Universal power law governing pedestrian interactions.

By Ioannis Karamouzas, B. Skinner, Stephen J. Guy. Physical Review Letters, 2014

344 citations recorded by the source corpus.

Human crowds often bear a striking resemblance to interacting particle systems, and this has prompted many researchers to describe pedestrian dynamics in terms of interaction forces and potential energies. The correct quantitative form of this interaction, however, has remained an open question. Here, we introduce a novel statistical-mechanical approach to directly measure the interaction energy between pedestrians. This analysis, when applied to a large collection of human motion data, reveals a simple power-law interaction that is based not on the physical separation between pedestrians but on their projected time to a potential future collision, and is therefore fundamentally anticipatory in nature. Remarkably, this simple law is able to describe human interactions across a wide variety of situations, speeds, and densities. We further show, through simulations, that the interaction law we identify is sufficient to reproduce many known crowd phenomena.

Subfields: Artificial Intelligence, Ocean Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/be2b627cdc9497af1648407a53520fa896e2c138

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Citations in the corpus

Overview of the PALM model system 6.0 (2019, methodology): “Concepts from the original model formulation (Helbing and Molnár, 1995) as well as from its extension for close-range collision prediction and avoidance using a power-law approach (Karamouzas et al., 2014) have been adopted for the formulation of the MAS.”
Social Attention: Modeling Attention in Human Crowds (2017, background): “This observation leads us to the insight that human-human interactions in crowd are not just dependent on relative distance, but also on other features such as velocity, time-to-collision [13], acceleration and heading.”
Core Challenges of Social Robot Navigation: A Survey (2021, unknown): “The literature proposes several techniques to model the crowd’s dynamics, where pedestrian simulation engines are drawn from work in crowd analysis dynamics [103] or computer graphics [59, 123, 261].”
SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction (2018, methodology): “Another behaviour factor that has been used in the trajectory research literature is the time-to-collision [11] factor which describes the time duration before two pedestrians collide if they continue at their current walking velocities.”