Wayfinding Signage and Walking Perception in Urban Environments: A Human Factors and Ergonomics Framework Based on a Systematic Review

Abstract

In rapidly transforming cities, the quality of pedestrian environments has become central to sustainable and equitable mobility. Wayfinding signage, as a highly adaptable and comparatively low-cost intervention, plays a critical yet frequently undervalued role in shaping how people interpret, traverse, and ultimately experience urban space. This review aims, through a Human Factors and Ergonomics perspective, to clarify the mechanisms through which wayfinding signage influences pedestrian walking perception. A PRISMA-based systematic review identified 32 empirical studies published between 2010 and 2024. Using a structured coding strategy, signage characteristics were synthesized into recurrent human-centered mechanisms and the perceptual outcomes they influence. The evidence portrays a field that is methodologically sophisticated but contextually narrow: research clusters around hospitals, transit hubs, and other controlled interiors, with limited attention to open streets, culturally diverse users, or groups with heightened vulnerability. Across heterogeneous methods, four HFE pathways consistently emerge—cognitive load, situation awareness, decision-making, and emotional engagement—jointly shaping four stable dimensions of walking perception: continuity, connectivity, accessibility, and environmental attractiveness. These cross-study regularities point to three actionable directions for design practice: strengthening perceptual clarity to reduce cognitive burden, integrating cultural and experiential meaning to enrich environmental interpretation, and building inclusive information structures that expand accessibility for diverse pedestrian groups. By consolidating dispersed findings into a coherent HFE-informed framework, this review reframes wayfinding signage as an active interface embedded within the pedestrian experience, offering a conceptual foundation for future causal modeling and evidence-driven urban design.