Evaluating suitability of the least risk path algorithm to support cognitive wayfinding in indoor spaces: An empirical study

Over the last couple of years, applications that support wayfinding in indoor spaces have become a booming industry. Finding one's way in complex 3D indoor environments can still be a challenging endeavor, partly induced by the specific indoor structure (e.g. fragmentation, less visibility, confined areas). Appropriate algorithms that help guide unfamiliar users by providing 'easier to follow' route instructions are so far mostly absent indoors. In outdoor space, several alternative algorithms exist, adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behavior (e.g. simplest paths, least risk paths). The aim of this research is to extend those richer cognitive algorithms to three-dimensional indoor environments. More specifically, the focal point of this paper is the application of the least risk path algorithm, i.e. an algorithm developed to minimize the risk of getting lost, to an indoor space. This algorithm is duplicated and extensively tested in a complex multi-story building by comparing the quality of the calculated least risk paths with their shortest path alternatives. The outcome of those tests reveals non-stable results in terms of selecting the least risky edges in indoor environments, which leads to the conclusion that the algorithm has to be adjusted to the specificities of indoor space. Several improvements for the algorithm are proposed and will be implemented as part of future work to improve the overall user experience during navigation in indoor environments.