How the strength of social relationship affects pedestrian evacuation behavior: A multi-participant fire evacuation experiment in a virtual metro station

In fire evacuation, social groups of pedestrians often maintain proximity, proceeding at a similar pace towards a common destination. However, the effect of social groups on pedestrian evacuation behavior is underexplored due to the lack of quantification of the social relationships and the subsequent inadequate assessment of their influence on pedestrian dynamics during evacuation. To address these issues, an immersive virtual reality (VR)-based multi-participant evacuation experiment was conducted in a virtual metro station. Social groups of different relationship strengths measured by trust were asked to evacuate from a simulated metro station fire emergency scene. Results showed that grouped pedestrians with stronger social relationships had lower stress response to emergency situations, and tended to stay closer to each other during evacuation. In addition, stronger social relationships also led to more coordinated evacuation decisions between grouped pedestrians. In terms of evacuation performance, stronger social relationship sightly delayed pedestrians’ initial response but reduced their overall evacuation time. By quantitatively measuring the strength of social relationships and comprehensively revealing its influence on pedestrians who evacuate in social groups, this study is expected to enhance the understanding of social group dynamics in pedestrian evacuation, and offer significant insights for emergency management in indoor environments, such as transportation facilities, where high footfall and complex crowd patterns demand efficient evacuations to avert massive injuries.

• Record URL:
  • https://doi.org/10.1016/j.trc.2024.104805
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X24003267
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Xia, Xiaolu
  • Chen, Jieyu
  • Zhang, Jin
  • Li, Nan
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 104805
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 167
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Crowds; Emergency management; Evacuation; Pedestrians; Social psychology; Virtual reality
• Subject Areas: Pedestrians and Bicyclists; Planning and Forecasting; Security and Emergencies; Terminals and Facilities;

Filing Info

• Accession Number: 01930525
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 16 2024 9:00AM