Information Theoretic Model to Simulate Agent-Signage Interaction for Wayfinding

Abstract

Signage systems are critical for communicating spatial information during wayfinding among a plethora of noise in the environment. A proper signage system can improve wayfinding performance and user experience by reducing the perceived complexity of the environment. However, previous models of sign-based wayfinding do not incorporate realistic noise or quantify the reduction in perceived complexity from the use of signage. Drawing upon concepts from information theory, we propose and validate a new agent-signage interaction model that quantifies available wayfinding information from signs for wayfinding. We conducted two online crowd-sourcing experiments to compute the distribution of a sign's visibility and an agent's decision-making confidence as a function of observation angle and viewing distance. We then validated this model using a virtual reality (VR) experiment with trajectories from human participants. The crowd-sourcing experiments provided a distribution of decision-making entropy (conditioned on visibility) that can be applied to any sign/environment. From the VR experiment, a training dataset of 30 trajectories was used to refine our model, and the remaining test dataset of 10 trajectories was compared with agent behavior using dynamic time warping (DTW) distance. The results revealed a reduction of 38.76% in DTW distance between the average trajectories before and after refinement. Our refined agentsignage interaction model provides realistic predictions of human wayfinding behavior using signs. These findings represent a first step towards modeling human wayfinding behavior in complex real environments in a manner that can incorporate several additional random variables (e.g., environment layout). Keywords Cognitive agent • Information theory • Entropy • Artificial intelligence • Agent-based model • Signage system Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A Sustainable Evaluation Method for a Tourism Public Wayfinding System: A Case Study of Shanghai Disneyland Resort

“…We evaluated the contents of three types of signs in the park according to the opinions expressed which can be seen in Figure 1 by subjects in the interviews and expert surveys. To assess the layout of the signs, we calculated the values of six indicators according to the horizontal and vertical coordinates of signs representing maps of the park [26]. The calculation process was as follows: (1) investigations of signs in the map; (2) calculation of indicators according to the proposed formulae according to the data provided by the park management personnel, thus we can get values of scenic spot popularity (c j ) and right of way (w k ).…”

The Optimized Social Distance Lab

“…The introduction of pedestrian flow for multi-objective optimization presented by Huang et al [10] provides insight into the potential of agent-based modelling on wayfinding cognition. Recent research by Dubey et al [11] proposes a new system for the automated positioning of signage based on a multi-criteria optimization approach; referencing theories of Space Syntax and behavioral and cognitive science. Yet, as a consequence of the rapid onset of the pandemic, none have investigated automated optimization of layouts in the setting of social distancing restrictions.…”

Designing mobile spatial navigation systems from the user's perspective: An interdisciplinary review

“…Users' varying cognitive abilities and constraints suggest a ninth and final design principle: that navigation systems should strive to minimize users' cognitive load when possible, and at the least avoid cognitive overload. Recent work has developed simulation models for non-digital navigation systems, such as signage and architectural design, in order to create easy to navigate environments with accessible signage (Dubey et al, 2019;Gath-Morad et al, 2020). For individuals with intellectual and cognitive disabilities, augmented reality has shown better spatial learning and wayfinding outcomes compared to Google Maps and paper maps, but there is a dearth of research in this domain and future work and application development is needed (Gomez et al, 2015;McMahon et al, 2015;Smith et al, 2017).…”