EconPapers    
Economics at your fingertips  
 

Transport modeling by multi-agent systems: a swarm intelligence approach

Dusˇan Teodorovic´

Transportation Planning and Technology, 2003, vol. 26, issue 4, 289-312

Abstract: There are a number of emergent traffic and transportation phenomena that cannot be analyzed successfully and explained using analytical models. The only way to analyze such phenomena is through the development of models that can simulate behavior of every agent. Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual ‘agents’ and that each agent interacts with other agents according to localized knowledge. The agent-based approach is a ‘bottom-up’ approach to modeling where special kinds of artificial agents are created by analogy with social insects. Social insects (including bees, wasps, ants and termites) have lived on Earth for millions of years. Their behavior in nature is primarily characterized by autonomy, distributed functioning and self-organizing capacities. Social insect colonies teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. On the other hand, a large number of traditional engineering models and algorithms are based on control and centralization. In this article, we try to obtain the answer to the following question: Can we use some principles of natural swarm intelligence in the development of artificial systems aimed at solving complex problems in traffic and transportation?

Date: 2003
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0308106032000154593 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:26:y:2003:i:4:p:289-312

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20

DOI: 10.1080/0308106032000154593

Access Statistics for this article

Transportation Planning and Technology is currently edited by Dr. David Gillingwater

More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:transp:v:26:y:2003:i:4:p:289-312