Abstract
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.
Highlights
Modeling the dynamics of pedestrians in large crowds is important for a number of reasons, from understanding how dangerous situations arise from individual behaviours [1,2,3,4], to predicting the spread of epidemic diseases [5,6,7]
We see that in both cases the average moving radius of gyration (MRG) clearly decrease during the events, and peak afterwards, indicating people collectively leaving the stadium after the events
The fitting results of both the conditionally Gaussian linear statespace model (CGLSSM) and the DCRWS suggest the movement tracks can be appropriately described as correlated random walks consisting of two discrete states
Summary
Modeling the dynamics of pedestrians in large crowds is important for a number of reasons, from understanding how dangerous situations arise from individual behaviours [1,2,3,4], to predicting the spread of epidemic diseases [5,6,7]. People stay in one place for some time, and decide to change location, usually in one continuous movement bout. This kind of intermittent movement behaviour typically occurs during large crowded events that span long time periods [11]. Despite the fact that crowd management is most critical for these events, intermittent movement behaviour is an aspect that is still missing in the ex-
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