Design mechanisms of airport visual guidance systems on passenger wayfinding performance: evidence from causal machine learning and a moderated mediation approach

Abstract

In airports, effective visual guidance systems are essential for supporting passenger wayfinding under cognitive load and time constraints. However, many existing signage designs lack user-centered optimization, leading to inefficient navigation and decision errors. This study investigates how signage information density and coding format influence passenger cognitive load and decision confidence, with time pressure as a moderator. A two-phase approach was adopted: (1) causal machine learning identified key design factors from real-world airport signage; (2) a laboratory eye-tracking experiment with 60 participants employed a 3 (Information Density: Low/Medium/High) × 2 (Coding Format: Text vs. Text + Graphic) × 2 (Time Pressure: Low/High) mixed design. Visual search tasks measured gaze behavior, accuracy, and subjective ratings. Results show that high information density and text-only formats increased cognitive load and reduced decision confidence, while text + graphic formats improved performance–especially under high time pressure–by lowering visual effort. Mediation analysis confirmed cognitive load as the key mechanism, with time pressure moderating both direct and indirect effects. Findings provide an S–O–R-based cognitive mechanism model and practical guidelines for designing airport signage to enhance wayfinding efficiency in high-density public environments.