scispace - formally typeset
Open AccessJournal ArticleDOI

Social Force Model for Pedestrian Dynamics

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

Crowdbrush: interactive authoring of real-time crowd scenes

TL;DR: A novel approach to create complex scenes involving thousands of animated individuals in a simple and intuitive way by employing a brush metaphor, analogous to the tools used in image manipulation programs, to distribute, modify and control crowd members in real-time with immediate visual feedback.
Book ChapterDOI

Convolutional Neural Network for Trajectory Prediction

TL;DR: This work proposes a convolutional neural network (CNN) based human trajectory prediction approach which supports increased parallelism and effective temporal representation, and the proposed compact CNN model is faster than the current approaches yet still yields competitive results.
Journal ArticleDOI

Multi-target tracking using CNN-based features: CNNMTT

TL;DR: Comprehensive evaluations of the algorithm (CNNMTT) reveal that the CNNMTT method achieves high quality tracking results in comparison to the state of the art while being faster and involving much less computational cost.
Journal ArticleDOI

Lane formation in driven mixtures of oppositely charged colloids

TL;DR: In this paper, the authors present quantitative experimental data on colloidal laning at the single-particle level and demonstrate a continuous increase in the fraction of particles in a lane for the case where oppositely charged particles are driven by an electric field.
Proceedings ArticleDOI

Safe Reinforcement Learning With Model Uncertainty Estimates

TL;DR: MC-Dropout and Bootstrapping are used to give computationally tractable and parallelizable uncertainty estimates and are embedded in a Safe Reinforcement Learning framework to form uncertainty-aware navigation around pedestrians, resulting in a collision avoidance policy that knows what it does not know and cautiously avoids pedestrians that exhibit unseen behavior.
References
More filters
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.
Related Papers (5)