Detecting Undeclared-Leader-Follower Structure in Pedestrian Evacuation Using Transfer Entropy

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations

Detail(s)

Original languageEnglish
Number of pages10
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Online published6 Apr 2022
Publication statusOnline published - 6 Apr 2022

Abstract

In evacuation models, individual interactions are typically assumed to follow the certain rules which are decided using prior knowledge. Our proposed improvement in evacuation modelling is inspired by the real-life experience that during crowd movements, individuals usually place their visual focus on the movements of their neighbours, even in the absence of social affiliations. That is, undeclared leader-follower (ULF) structures tend to emerge in crowd motion. In this study, to clarify the mechanism underlying individual interactions in crowd motion, a force-based model integrated with the ULF structure (LFM) was developed. An information-theoretic and model-free method, transfer entropy (TE), was applied to measure the ULF structure in evacuation crowds. Movement information (e.g., velocity and acceleration) was used as the time series for computing TE. The results showed that the LFM provides more realistic trajectories than does the classical social force model. In addition, the leader-follower structures were found to change over time, and an individual could act as a leader and a follower simultaneously during evacuation. ULF behaviour is strong at the early stage of evacuation and becomes weak when evacuees' differences in movement states diminish. Moreover, the density map of leaders' distributions presented a `V-like' formation. Recognising the leader-follower relationship is central to understanding the mechanisms underlying the emergence of collective behaviour. The proposed approach is expected to aid in simulating realistic local interactions among evacuees and in providing a data-driven perspective for understanding crowd evacuation.

Research Area(s)

  • causality, Evacuation, force-based model, leadership, transfer entropy