A Novel Framework for Prioritizing Road Construction Projects via Clustering-Based Optimization of Resource Allocation

Expanding road transport infrastructure remains a strategic priority in many developing nations, particularly those with rising geopolitical relevance. In contexts where fiscal resources are constrained and competing stakeholder interests complicate decision-making, a structured prioritization approach becomes essential. This study identifies four core parameters influencing road design and construction, ensuring alignment with regulatory and engineering standards. A rigorous data collection protocol was implemented, and the resulting quantitative analyses informed the development of a novel decision-support framework grounded in the Charged System Search (CSS) algorithm. Utilizing an unsupervised clustering method, 300 candidate projects were classified into three priority tiers, with assigned weights of 0.94 (Priority 1), 0.49 (Priority 2), and 0.40 (Priority 3). These clusters guided the formulation of four distinct budgeting strategies, each accommodating between 8 and 300 projects. Prioritization was driven by four key variables: travel demand, socioeconomic benefits, cost efficiency, and environmental impact. The proposed model provides national infrastructure authorities with a data-driven framework for managing complex transport development portfolios, enhancing both transparency and strategic coherence.