This monograph provides a comprehensive overview of methods for searching, evaluating, and optimizing highway location and alignments using genetic algorithms (GAs), a powerful Artificial Intelligence (AI) technique. It presents a two-level programming structure to deal with the effects of varying highway location on traffic level changes in surrounding road networks within the highway location search and alignment optimization process. In addition, the proposed method evaluates environmental impacts as well as all relevant highway costs associated with its construction, operation, and maintenance. The monograph first covers various search methods, relevant cost functions, constraints, computational efficiency, and solution quality issues arising from optimizing the highway alignment optimization (HAO) problem. It then focuses on applications of a special-purpose GA in the HAO problem where numerous highway alignments are generated and evaluated, and finally the best ones are selected based on costs, traffic impacts, safety, energy, and environmental considerations. A review of other promising optimization methods for the HAO problem is also provided in this monograph.

This monograph provides a comprehensive overview of methods for searching, evaluating, and optimizing highway location and alignments using genetic algorithms (GAs), a powerful Artificial Intelligence (AI) technique. It presents a two-level programming structure to deal with the effects of varying highway location on traffic level changes in surrounding road networks within the highway location search and alignment optimization process. In addition, the proposed method evaluates environmental impacts as well as all relevant highway costs associated with its construction, operation, and maintenance. The monograph first covers various search methods, relevant cost functions, constraints, computational efficiency, and solution quality issues arising from optimizing the highway alignment optimization (HAO) problem. It then focuses on applications of a special-purpose GA in the HAO problem where numerous highway alignments are generated and evaluated, and finally the best ones are selected based on costs, traffic impacts, safety, energy, and environmental considerations. A review of other promising optimization methods for the HAO problem is also provided in this monograph.

This monograph provides a comprehensive overview of methods for searching, evaluating, and optimizing highway location and alignments using genetic algorithms (GAs), a powerful Artificial Intelligence (AI) technique. It presents a two-level programming structure to deal with the effects of varying highway location on traffic level changes in surrounding road networks within the highway location search and alignment optimization process. In addition, the proposed method evaluates environmental impacts as well as all relevant highway costs associated with its construction, operation, and maintenance. The monograph first covers various search methods, relevant cost functions, constraints, computational efficiency, and solution quality issues arising from optimizing the highway alignment optimization (HAO) problem. It then focuses on applications of a special-purpose GA in the HAO problem where numerous highway alignments are generated and evaluated, and finally the best ones are selected based on costs, traffic impacts, safety, energy, and environmental considerations. A review of other promising optimization methods for the HAO problem is also provided in this monograph.