State-space models reveal bursty movement behaviour of dance event visitors

EPJ Data Science

Sander Klous¹  Philip Rutten¹  Michael H. Lees¹  Peter M. A. Sloot² 

[1] Informatics Institute, University of Amsterdam, Science Park 904, 1098XH, Amsterdam, The Netherlands;Informatics Institute, University of Amsterdam, Science Park 904, 1098XH, Amsterdam, The Netherlands;Institute for Advanced Study, 1012 GC, Amsterdam, The Netherlands;

关键词: Human mobility;    Wi-Fi data;    State-space models;    Movement ecology;    Bursty dynamics;   

DOI  :  10.1140/epjds/s13688-021-00292-9

来源: Springer

【 摘 要 】

Pedestrian movements during large crowded events naturally consist of different modes of movement behaviour. Despite its importance for understanding crowd dynamics, intermittent movement behaviour is an aspect missing in the existing crowd behaviour literature. Here we analyse movement data generated from nearly 600 Wi-Fi sensors during large entertainment events in the Johan Cruijff ArenA football stadium in Amsterdam. We use the state-space modeling framework to investigate intermittent motion patterns. Movement models from the field of movement ecology are used to analyse individual pedestrian movement. Joint estimation of multiple movement tracks allows us to investigate statistical properties of measured movement metrics. We show that behavioural switching is not independent of external events, and the probability of being in one of the behavioural states changes over time. In addition, we show that the distribution of waiting times deviates from the exponential and is best fit by a heavy-tailed distribution. The heavy-tailed waiting times are indicative of bursty movement dynamics, which are here for the first time shown to characterise pedestrian movements in dense crowds. Bursty crowd behaviour has important implications for various diffusion-related processes, such as the spreading of infectious diseases.

【 授权许可】

CC BY