High statistics measurements of pedestrian dynamics
arXiv:1407.1254 · doi:10.1016/j.trpro.2014.09.013
Abstract
Understanding the complex behavior of pedestrians walking in crowds is a challenge for both science and technology. In particular, obtaining reliable models for crowd dynamics, capable of exhibiting qualitatively and quantitatively the observed emergent features of pedestrian flows, may have a remarkable impact for matters as security, comfort and structural serviceability. Aiming at a quantitative understanding of basic aspects of pedestrian dynamics, extensive and high-accuracy measurements of pedestrian trajectories have been performed. More than 100.000 real-life, time-resolved trajectories of people walking along a trafficked corridor in a building of the Eindhoven University of Technology, The Netherlands, have been recorded. A measurement strategy based on Microsoft Kinect\texttrademark has been used; the trajectories of pedestrians have been analyzed as ensemble data. The main result consists of a statistical descriptions of pedestrian characteristic kinematic quantities such as positions and fundamental diagrams, possibly conditioned to local crowding status (e.g., one or more pedestrian(s) walking, presence of co-flows and counter-flows).
14 pages, 9 figures
Cited by in corpus (13)
- Monitoring physical distancing for crowd management: real-time trajectory and group analysis
- Intrinsic group behaviour: dependence of pedestrian dyad dynamics on principal social and personal features
- Physics-based modeling and data representation of pedestrian pairwise interactions
- Social Force Model parameter testing and optimization using a high stress real-life situation
- Fluctuations around mean walking behaviours in diluted pedestrian flows
- High-statistics pedestrian dynamics on stairways and their probabilistic fundamental diagrams
- Fluctuations in pedestrian dynamics routing choices
- Identification of social groups and waiting pedestrians at railway platforms using trajectory data
- Data-driven physics-based modeling of pedestrian dynamics
- High-statistics modeling of complex pedestrian avoidance scenarios
- Comparing first order microscopic and macroscopic crowd models for an increasing number of massive agents
- Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields
- Avalanches of choice: how stranger-to-stranger interactions shape crowd dynamics