Can high-density human collective motion be forecasted by spatiotemporal fluctuations?

TL;DR

This study demonstrates that emergent collective human crowd motions, such as density waves, can be forecasted directly from video data using model-free methods, aiding real-time crowd safety assessments.

Contribution

It introduces a model-free approach utilizing mode analysis and physics concepts to predict collective crowd behavior from video footage, with a 1-second forecasting window.

Findings

Successfully forecasted density wave patterns from crowd video data.

Identified temporal precursors to collective motion events.

Achieved 1-second prediction lead time for emergent crowd phenomena.

Abstract

Concerts, protests, and sporting events are occurring with increasing frequency and magnitude. The extreme physical conditions common to these events are known to cause injuries and loss-of-life due to the emergence of collective motion such as crowd crush, turbulence, and density waves. Mathematical models of human crowds aimed at enhancing crowd safety by understanding these phenomena are developed with input from a variety of disciplines. However, model validation is challenged by a lack of high-quality empirical data and ethical constraints surrounding human crowd research. Consequently, generalized model-based approach for real-time monitoring/risk-assessment of crowd collective motion remains an open problem. Here, we take a model-free approach to crowd analysis and show that emergent collective motion can be forecasted directly from video data. We use mode analysis methods from material science and concepts from non-equilibrium physics to study footage of a human crowd at an Oasis rock concert. We analyze the attendees positional fluctuations during a period of crowd turbulence to predict the spatial patterns of an emergent human density wave. In addition to predicting spatial patterns of collective motion, we also identify and measure temporal patterns that precede the density wave and forecast its appearance by 1~s. Looking ahead, widening this forecasting window beyond 1~s will enable new computer vision technologies for real-time risk-assessment of emergent human collective motion.