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

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