Social Force Model for Pedestrian Dynamics
Dirk Helbing,Péter Molnár +1 more
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TLDR
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.Abstract:
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 also be 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 from other pedestrians and borders; and third, a term modeling attractive effects. The resulting equations of motion of 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.read more
Citations
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Proceedings ArticleDOI
Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker
TL;DR: This paper presents an approach which includes the interaction between pedestrians in two ways: first, considering social and grouping behavior, and second, using a global optimization scheme to solve the data association problem.
Proceedings ArticleDOI
Feature-Based Prediction of Trajectories for Socially Compliant Navigation
TL;DR: This paper presents a novel approach to predict the movements of pedestrians that applies a maximum entropy learning method based on features that capture relevant aspects of the trajectories to determine the probability distribution that underlies human navigation behavior.
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Crowd Simulation
TL;DR: The second edition of Crowd Simulation includes in-depth discussions on the techniques of path planning, including a new hybrid approach between navigation graphs and potential-based methods and a free-of-collision method for crowds.
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A dynamic cellular automaton model for evacuation process with obstacles
TL;DR: A dynamic cellular automaton (CA) model is proposed to simulate the evacuation process in the rooms with obstacles, and effects of pedestrians distribution, doors position and doors width on the evacuation time are discussed.
Journal ArticleDOI
A modification of the Social Force Model can reproduce experimental data of pedestrian flows in normal conditions
TL;DR: The Social Force Model presents some limitations when describing the experimental data of pedestrian flows in normal conditions — in particular the specific flow rates for different door widths and the fundamental diagram.
References
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