Assessing Pedestrian Road Crossing Behavior and Risks Using Multicriteria Decision-Making Models: Implications for Sustainable and Resilient Cities

Abstract

Rapid urbanization, coupled with inadequate pedestrian infrastructure, has contributed to an increase in pedestrian fatalities within major Bangladeshi cities. This study employs analytical hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), and simple additive weighting (SAW) models to evaluate pedestrian road crossing behaviors and associated crash risk factors in Khulna City. Findings reveal group crossing (R_i = 0.16), mixed and perpendicular crossing patterns, and mobile-phone usage pose the highest risk at zebra crossings, while young pedestrians (A_i = 0.87), hand gestures (R_i = 0.00), and running are identified as the most significant risks at nonzebra crossings. The multimodel approach provides comprehensive insights. Specifically, TOPSIS identifies group crossing and hand gestures as the riskiest behaviors at respective intersection types and SAW highlighting perpendicular crossing (A_i = 0.88) and young pedestrians as particularly vulnerable. These findings offer evidence-based guidance for the development of safer and more sustainable urban environments, aligning with the journal’s focus on resilient urban environments, smart transportation, and social aspects. Urban planners can utilize these findings to enhance pedestrian infrastructure, promote clean mobility, and foster livable communities. By quantifying risk factors and implementing decision support systems, this research advances the design of sustainable, socially resilient cities and contributes to creating healthy, equitable urban societies.