Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Optimizing Urban Road Networks for Resilience Using Genetic Algorithms
Urban road networks face increasing challenges in balancing traffic efficiency, budget limitations, and environmental impacts as cities prepare for future demand. This paper presents a multi-objective optimization approach using Genetic Algorithms (GAs) to enhance the performance of an urban transportation network while integrating sustainability goals. By simultaneously optimizing travel times, reducing bottlenecks, and addressing budget constraints, this framework enables a balanced approach to infrastructure improvement. The inclusion of environmental considerations, such as greenhouse gas (GHG) emissions, aligns network development with broader sustainability objectives, promoting a healthier urban environment. Future extensions of this framework include adaptive strategies to respond to shifting traffic patterns and the potential integration of regulatory constraints, such as emission licenses. The proposed GA approach demonstrates a flexible, scalable solution for urban planners and policymakers tasked with building resilient, sustainable road networks, offering practical insights into addressing the multifaceted demands of modern urban infrastructure.
Optimizing Urban Road Networks for Resilience Using Genetic Algorithms
Urban road networks face increasing challenges in balancing traffic efficiency, budget limitations, and environmental impacts as cities prepare for future demand. This paper presents a multi-objective optimization approach using Genetic Algorithms (GAs) to enhance the performance of an urban transportation network while integrating sustainability goals. By simultaneously optimizing travel times, reducing bottlenecks, and addressing budget constraints, this framework enables a balanced approach to infrastructure improvement. The inclusion of environmental considerations, such as greenhouse gas (GHG) emissions, aligns network development with broader sustainability objectives, promoting a healthier urban environment. Future extensions of this framework include adaptive strategies to respond to shifting traffic patterns and the potential integration of regulatory constraints, such as emission licenses. The proposed GA approach demonstrates a flexible, scalable solution for urban planners and policymakers tasked with building resilient, sustainable road networks, offering practical insights into addressing the multifaceted demands of modern urban infrastructure.
Optimizing Urban Road Networks for Resilience Using Genetic Algorithms
Cheng, Xueyi (Autor:in) / Che, Chang (Autor:in)
16.11.2024
ark:/40704/AJSM.v2n6a01
Academic Journal of Sociology and Management; Vol. 2 No. 6 (2024); 1-7 ; 3005-5059 ; 3005-5040
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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