Tracking and analyzing the movement trajectories of individuals and groups is an important problem with applications in crowd management and the development of transportation systems. However, real-world tracking is limited due to the size of the trackable area and the precision with which a person can be tracked. Experiments in virtual environments have many advantages, including practically unlimited sizes and the precise measurement of spatial behavior. However, the generalizability of research using virtual environments to real-world scenarios is often limited by the translation of participants’ movements to those of their avatars. We compared human movement patterns in virtual environments with different control interfaces: a handheld joystick, a mouse-and-keyboard setup, and a keyboard-only setup. With each of these controls, participants completed several movement-related tasks of varying difficulty in a limited amount of time. Questionnaires indicated that participants preferred the mouse-and-keyboard setup over the other two setups. Standard performance measures suggested that the joystick underperformed in a variety of tasks. Movement trajectories in the final task indicated that each of the control setups produced somewhat realistic behavior, despite some apparent differences from real-world trajectories. Overall, the results indicated that, given limited resources, mouse-and-keyboard setups consistently outperform joysticks and produce realistic movement patterns.

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 agent-signage 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).