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|Title=On Modeling and Evolutionary Optimization of Nonlinearly Coupled Pedestrian Interactions
 
|Title=On Modeling and Evolutionary Optimization of Nonlinearly Coupled Pedestrian Interactions
 
|Year=2010
 
|Year=2010
 +
|Month=April
 
|Booktitle=EVoStar 2010, Evolutionary Algorithms and Complex Systems
 
|Booktitle=EVoStar 2010, Evolutionary Algorithms and Complex Systems
 
|Pages=21-30
 
|Pages=21-30
 
|Publisher=Springer
 
|Publisher=Springer
 +
|Address=Berlin Heidelberg
 
|Series=Lecture Notes in Computer Science
 
|Series=Lecture Notes in Computer Science
 
|Number=6024
 
|Number=6024

Aktuelle Version vom 15. März 2011, 13:51 Uhr


On Modeling and Evolutionary Optimization of Nonlinearly Coupled Pedestrian Interactions


On Modeling and Evolutionary Optimization of Nonlinearly Coupled Pedestrian Interactions



Published: 2010 April

Buchtitel: EVoStar 2010, Evolutionary Algorithms and Complex Systems
Nummer: 6024
Reihe: Lecture Notes in Computer Science
Seiten: 21-30
Verlag: Springer
Erscheinungsort: Berlin Heidelberg

Referierte Veröffentlichung

BibTeX

Kurzfassung
Social force based modeling of pedestrians is an advanced microscopic approach for simulating the dynamics of pedestrian motion. The developments presented in this paper extend the widespread social force model to include improved velocity-dependent interaction forces. This modeling considers interactions of pedestrians with both static and dynamic obstacles, which can be also be effectively used to model pedestrian-vehicle interactions. The superiority of the proposed model is shown by comparing it with existing ones considering several thought experiments. Moreover, we apply an evolutionary algorithm to solve the model calibration problem, considering two real-world instances. The objective function for this problem comes from a set of highly nonlinear coupled differential equations. An interesting feature that came out is that the solutions are multi-modal. This makes this problem an excellent example for evolutionary algorithms and other such population based heuristics algorithms.



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet

Evolutionäre Algorithmen, Agentensysteme, Modellierung