Improving Transportation Construction Project Performance: Development of a Model to Support the Decision-Making Process for Incentive/Disincentive Construction Projects

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Improving Transportation Construction Project Performance: Development of a Model to Support the Decision-Making Process for Incentive/Disincentive Construction Projects


This research presents a project time and cost performance simulation model to assist project planners and managers by providing a complete picture during the Incentive/Disincentive (I/D) contracting decision-making process of possible performance outcomes with probabilities based on historical data. This study was performed by collecting transportation construction project data. The collected project data from the Florida Department of Transportation were evaluated using time and cost performance indices and then statistical data analysis was performed to identify important factors that influence construction project time performance. Using Monte Carlo simulation procedures, this study demonstrated a methodology for developing an I/D project time and cost performance prediction model. User-friendly visual interfaces were developed to perform the simulation and report results using Visual Basic Application programming. The developed model was validated using additional cases of transportation construction projects.

Based on statistical analysis, this research found that several project factors influence I/D contracting performance. The important factors that had significant impacts on project performance were the effects of contract type, project type, district, project size, project length, maximum incentive amount, and daily I/D amount. In conclusion, the developed model applied to I/D contracting projects will be a useful tool to assist the project planners and managers during the decision-making process and will promote the efficient use of I/D contracting, which will benefit the traveling public by saving their travel time from construction delays. With additional project data, the developed model can be updated easily and the more data used for the model, the better the accuracy of prediction that can be expected.



Jae-Ho Pyeon, MTI Research Associate, is an assistant professor in the Department of Civil and Environmental Engineering at San Jose State University. Dr. Pyeon received both his masters and doctors degrees in civil engineering from the University of Florida. He teaches and conducts research in the area of construction engineering and management, and teaches graduate courses in construction management and information technology and undergraduate courses in project management, civil engineering law, scheduling, and construction methods and equipment.

Dr. Pyeons research interests include seeking efficient ways to improve the construction process, assessing uncertainty in construction, and developing decision support systems. Specific research areas are alternative contracting methods, project delivery systems, sustainable construction, project risk management, and innovative construction techniques.

Dr. Pyeon has been involved in several federal- and/or state-funded transportation construction research projects including evaluation of alternative contracting techniques on FDOT construction projects; improving the time performance of highway construction contracts; development of improved procedures for managing pavement markings during highway construction projects; and development of procedures for utilizing pit proctors in the construction process for pavement base materials. Dr. Pyeon is a member of the Construction Research Council, Construction Institute, American Society of Civil Engineers.




Taeho Park, MTI Research Associate, is a professor at San Jos State University. He teaches several operations-related courses, including operations management, supply chain management, total quality management, and materials management. Dr. Park received both B.S. and M.S. degrees in industrial engineering from Seoul National University, Korea, and his PhD in industrial engineering from University of Wisconsin-Madison. He has performed several academic and industry consulting projects in the areas of operations management, total quality management, risk management, and logistics.

Dr. Parks research interests include production and risk management of supply chain systems in both manufacturing and service sectors, green technology management, and logistics system management. His research papers have been published in several journals, a book, and conference proceedings,including the Journal of Operations Management, Computers and Industrial Engineering, International Journal of Production Research, Information Systems, International Journal of Operations and Quantitative Management, European Journal of Operational Research, the Korean Production and Operations Management Society, Journal of Vocational Education Research, California Journal of Operations Management, Journal of Korea Trade, Journal of the Korea Society of Supply Chain Management, and International Journal of Computer Applications in Technology.

March 2010
Decision support systems
Highway construction
Performance evaluations
Statistical analysis



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