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Physics-based modeling and data representation of pairwise interactions among pedestrians

Alessandro Corbetta, Jasper A. Meeusen, Chung-min Lee, Roberto Benzi, and Federico Toschi
Phys. Rev. E 98, 062310 – Published 14 December 2018
Physics logo See Synopsis: How Walkers Avoid Collisions

Abstract

In this work we study pedestrian-pedestrian interactions from observational experimental data in diluted pedestrian crowds. While in motion, pedestrians continuously adapt their walking paths trying to preserve mutual comfort distances and to avoid collisions. Leveraging on a high-quality, high-statistics data set, composed of several few millions real-life trajectories acquired from state-of-the-art observational experiments (about 6 months of high-resolution pedestrian tracks acquired in a train station), we develop a quantitative model capable of addressing interactions in the case of binary collision avoidance. We model interactions in terms of both long-range (sight based) and short-range (hard-contact avoidance) forces, which we superimpose on our Langevin model for noninteracting pedestrian motion [Corbetta et al., Phys. Rev. E 95, 032316 (2017)] (here further tested and extended). The model that we propose here features a Langevin dynamics with fast random velocity fluctuations that are superimposed on the slow dynamics of a hidden model variable: the intended walking path. In the case of interactions, social forces may act both on the intended path and on the actual walked path. The model is capable of reproducing quantitatively relevant statistics of the collision avoidance motion, such as the statistics of the side displacement and of the passing speed. Rare occurrences of actual bumping events are also recovered. Furthermore, comparing with large data sets of real-life tracks involves an additional computational challenge so far neglected: identifying automatically, within a database containing very heterogeneous conditions, only the relevant events corresponding to binary avoidance interactions. In order to tackle this challenge, we propose a general approach based on a graph representation of pedestrian trajectories, which allows us to effectively operate complexity reduction for efficient data classification and selection.

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  • Received 7 August 2018

DOI:https://doi.org/10.1103/PhysRevE.98.062310

©2018 American Physical Society

Physics Subject Headings (PhySH)

Interdisciplinary Physics

Synopsis

Key Image

How Walkers Avoid Collisions

Published 14 December 2018

Observations of large numbers of pedestrians in two new studies offer insights into how humans avoid bumping into each other.  

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Authors & Affiliations

Alessandro Corbetta1, Jasper A. Meeusen1, Chung-min Lee2, Roberto Benzi3, and Federico Toschi4

  • 1Department of Applied Physics, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
  • 2Department of Mathematics and Statistics, California State University, Long Beach, Long Beach, California 90840, USA
  • 3Department of Physics and INFN, University of Tor Vergata, I-00133 Rome, Italy
  • 4Department of Applied Physics and Department of Mathematics and Computer Science, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands and CNR-IAC, I-00185 Rome, Italy

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Issue

Vol. 98, Iss. 6 — December 2018

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