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Open AccessJournal ArticleDOI

Social Force Model for Pedestrian Dynamics

Dirk Helbing, +1 more
- 01 May 1995 - 
- Vol. 51, Iss: 5, pp 4282-4286
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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.

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Citations
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Modeling crowd evacuation of a building based on seven methodological approaches

TL;DR: It is concluded that a variety of different kinds of approaches should be combined to study crowd evacuation, and psychological and physiological elements affecting individual and collective behaviors should be also incorporated into the evacuation models.
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Human motion trajectory prediction: a survey:

TL;DR: In this article, the ability of intelligent autonomous systems to perceive, understand, and anticipate human behavior becomes increasingly important in a growing number of intelligent systems in human environments, and the ability to do so is discussed.
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The fundamental diagram of pedestrian movement revisited

TL;DR: In this paper, a linear relation between the velocity and the inverse of the density of a single-file movement along a line has been analyzed, which can be regarded as the required length for one pedestrian to move.
Proceedings ArticleDOI

Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies

TL;DR: In this article, a structure of Recurrent Neural Networks (RNNs) is proposed to jointly reason on multiple cues over a temporal window, which allows to correct data association errors and recover observations from occluded states.
References
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Book

Field theory in social science

Kurt Lewin
Book

Kinetic theory of vehicular traffic

TL;DR: A theory of multi-LANE traffic flow and the space-time evolution of thevelocity distribution of cars are examined to help understand the role of driver behaviour and strategy in this network.
Journal ArticleDOI

Improved fluid-dynamic model for vehicular traffic.

TL;DR: The fluid-dynamic traffic model of Kerner and Konh\"auser is extended by an equation for the vehicles' velocity variance, able to describe the observed increase of velocity variance immediately before a traffic jam develops.
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