A Multi-Objective Genetic Algorithm Approach to Sustainable Road–Stream Crossing Management

Road–stream crossings (RSCs) are vital for the sustainability of both stream ecosystems and transportation networks, yet many are aging, undersized, or failing. Limited funding and lack of stakeholder coordination hinder effective RSC management. This study develops a multi-objective optimization (MOO) framework utilizing the non-dominated sorting genetic algorithm (NSGA-II) to maximize and balance diverse stakeholder interests (i.e., environmental and transportation agencies) while minimizing management costs. MOO was used to identify optimal RSC management scenarios at a watershed scale, using the Piscataqua–Salmon Falls watershed, New Hampshire, as a testbed. It was found that MOO consistently outperformed the currently used scoring and ranking method by the environmental and transportation agencies, improving the environmental and transportation objectives by at least 19.56% and 37.68%, respectively, across all evaluated budget limits. These improvements translate to a maximum cost saving of USD 19.87 million under a USD 50 million budget limit. Structural conditions emerged as the most influential factor, with a Pearson coefficient of 0.60. This research highlights the potential benefits of a data-driven, optimization-based approach to sustainable RSC management.