Wayfinding behaviour in a multi-level building: A comparative study of HMD VR and Desktop VR

Yan Feng,Dorine C Duives,Serge P Hoogendoorn

doi:10.1016/j.aei.2021.101475

Yan Feng, Dorine C Duives + Show 1 more

Open Access

https://doi.org/10.1016/j.aei.2021.101475

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Abstract

Virtual Reality (VR) provides the possibility to study pedestrian wayfinding behaviour in multi-level buildings. Although VR has been applied increasingly to study pedestrian behaviour, it has remained unclear how different VR technology would affect behavioural outcomes in a multi-level building. The study compares the adoption of different VR technologies for pedestrian wayfinding studies, via investigating the difference in pedestrian wayfinding behaviour and user experience. Wayfinding experiments with two groups of participants were conducted using either HMD VR or Desktop VR. Pedestrian movement trajectory data were collected via the VR system and user experience data were recorded via a questionnaire. These data allow for direct comparison and detailed analysis of pedestrian behaviour and user experience between the adoption of two VR technologies. The results showed that technological differences have a significant impact on wayfinding task performance and head rotation change. However, the route choice, exit choice and user experience were overall similar between the two groups. These results provide empirical evidence supporting researchers to choose between immersive and non-immersive VR when study pedestrian wayfinding behaviour.

Highlights

Pedestrians perform wayfinding activities in buildings on a daily basis
This study examined the difference in participants’ wayfinding behaviour and user experience as a result of the adoption of HMD Virtual Reality (VR) and Desktop VR
This section presents an analysis of pedestrian behavioural data collected during the VR experiment, namely (1) pedestrian route and evacuation exit choice, (2) observation behaviour, and (3) wayfinding task performance

Summary

Introduction

Pedestrians perform wayfinding activities in buildings on a daily basis. Wayfinding here is defined as a decision-making process in which pedestrians determine the route to a destination and finding an exit to leave the building [1]. Field experiments and surveys have been widely used to investigate pedestrian wayfinding behaviour under both normal and emergency situations (e.g., [6,7,8,9,10,11]) These methods have constraints in terms of experimental control, cost, and data accuracy for studying pedestrian wayfinding behaviour [12]. The accuracy of behavioural data is highly influenced by the sensor setup and techniques, and it often requires a large investment in labour to extract the collected data [12] It is time-consuming and challenging to obtain approval to perform a field observation or create an artificial experiment environ ment.

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