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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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Journal ArticleDOI

Extraction and quantitative analysis of microscopic evacuation characteristics based on digital image processing

TL;DR: In this article, the microscopic characteristics of pedestrian dynamics, including velocity, density, and lateral oscillation, as well as their interrelations, were obtained and analyzed using a digital image processing method based on a mean shift algorithm.
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What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction

TL;DR: This work shows how neural networks for pedestrian motion prediction can be thoroughly evaluated and which research directions for neural motion prediction are promising in future and clarifies false assumptions about the problem itself.
Proceedings ArticleDOI

Robot navigation in dense human crowds: the case for cooperation

TL;DR: A cooperation model is important for safe and efficient robot navigation in dense human crowds by developing a probabilistic predictive model of cooperative collision avoidance and goal-oriented behavior by extending the interacting Gaussian processes approach to include multiple goals and stochastic movement duration.
Journal ArticleDOI

Path Data in Marketing: An Integrative Framework and Prospectus for Model-Building

TL;DR: A formal definition of a path is proposed, a unifying framework is derived that allows us to classify different kinds of paths, and a range of important operational issues that should be taken into account as researchers begin to build formal models of path-related phenomena are covered.
Journal ArticleDOI

Time-Evolving Measures and Macroscopic Modeling of Pedestrian Flow

TL;DR: In this paper, a new model of pedestrian flow, formulated within a measure-theoretic framework, is introduced, which consists of a macroscopic representation of the system via a family of measures which, pushed forward by some flow maps, provide an estimate of the space occupancy by pedestrians at successive times.
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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