arXiv2018
The possibility to understand and to quantitatively model the physics of the interactions between pedestrians walking in crowds has compelling relevant applications, e.g. related to the design and safety of civil infrastructures. In this work we study pedestrian-pedestrian interactions from observational experimental data in diluted crowds. While in motion, pedestrians adapt their walking paths trying to preserve mutual comfort distances and to avoid collisions. In mathematical models this behavior is typically modeled via "social" interaction forces. Leveraging on a high-quality, high-statistics dataset - composed of few millions of real-life trajectories acquired from state-of-the-art observational experiments - we develop a quantitative model capable of addressing interactions in the case of binary collision avoidance. We model interactions in terms of both long- and short-range forces, which we superimpose to our Langevin model for non-interacting pedestrian motion [Corbetta et al. Phys.Rev.E 95, 032316, 2017]. The new model that we propose here features a Langevin dynamics with "fast" random velocity fluctuations that are superimposed to the "slow" dynamics of a hidden model variable: the "intended" walking path. The model is capable of reproducing relevant statistics of the collision avoidance motion, such as the statistics of the side displacement and of the passing speed. Rare occurrences of bumping events are also recovered. Furthermore, comparing with large datasets of real-life tracks involves an additional challenge so far neglected: identifying, within a database containing very heterogeneous conditions, only the relevant events corresponding to binary avoidance interactions. To tackle this challenge, we propose a novel approach based on a graph representation of pedestrian trajectories, which allows us to operate complexity reduction for efficient data selection.