Age-Inclusive Dynamic Wayfinding Design for Large Transportation Hubs: An Experimental Study of Information Volume and Scrolling Cycle

The proliferation of Variable Message Signs (VMS) in large transportation hubs is driven by their core capability to flexibly address complex and changing conditions in vehicular and pedestrian traffic. This capability allows for real-time guidance that alleviates congestion and optimizes the use of space. The complexity and rapid dynamics of VMS information can exceed older adults’ cognitive processing capacities, hindering their ability to rapidly locate critical information and comprehend travel instructions. These difficulties subsequently lead to inefficient wayfinding, increased decision-making errors, and heightened frustration. To address this issue, this study examines the effects of two key dynamic design parameters—information volume and scrolling cycle—on wayfinding efficiency and attention allocation across different age groups. Grounded in theories of visual perception and information entropy, a controlled eye-tracking experiment was conducted that combined behavioral performance measures with gaze data. The findings reveal an effective balance between information volume and scrolling cycle. This specific combination helps reduce performance differences across age groups: it maintains high interaction efficiency for younger users while significantly enhancing information acquisition for older adults. Furthermore, it mitigated the over-capture of attention by dynamic information. Based on these empirical results, we propose a design strategy for VMS that integrates dynamic information parameters with static interface principles. This strategy applies inclusive human-computer interaction (HCI) principles to the domain of dynamic public interfaces. Moving beyond foundational universal design elements (e.g., typography, color contrast), it positions empirically optimized dynamic information quantification and presentation rhythm as core design dimensions. This research provides empirical evidence and practical design guidance for optimizing intelligent wayfinding systems in complex public environments, contributing to more inclusive and effective human-computer interactions.