Data-Driven Predictive Modeling of Highway Construction Cost Items
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 3
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.
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Data Availability Statement
Data analyzed during the study were provided by a third party. Requests for data should be directed to the provider indicated in the Acknowledgements. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
The Florida Department of Transportation provided all the data needed for this project including the historical highway construction cost items. The researchers wish to thank the following FDOT individuals: Dianne Perkins, Cheri Sylvester, and June Mobley. We would also like to thank Hari Salkapuram (HDR Inc.), and Mansoor Khuwaja (Hanson Service Inc.).
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Published In
Journal of Construction Engineering and Management
Volume 147 • Issue 3 • March 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Apr 29, 2020
Accepted: Sep 15, 2020
Published online: Dec 24, 2020
Published in print: Mar 1, 2021
Discussion open until: May 24, 2021
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