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
Proceedings ArticleDOI
Who are you with and where are you going
TL;DR: This model views pedestrians as decision-making agents who consider a plethora of personal, social, and environmental factors to decide where to go next and forms prediction of pedestrian behavior as an energy minimization on this model.
Journal ArticleDOI
Discrete choice models of pedestrian walking behavior
TL;DR: A model predicting where the next step of a walking pedestrian will be, at a given point in time is developed, using a dynamic and individual-based spatial discretization, representing the physical space.
Proceedings ArticleDOI
Socially aware motion planning with deep reinforcement learning
TL;DR: Using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms and is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.
Posted Content
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks.
TL;DR: In this paper, a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people, and predicts socially plausible future by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss.
Journal ArticleDOI
Jamming transition in pedestrian counter flow
TL;DR: In this article, a lattice gas model with biased random walkers is presented to mimic the pedestrian counter flow in a channel under the open boundary condition of constant density, and the transition point is given by pc=0.45±0.01, not depending on the system size.
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.