Optimizing urban walkability with NSGA-III for sustainable city planning and construction

Urban walkability is essential for sustainable city planning and construction, fostering public health, environmental benefits, and social equity. However, optimizing walkability involves balancing multiple, often conflicting objectives, such as accessibility, safety, environmental quality, and social inclusivity. This paper presents a novel approach to optimizing urban walkability using the Non-dominated Sorting Genetic Algorithm III (NSGA-III). By applying NSGA-III, we address the complexities of multi-objective optimization in urban environments, generating a set of Pareto-optimal solutions that cater to diverse planning priorities. A case study in a mid-sized urban area demonstrates the effectiveness of the proposed methodology. The results highlight key trade-offs between objectives, such as the balance between accessibility and safety or environmental quality and social inclusivity. The findings provide urban planners with a robust decision-making framework that supports the creation of walkable, sustainable cities. The study concludes with policy recommendations to enhance urban walkability and suggests avenues for future research, including the integration of economic considerations and the application of this approach in larger, more complex urban settings. This research contributes to the field of urban planning by offering a comprehensive tool for optimizing walkability, ultimately promoting more livable and sustainable cities.