Physics-based modeling and data representation of pedestrian pairwise interactions

Abstract

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.

arXiv comment17 figures, 18 pages

References in corpus (11)

• Social Force Model for Pedestrian Dynamics
• Interaction Ruling Animal Collective Behaviour Depends on Topological rather than Metric Distance: Evidence from a Field Study
• How simple rules determine pedestrian behavior and crowd disasters
• Active Brownian Particles. From Individual to Collective Stochastic Dynamics
• The walking behaviour of pedestrian social groups and its impact on crowd dynamics
• Experimental study of the behavioural mechanisms underlying self-organization in human crowds
• Experimental study of pedestrian counterflow in a corridor
• Parameter Estimation of Social Forces in Crowd Dynamics Models via a Probabilistic Method
• Fluctuations around mean walking behaviours in diluted pedestrian flows
• High statistics measurements of pedestrian dynamics
• Experimental studies of pedestrian flows under different boundary conditions

Cited by in corpus (20)

• Monitoring physical distancing for crowd management: real-time trajectory and group analysis
• Managing Crowded Museums: Visitors Flow Measurement, Analysis, Modeling, and Optimization
• Experimental study on the evading behavior of individual pedestrians when confronting with an obstacle in a corridor
• Pedestrian orientation dynamics from high-fidelity measurements
• High-statistics pedestrian dynamics on stairways and their probabilistic fundamental diagrams
• Fluctuations in pedestrian dynamics routing choices
• Social aspects of collision avoidance: A detailed analysis of two-person groups and individual pedestrians
• Body and mind: Decoding the dynamics of pedestrians and the effect of smartphone distraction by coupling mechanical and decisional processes
• Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control
• Micro- and Macroscopic Modeling of Crowding and Pushing in Corridors
• Data-driven physics-based modeling of pedestrian dynamics
• High-statistics modeling of complex pedestrian avoidance scenarios
• What are Social Norms for Low-speed Autonomous Vehicle Navigation in Crowded Environments? An Online Survey
• Understanding complex crowd dynamics with generative neural simulators
• Social Phases in Human Movement
• Stochastic fluctuations of diluted pedestrian dynamics along curved paths
• Quantifying spatio-temporal patterns in classical and quantum systems out of equilibrium
• Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios
• Pedestrian fluxes in confined geometric networks: entropic measures and robustness of accessibility in a university campus
• Ordering leads to multiple fast tracks in simulated collective escape of human crowds