• Sorted by Date • Classified by Publication Type • Classified by Topic • Grouped by Student (current) • Grouped by Former Students •
Natalie Fridman and Gal A. Kaminka.
Modeling Pedestrian Crowd Behavior Based on a Cognitive Model of Social Comparison Theory. Computational and Mathematical
Organizational Theory, 16(4):348–372, 2010. Special issue on Social Simulation from the Perspective of Artificial
Intelligence
Modeling crowd behavior is an important challenge for cognitive modelers. Models of crowd behavior facilitate analysis and prediction of human group behavior, where people are close geographically or logically, and are affected by each other's presence and actions. Existing models of crowd behavior, in a variety of fields, leave many open challenges. In particular, psychology models often offer only qualitative description, and do not easily permit algorithmic replication, while computer science models are often not tied to cognitive theory and often focus only on a specific phenomenon (e.g., flocking, bi-directional pedestrian movement), and thus must be switched depending on the goals of the simulation. We propose a novel model of crowd behavior, based on Festinger's Social Comparison Theory (SCT), a social psychology theory known and expanded since the early 1950s. We propose a concrete algorithmic framework for SCT, and evaluate its implementations in several pedestrian movement phenomena such as creation of lanes in bidirectional movement and movement in groups with and without obstacle. Compared to popular models from the literature, the SCT model was shown to provide improved results. We also evaluate the SCT model on general pedestrian movement, and validate the model against human pedestrian behavior. The results show that SCT generates behavior more in-tune with human crowd behavior then existing non-cognitive models.
The article's official web page is at: http://www.springerlink.com/content/r33785714467l274/.
@Article{cmot10,
author={Natalie Fridman and Gal A. Kaminka},
title = {Modeling Pedestrian Crowd Behavior Based on a Cognitive Model of Social Comparison Theory},
journal = {Computational and Mathematical Organizational Theory},
year = {2010},
OPTkey = {},
volume = {16},
number = {4},
note = {Special issue on Social Simulation from the Perspective of Artificial Intelligence},
pages = {348--372},
OPTmonth = {November},
wwwnote = {},
OPTannote = {},
abstract = {Modeling crowd behavior is an important challenge for
cognitive modelers. Models of crowd behavior
facilitate analysis and prediction of human group
behavior, where people are close geographically or
logically, and are affected by each other's presence
and actions. Existing models of crowd behavior, in a
variety of fields, leave many open challenges. In
particular, psychology models often offer only
qualitative description, and do not easily permit
algorithmic replication, while computer science
models are often not tied to cognitive theory and
often focus only on a specific phenomenon (e.g.,
flocking, bi-directional pedestrian movement), and
thus must be switched depending on the goals of the
simulation. We propose a novel model of crowd
behavior, based on Festinger's Social Comparison
Theory (SCT), a social psychology theory known and
expanded since the early 1950s. We propose a
concrete algorithmic framework for SCT, and evaluate
its implementations in several pedestrian movement
phenomena such as creation of lanes in bidirectional
movement and movement in groups with and without
obstacle. Compared to popular models from the
literature, the SCT model was shown to provide
improved results. We also evaluate the SCT model on
general pedestrian movement, and validate the model
against human pedestrian behavior. The results show
that SCT generates behavior more in-tune with human
crowd behavior then existing non-cognitive models.},
}
Generated by bib2html.pl (written by Patrick Riley ) on Fri Aug 30, 2024 17:29:51