Blog
About

  • Record: found
  • Abstract: not found
  • Article: not found

Investigation of Voronoi diagram based direction choices using uni- and bi-directional trajectory data

Read this article at

ScienceOpenPublisher
Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      Related collections

      Most cited references 26

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Social Force Model for Pedestrian Dynamics

      It is suggested that the motion of pedestrians can be described as if they would be subject to `social forces'. These `forces' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can be also applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: First, a term describing the acceleration towards the desired velocity of motion. Second, terms reflecting that a pedestrian keeps a certain distance to other pedestrians and borders. Third, a term modeling attractive effects. The resulting equations of motion are nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically.
        Bookmark
        • Record: found
        • Abstract: not found
        • Article: not found

        Voronoi diagrams---a survey of a fundamental geometric data structure

          Bookmark
          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          How simple rules determine pedestrian behavior and crowd disasters

          With the increasing size and frequency of mass events, the study of crowd disasters and the simulation of pedestrian flows have become important research areas. Yet, even successful modeling approaches such as those inspired by Newtonian force models are still not fully consistent with empirical observations and are sometimes hard to calibrate. Here, a novel cognitive science approach is proposed, which is based on behavioral heuristics. We suggest that, guided by visual information, namely the distance of obstructions in candidate lines of sight, pedestrians apply two simple cognitive procedures to adapt their walking speeds and directions. While simpler than previous approaches, this model predicts individual trajectories and collective patterns of motion in good quantitative agreement with a large variety of empirical and experimental data. This includes the emergence of self-organization phenomena, such as the spontaneous formation of unidirectional lanes or stop-and-go waves. Moreover, the combination of pedestrian heuristics with body collisions generates crowd turbulence at extreme densities-a phenomenon that has been observed during recent crowd disasters. By proposing an integrated treatment of simultaneous interactions between multiple individuals, our approach overcomes limitations of current physics-inspired pair interaction models. Understanding crowd dynamics through cognitive heuristics is therefore not only crucial for a better preparation of safe mass events. It also clears the way for a more realistic modeling of collective social behaviors, in particular of human crowds and biological swarms. Furthermore, our behavioral heuristics may serve to improve the navigation of autonomous robots.
            Bookmark

            Author and article information

            Journal
            PLEEE8
            Physical Review E
            Phys. Rev. E
            American Physical Society (APS)
            2470-0045
            2470-0053
            May 2018
            May 18 2018
            : 97
            : 5
            10.1103/PhysRevE.97.052127
            © 2018

            https://link.aps.org/licenses/aps-default-license

            Product

            Comments

            Comment on this article