Optimization of urban walkability indices and their correlation with pedestrian safety outcomes using multi-criteria decision-making techniques

Urban walkability has emerged as a critical component of sustainable urban development, directly influencing pedestrian safety, public health, and quality of life. This review examines the current state of research on optimizing urban walkability indices and their correlation with pedestrian safety outcomes through multi-criteria decision-making (MCDM) techniques. The analysis synthesizes diverse methodological approaches, key performance indicators, and optimization frameworks across various geographical contexts and urban settings. This comprehensive examination identifies significant advancements in integrated assessment methodologies that combine infrastructure quality, connectivity, safety perception, and environmental factors. Key findings indicate that hybrid MCDM approaches, particularly combinations of Analytic Hierarchy Process (AHP) with TOPSIS and fuzzy logic integration, yield robust predictive models for pedestrian safety outcomes with improved accuracy compared to single-criterion approaches. Advanced technologies including GIS-based spatial analysis, computer vision, and IoT sensors are transforming large-scale walkability assessment, enabling real-time optimization capabilities. However, significant gaps remain in standardization of measurement protocols, incorporation of dynamic environmental factors, and economic valuation methodologies. This review provides evidence-based recommendations for future research directions and practical implementation strategies for urban planners and policymakers seeking to optimize pedestrian environments for safety and sustainability.