Virtual sensing and virtual reality: How new technologies can boost research on crowd dynamics

Mehdi Moussaïd, Victor R. Schinazi, Mubbasir Kapadia, Tyler Thrash

Research output: Contribution to journalReview articlepeer-review

15 Scopus citations

Abstract

The collective behavior of human crowds often exhibits surprisingly regular patterns of movement. These patterns stem from social interactions between pedestrians such as when individuals imitate others, follow their neighbors, avoid collisions with other pedestrians, or push each other. While some of these patterns are beneficial and promote efficient collective motion, others can seriously disrupt the flow, ultimately leading to deadly crowd disasters. Understanding the dynamics of crowd movements can help urban planners manage crowd safety in dense urban areas and develop an understanding of dynamic social systems. However, the study of crowd behavior has been hindered by technical and methodological challenges. Laboratory experiments involving large crowds can be difficult to organize, and quantitative field data collected from surveillance cameras are difficult to evaluate. Nevertheless, crowd research has undergone important developments in the past few years that have led to numerous research opportunities. For example, the development of crowd monitoring based on the virtual signals emitted by pedestrians' smartphones has changed the way researchers collect and analyze live field data. In addition, the use of virtual reality, and multi-user platforms in particular, have paved the way for new types of experiments. In this review, we describe these methodological developments in detail and discuss how these novel technologies can be used to deepen our understanding of crowd behavior.

Original languageEnglish (US)
Article number82
JournalFrontiers Robotics AI
Volume5
Issue numberJUN
DOIs
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

Keywords

  • Collective movement
  • Complex systems
  • Pedestrians
  • Social interactions
  • Tracking
  • Virtual environment

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