METHODS AND TECHNIQUES FOR ESTIMATING CONCEPTUAL COSTS IN PUBLIC HIGHWAY PROJECTS: A SYSTEMATIC LITERATURE REVIEW

Keywords: conceptual cost, cost estimation, highway projects, estimation approaches, cost drivers highlight

Abstract


Accurate cost estimation during the conceptual phase of public highway construction is essential for effective transportation planning and budgeting. This paper reviews techniques and methods for initial cost estimation in highway projects, focusing on accuracy, trends, and key cost drivers. The research methodology involved selecting relevant papers by reviewing titles, abstracts, keywords, and full texts during the final stage. This process yielded 87 relevant publications and 33 cost estimation methods and techniques spanning the period from 1980 to 2025. The study found that artificial neural networks, machine learning, and regression analysis are the most used methods for predicting costs early in projects, with machine learning techniques showing the highest accuracy. Among the 184 cost drivers reviewed, 50 were grouped into five categories, providing a basis for selecting critical inputs during the planning phase of cost estimation for highway projects. A thorough review of the research literature highlights gaps and provides recommendations for future studies to focus on the use of advanced technologies to improve cost estimation. Specifically, employing GIS and 3D terrain models, along with geomechanical data, can help accurately calculate the quantities of all items in the bill of quantities. Such advancements are expected to enhance the precision of conceptual cost estimation in highway construction.

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Published
2026/05/04
Section
Review Paper