Title
LARGE-SCALE HIGH-FIDELITY AGENT-BASED SIMULATION IN AIR TRAFFIC DOMAIN
Abstract
We present the concept of dynamic partitioning of scalable, high-fidelity multi-agent simulation complemented with intelligent load-balancing processes. The simulation framework is designed to simulate entities to high details that require extended computation resources. To be able to simulate a huge amount of entities, distributed simulation is introduced using spatial partitioning and dynamic load balancing. A novel and important feature is the combination of the synchronous and asynchronous parts in the simulation. We use the domain of the air traffic simulation to verify the simulation framework. We present a method to perform spatial and temporal planning within 3D space and multilayer architecture using several collision avoidance algorithms to illustrate the high computational demands of each airplane. The platform has been used to support simulation of an entire civilian air traffic touching the national airspace of United States. A thorough evaluation of the system has been performed, confirming that it can scale up to a very high number of complex agents operating simultaneously (thousands of aircrafts) with full detailed models.
Year
DOI
Venue
2011
10.1080/01969722.2011.610270
Cybernetics and Systems
Keywords
Field
DocType
high number,simulation framework,high detail,high-fidelity multi-agent simulation,dynamic partitioning,large-scale high-fidelity agent-based simulation,entire civilian air traffic,dynamic load balancing,spatial partitioning,air traffic simulation,air traffic domain,high computational demand,air traffic,load balance
Space partitioning,Asynchronous communication,Computer science,Air traffic control,Real-time computing,Airspace class,Network traffic simulation,Dynamic simulation,Scalability,Computation,Distributed computing
Journal
Volume
Issue
ISSN
42
7
0196-9722
Citations 
PageRank 
References 
7
0.74
12
Authors
3
Name
Order
Citations
PageRank
Premysl Volf1356.16
David Sislak2223.24
Michal Pěchouček31134133.88