Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis

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Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis


The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.


Dr. Kim earned his PhD in Transportation in the Department of Civil and Environmental Engineering at the University of California, Davis in 2008. He is a project manager at the University of California Pavement Research Center at Berkeley and Davis. Dr. Kim led research projects on life cycle cost analysis (LCCA) for 15 years in California. He developed the California-customized LCCA software, RealCost CA versions, and co- authored Caltrans LCCA procedure manual. He trained LCCA implementation and provided LCCA supports to the Caltrans transportation engineers. He published over 20 papers in the peer-reviewed international journals including Transportation Research Record and ASCE Journal of Transportation, Infrastructure and Construction Management. Dr. Kim, as a part-time faculty, teaches Transportation Engineering courses in the Department of Civil Engineering at Sacramento State.

Ghazan Khan is an Associate Professor in the Department of Civil Engineering at Sacramento State. His teaching and research focuses on Transportation Engineering, Traffic Operations and Safety, Geographic Information Systems (GIS), and Statistics. Dr. Khan joined the Department of Civil Engineering at Sacramento State in 2013.

Prior to joining Sacramento State, Dr. Khan worked for nine years as an Assistant Researcher, Research Associate, and Research Assistant at the Traffic Operations and Safety (TOPS) Laboratory, University of Wisconsin-Madison leading and working on various national and state sponsored research projects in the areas of Traffic Operations and Safety, Traffic Control Devices, Geographic Information Systems (GIS) and Statistical Applications in Transportation, Transportation Data and Asset Management, and so on.

December 2020
Life cycle cost analysis
Pavement materials
Unit costs



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