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The State Highway Operation and Protection Program (SHOPP) is crucial for maintaining California’s 15,000-mile state highway system, which includes projects like pavement rehabilitation, bridge repair, safety enhancements, and traffic management systems. Administered by Caltrans, SHOPP aims to preserve highway efficiency and safety, supporting economic growth and public safety. This research aimed to develop robust cost-estimating models to improve budgeting and financial planning, aiding Caltrans, the California Transportation Commission (CTC), and the Legislature. The research team collected and refined comprehensive data from Caltrans project expenditures from 1983 to 2021, ensuring a high-quality dataset. Subject matter experts validated the data, enhancing its reliability. Two models were developed: a statistical model using exponential regression to account for non-linear cost growth, and an AI model employing neural networks to handle complex relationships in the data. Model performance was evaluated based on accuracy and reliability through repeated testing and validation. Key findings indicated that the new models significantly improved the precision of cost forecasts, reducing the variance between predicted and actual project costs. This advancement minimizes budget overruns and enhances resource allocation efficiency. Additionally, leveraging historical data with current market trends refined the models’ predictive power, boosting stakeholder confidence in project budgeting and financial planning. The study’s innovative approach, integrating machine learning and big data analytics, transforms traditional estimation practices and serves as a reference for other state highway programs. Continuous improvement and broader application of these models are recommended to further enhance cost estimation accuracy and support informed decision-making in transportation infrastructure management.
ELHAMI NASR, PHD
Dr. Nasr is a Subject Matter Expert in Construction and Project Management. He built his career of over three decades with dual careers in academia and industry. He is a Professor of Construction Engineering Management at California State University, Long Beach. Previously, he served as Vice Provost, Interim Provost, and Vice President of Academic Affairs at Florida Polytechnic University. He also worked in industry (Caltrans) and in various national and international consulting roles, where he initiated, developed, and delivered hundreds of specialized and customized companywide project management training programs for large-scale professional organizations, in both private and government sectors.
TARIQ SHEHAB, PHD
Dr. Shehab is an expert in developing cost estimating applications and the field of artificial intelligence. He has more than 25 years of experience in developing intelligent cost estimating applications, during which he developed systems for highway projects, utility pipe networks, and school buildings. His contributions also included the determination of key factors that attribute to the maintenance costs of major infrastructure facilities. He is the author and co-author of numerous articles published in many ASCE journals and other equally prestigious periodicals.
NIGEL BLAMPIED, PHD
Nigel Blampied’s research and teaching focuses on project management in public transportation agencies. He teaches in the Master of Transportation Management program at San José State University and is a Research Associate at the Mineta Transportation Institute. His doctoral dissertation at the University of California, Berkeley, discussed parametric cost estimating at the early conceptual phase of projects, the subject, in part, of this report, and used data from the SHOPP, as does this report.
VINIT KANANI
Vinit Kanani earned his master’s degree in artificial intelligence from San José State University, where he excelled academically and developed a solid foundation in machine learning and data science. He is currently working as a Data Discovery ETL engineer for Sikka Software, where his responsibilities include designing and implementing data pipelines, ensuring data quality, and applying machine learning techniques to extract valuable insights from complex datasets. His professional interests are centered on machine learning and artificial intelligence, and he is passionate about using these technologies to solve real-world problems.
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