Traffic prediction using pheromone model

Osamu Masutani, Hiroshi Sasaki, Hirotoshi Iwasaki, Yasushi Ando, Yoshiaki Fukazawa, Shinichi Honiden

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Social insects such as ants, bees and wasps perform complex tasks with pheromone communication despite lack of top-down style control. We have examined applications of this pheromone mechanism towards ITS. In this paper, a car is regarded as an insect that releases virtual pheromone that represents traffic congestion level. We propose a method to predict traffic congestion of the immediate future through a pheromone mechanism without resort to the use of a traffic control center. Furthermore we introduced multi semantics of pheromone to refine the prediction performance. We evaluate our method using actual traffic data and the results indicate our method is superior to other conventional techniques and our previous method.

Original languageEnglish
Title of host publicationIntelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005
Pages964-975
Number of pages12
Publication statusPublished - 2009 Dec 1
Event12th World Congress on Intelligent Transport Systems 2005 - San Francisco, CA, United States
Duration: 2005 Nov 62005 Nov 10

Publication series

NameIntelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005
Volume2

Conference

Conference12th World Congress on Intelligent Transport Systems 2005
CountryUnited States
CitySan Francisco, CA
Period05/11/605/11/10

Keywords

  • Genetic algorithm
  • Traffic prediction
  • Virtual pheromone

ASJC Scopus subject areas

  • Mechanical Engineering
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Transportation
  • Automotive Engineering
  • Computer Networks and Communications
  • Artificial Intelligence
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Traffic prediction using pheromone model'. Together they form a unique fingerprint.

  • Cite this

    Masutani, O., Sasaki, H., Iwasaki, H., Ando, Y., Fukazawa, Y., & Honiden, S. (2009). Traffic prediction using pheromone model. In Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005 (pp. 964-975). (Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005; Vol. 2).