Social Force Model for Pedestrian Dynamics
Dirk Helbing,Péter Molnár +1 more
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
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Journal ArticleDOI
The escape of pedestrians with view radius
TL;DR: The classic social force model of Helbing is modified, which is applied to simulate how a pedestrian gets outside a hall full of smoke, and the view radius is introduced to describe the range the pedestrian can see.
User Simulation of Space Utilisation : System for Office Building Usage Simulation
TL;DR: The validation of USSU with regard to data collected in the RFID experiment is discussed and the goodness-of-fit between USSU and POPI+ was determined using the following criterion variables: time percentage, mean duration and mean frequency.
Journal ArticleDOI
Collective Crowd Formation Transform with Mutual Information-Based Runtime Feedback
TL;DR: This approach combines both macroscopic and microscopic controls of the crowd transformation to maximally maintain subgroups' local stability and dynamic collective behaviour, while minimizing the overall effort of the agents during the transformation.
Journal ArticleDOI
A mobility network approach to identify and anticipate large crowd gatherings
TL;DR: Within the anomalous mobility networks, high-stress crowd density is found to be preceded by a node in-degree kin surpassing the critical threshold kc, typically preceding the maximum crowd density by a couple of hours, enabling us to anticipate large crowd gatherings via a surprisingly simple approach based on the simple network index kin.
Book ChapterDOI
Passenger Dynamics at Airport Terminal Environment
TL;DR: A significant difference in behavior between business and leisure related passenger groups was resolved and the size of a passenger group has a significant influence on walking speed, whereas large groups tend to diverge into smaller groups with 2–3 members.
References
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Book
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Journal ArticleDOI
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