Social Force Model for Pedestrian Dynamics
Dirk Helbing,Péter Molnár +1 more
Reads0
Chats0
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
Journal ArticleDOI
Behavior-based cellular automaton model for pedestrian dynamics
TL;DR: A behavior-based cellular automaton model is proposed, which involves the environmental characteristics and neighbors' behaviors and shows that enhancing the degree of emergency will shorten evacuation time yet decrease cooperation enthusiasm, which may shed new light to the study of pedestrian dynamics in realistic world.
Posted Content
What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction
TL;DR: In this paper, a simple Constant Velocity Model (CVM) is proposed to predict pedestrian motion. But the model is not able to make use of the additional information they are provided with, or this information is not as relevant as commonly believed.
Journal ArticleDOI
Cyclist Social Force Model at Unsignalized Intersections With Heterogeneous Traffic
TL;DR: Simulation results indicated that the model can represent cyclist crossing behavior at unsignalized intersection with heterogeneous traffic as in the real world.
Journal ArticleDOI
Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach
TL;DR: This work provides a self-contained tutorial on a conditional variational autoencoder (CVAE) approach to human behavior prediction which, at its core, can produce a multimodal probability distribution over future human trajectories conditioned on past interactions and candidate robot future actions.
Journal ArticleDOI
Can Walking Behavior be Predicted?: Analysis of Calibration and Fit of Pedestrian Models:
TL;DR: This paper compares three calibration methods for a slightly adapted social force model and suggests that several data sets with different characteristics do not need to be included in the calibration process to achieve a model that performs well in a wider variety of settings.
References
More filters
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.