Abstract
Social insects perform complex tasks without top-down style control, by sensing and depositing chemical markers called "pheromone". We have examined applications of this pheromone paradigm towards intelligent transportation systems (ITS). Many of the current traffic management approaches require central processing with the usual risk for overload, bottlenecks and delays. Our work points towards a more decentralized approach that may overcome those risks. In this paper, a car is regarded as a social insect that deposits (electronic) pheromone on the road network. The pheromone represents density of traffic. We propose a method to predict traffic congestion of the immediate future through a pheromone mechanism without resorting to the use of a traffic control center. We evaluate our method using a simulation based on real-world traffic data and the results indicate applicability to prediction of immediate future traffic congestion. Furthermore, we describe the relationship between pheromone parameters and accuracy of prediction.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 182-196 |
| Number of pages | 15 |
| Volume | 3910 LNAI |
| DOIs | |
| Publication status | Published - 2006 |
| Event | 3rd International Workshop on Engineering Self-Organising Applicaions, ESOA 2005 - Utrecht Duration: 2005 Jul 25 → 2005 Jul 25 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 3910 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 3rd International Workshop on Engineering Self-Organising Applicaions, ESOA 2005 |
|---|---|
| City | Utrecht |
| Period | 05/7/25 → 05/7/25 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science
Cite this
Pheromone model : Application to traffic congestion prediction. / Ando, Yasushi; Masutani, Osamu; Sasaki, Hiroshi; Iwasaki, Hirotoshi; Fukazawa, Yoshiaki; Honiden, Shinichi.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3910 LNAI 2006. p. 182-196 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3910 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Pheromone model
T2 - Application to traffic congestion prediction
AU - Ando, Yasushi
AU - Masutani, Osamu
AU - Sasaki, Hiroshi
AU - Iwasaki, Hirotoshi
AU - Fukazawa, Yoshiaki
AU - Honiden, Shinichi
PY - 2006
Y1 - 2006
N2 - Social insects perform complex tasks without top-down style control, by sensing and depositing chemical markers called "pheromone". We have examined applications of this pheromone paradigm towards intelligent transportation systems (ITS). Many of the current traffic management approaches require central processing with the usual risk for overload, bottlenecks and delays. Our work points towards a more decentralized approach that may overcome those risks. In this paper, a car is regarded as a social insect that deposits (electronic) pheromone on the road network. The pheromone represents density of traffic. We propose a method to predict traffic congestion of the immediate future through a pheromone mechanism without resorting to the use of a traffic control center. We evaluate our method using a simulation based on real-world traffic data and the results indicate applicability to prediction of immediate future traffic congestion. Furthermore, we describe the relationship between pheromone parameters and accuracy of prediction.
AB - Social insects perform complex tasks without top-down style control, by sensing and depositing chemical markers called "pheromone". We have examined applications of this pheromone paradigm towards intelligent transportation systems (ITS). Many of the current traffic management approaches require central processing with the usual risk for overload, bottlenecks and delays. Our work points towards a more decentralized approach that may overcome those risks. In this paper, a car is regarded as a social insect that deposits (electronic) pheromone on the road network. The pheromone represents density of traffic. We propose a method to predict traffic congestion of the immediate future through a pheromone mechanism without resorting to the use of a traffic control center. We evaluate our method using a simulation based on real-world traffic data and the results indicate applicability to prediction of immediate future traffic congestion. Furthermore, we describe the relationship between pheromone parameters and accuracy of prediction.
UR - http://www.scopus.com/inward/record.url?scp=33745773885&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745773885&partnerID=8YFLogxK
U2 - 10.1007/11734697_14
DO - 10.1007/11734697_14
M3 - Conference contribution
AN - SCOPUS:33745773885
SN - 3540333428
SN - 9783540333425
VL - 3910 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 182
EP - 196
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -