Anna Charisse Farr

Wayfinding: A simple concept, a complex process

Wayfinding is the process of finding your way to a destination in a familiar or unfamiliar setting using any cues given by the environment. Due to its ubiquity in everyday life, wayfinding appears on the surface to be a simply characterized and understood process; however, this very ubiquity and the resulting need to refine and optimize wayfinding has led to a great number of studies that have revealed that it is in fact a deeply complex exercise. In this article, we examine the motivations for investigating wayfinding, with particular attention being paid to the unique challenges faced in transportation hubs, and discuss the associated principles and factors involved as they have been perceived from different research perspectives. We also review the approaches used to date in the modelling of wayfinding in various contexts. We attempt to draw together the different perspectives applied to wayfinding and postulate the importance of wayfinding and the need to understand this seemingly simple, but concurrently complex, process.

Investigating effective wayfinding in airports: a Bayesian network approach

Effective Wayfinding is the successful interplay of human and environmental factors resulting in a person successfully moving from their current position to a desired location in a timely manner. To date this process has not been modelled to reflect this interplay. This paper proposes a complex modelling system approach of wayfinding by using Bayesian Networks to model this process, and applies the model to airports. The model suggests that human factors have a greater impact on effective wayfinding in airports than environmental factors. The greatest influences on human factors are found to be the level of spatial anxiety experienced by travellers and their cognitive and spatial skills. The model also predicted that the navigation pathway that a traveller must traverse has a larger impact on the effectiveness of an airport’s environment in promoting effective wayfinding than the terminal design.

An Intuitive Dashboard for Bayesian Network Inference

Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.