Generating trajectory data without behavior modeling: An online virtual reality method in wayfinding performance evaluation for buildings

Abstract

Wayfinding performance is one of the key issues in pre- or post-occupancy evaluation for large-scale public buildings and such evaluation typically revolves around participants’ wayfinding trajectories. Traditional evaluation methods, including laboratory virtual reality (VR) experiments and computer simulations, have their respective advantages in accuracy or cost-efficiency. However, they either suffer from high costs and low efficiency or sacrifice the diversity of behavioral data for higher efficiency and lower costs. Therefore, this paper proposes a novel method named “sampling as simulation” (SaS) via an online VR wayfinding experiment. This method allows for the rapid collection of behavioral data at a lower cost to generate highly accurate wayfinding trajectory data for evaluation purposes. It utilizes a model-image integrated approach to create realistic online virtual environments and employs a spatiotemporal replay algorithm to generate a crowd of true-trajectory-driven avatars, significantly enhancing the immersion and realism of the online virtual scenes. This method was demonstrated and validated with a wayfinding performance post-occupancy evaluation (POE) project at the International Departure of Satellite Terminal One (S1) in Shanghai Pudong International Airport (PVG). In this example, a total of 13,712 original trajectories were collected within a 72-h time window, serving as the original dataset for evaluation. Based on this dataset, the study effectively simulated wayfinding behavior in three different scenarios and evaluated wayfinding performance. Compared to existing methods, the SaS method not only improves data collection efficiency and reduces costs but also maintains the diversity of behavior by generating transformed trajectories directly from human data without behavior modeling.