Roads Development Optimization for All-Season Service Accessibility Improvement in Rural Nepal Using a Novel Cost-Time Model and Evolutionary Algorithm

Existing methods of prioritizing rural roads for construction in hilly and mountainous settings require expensive data collection or major simplifications of ground conditions. Traditional social surplus based-methods favor economic and political decision criteria over social criteria, despite evidence of the latter’s importance, and struggle to scale beyond major roads to feeder roads, forcing local governments with limited capacity to adopt ad-hoc alternative criteria. Using roads proposed for construction in Nepal’s remote Karnali province, this paper develops a scalable method to prioritize these roads for inclusion in construction plans with the aim of optimizing potential accessibility improvements to specified services in dry and monsoon seasons—within Karnali’s infrastructure budget constraints. Road-specific improvements in accessibility to services are measured by estimating accessibility changes resulting from each proposed road within a multimodal accessibility model. In this paper, walking across Karnali’s mountainous, high-elevation terrain is incorporated as a primary modality—a rarity in related accessibility literature. These improvements are implemented within heuristic and integer-linear programming optimization models. Optimization-determined solutions were calculated within a day, and substantially outperformed the actual roads selected by Karnali’s provincial government in terms of accessibility, efficiency, and political economy.