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The Comparison of Stigmergy Strategies for Decentralized Traffic Congestion Control: Preliminary Results

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7458)

Abstract

We investigate several stigmergies models for decentralized traffic congestion control. For realizing a smart city, one of the main problems that should be handled is traffic congestion. There have been a lot of works on managing traffic congestion with information technology. There is a relatively long history on observing traffic flow and then providing stochastic estimation on traffic congestion. Recently, more dynamic coordination methods are becoming possible by using more short term traffic information. Short term traffic information can be provided by car navigation systems with GPS (Global Positioning System)s and probe-vehicle information. There are several approaches to handle short term traffic information, in which stigmergy-based approach is a popular. Stigmergy is employed for indirect communication for cooperation among distributed agents. We can imagine several types of stigmergies : long term memory, short term memory, and anticipatory memory. However, there have been no discussion what kind of stigmergies can work well for managing traffic congestion. We conducted several simulations to compare the different kind of stigmergies. Our preliminary results demonstrate that if the traffic network is static, the combination of long term and short term stigmergies overcome the other stigmergies.

Keywords

  • Road Network
  • Multiagent System
  • Congestion Control
  • Trust Rate
  • Smart City

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2012 Springer-Verlag Berlin Heidelberg

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Ito, T., Kanamori, R., Takahashi, J., Maestre, I.M., de la Hoz, E. (2012). The Comparison of Stigmergy Strategies for Decentralized Traffic Congestion Control: Preliminary Results. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_15

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  • DOI: https://doi.org/10.1007/978-3-642-32695-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32694-3

  • Online ISBN: 978-3-642-32695-0

  • eBook Packages: Computer ScienceComputer Science (R0)