Exploring the Use of Eye‐Tracking and Virtual Reality to Optimize the Navigational Capabilities of Subway Platform Signage: An Experimental Study on Pedestrians' Perceived Satisfaction

ABSTRACT Signage is critical for subway platform navigation, but there has been limited validation research on signage navigational capabilities. This study involves two experiments based on user research study. The first experiment involves 42 participants exploring design factors that influence the navigational satisfaction of signage and identifying problematic signage, using eye‐tracking and virtual reality technology. The second, involving 30 participants, evaluates how optimization solutions improve such signage navigational satisfaction. At the same time, the entropy‐weighted TOPSIS method verified findings. Results showed significant linear relationships between signage satisfaction and eye‐tracking metrics (AFD, FFD, FC; p < 0.05) and design elements (size, content, text size, text color, position, angle; p < 0.01). Multiple linear regression models had high goodness of fit ( R 2 = 0.948 and adjusted R 2 = 0.944 in the first experiment; R 2 = 0.899 and adjusted R 2 = 0.888 in the second experiment). Optimization increased AFD, FC, FFD by 3–5 times, while the optimized model retains high explanatory power ( R 2 = 0.888). The optimal solution selected by the entropy‐weighted TOPSIS method is highly consistent with experimental results, confirming optimized schemes enhanced navigational satisfaction. This study provides practical pathways and data support for quantitative evaluation and optimization of subway signage.