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Infrastructure development is crucial for any country because it has significant effects on both social welfare and economic growth. Due to limited financial resources, the government prioritizes the infrastructure investment that maximizes impact and resource efficiency. However, many methods to support the decision-making undermine to derive insights from project experiences and ensure time- and cost-effectiveness. This study aims to identify the critical factors for the preliminary feasibility study (PFS) of large-scale infrastructure investment in South Korea by developing decision-tree classifiers based on the type of infrastructure (point, linear, and plane). The classifiers use data from 1,905 evaluations of 354 projects in Korea, classifying them as go, pending, or no-go with accuracies of 84.64%, 82.08%, and 87.88% by type of infrastructure, respectively. The critical factors affecting PFS are benefit-cost ratio, city-level development rank, regional development level, and total project cost among the 13 decision factors of PFS. Although multicriteria decision-making is supposed to comprehensive evaluation of various factors, some factors have governed the result, whereas others do not affect. The impact of these factors is discussed depending on the type of infrastructure. The results show the potential for this approach to improve efficiency in terms of time and cost by enabling early and reliable project screening. Although the study focuses mainly on South Korea, findings can provide valuable insights for more informed and efficient decisions to other countries.
Park et al. (Tue,) studied this question.
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