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 (M...

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Vydáno v:Sustainability Ročník 17; číslo 9; s. 3987
Hlavní autoři: Asadifakhr, Koorosh, Roy, Samuel G., Taherkhani, Amir Hosein, Han, Fei, Bell, Erin S., Mo, Weiwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2025
Témata:
Design
Funding
Genetic algorithms
Linear programming
Management research
Methods
Multiple criteria decision making
Optimization
Pareto optimum
Roads
Roads & highways
Streamflow
Streets
Sustainable development
Transportation planning
United States
Watersheds
United States
United States > US
Massachusetts
Maine
ISSN:2071-1050, 2071-1050
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Shrnutí: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.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su17093987