the total construction cost of highway projects by utilizing machine learning algorithms. This study developed a modeling pipeline to automate much of the cost forecasting process, reducing the amount of manual work and dependence on skilled data scientists. This study used the Florida Department of Transportation’s (FDOT’s) critical highway construction cost items between 2001 and 2017 to test the model. The highways of Florida were selected for testing due to the states’ population growth, high immigrant population, logistics, and hurricane frequency. This study used a pool of five categories of independent variables (69 variables total), including the construction market, energy market, socioeconomics, US economy, and temporal variables, which were compiled from relevant sources and existing literature. The results revealed that our linear model exhibits superiority in generalization and prediction of cost items over nonlinear models and is capable of accurately forecasting highway construction costs. Our suggested approach in this study also provides more accurate forecasts for the detailed cost estimation by considering the monthly historical information for the average 92.6% of the six highway construction types mentioned with a 92.51% prediction accuracy. By employing our developed model, local governments, network operators, contractors, and logistics sectors would be capable of a more exact prediction of highway construction costs.