Intelligent signal coordination on congested networks using parallel micro genetic algorithms

Montty Girianna, Rahim F Benekohal

Research output: Contribution to conferencePaper

Abstract

This paper presents the application of Parallel Genetic Algorithms (GA) on signal coordination for networks with congested intersections. When a serial GA is applied to solve traffic control problems, its performance in terms of computation time diminishes as the size of signal networks increases, or the duration of congestion lengthens. This paper uses master-slave parallel micro GA (PMGA) to solve signal coordination, formulated as a dynamic optimization problem. The elapsed time per generation reduces as the number of processors involved increases. When the number of processors equals the number of individuals, for network described in this paper, the master-slave PMGA reaches the highest speed-up. Although the master-slave PMGA provides a lower bound speed-up and its efficiency degrades as the number of processors increases, this study demonstrates the potential benefits of using parallel GA in solving signal coordination problems.

Original languageEnglish (US)
Pages191-198
Number of pages8
StatePublished - Jan 1 2002
EventProceedings of the seventh International Conference on: Applications of Advanced Technology in Transportation - Cambridge, MA, United States
Duration: Aug 5 2002Aug 7 2002

Other

OtherProceedings of the seventh International Conference on: Applications of Advanced Technology in Transportation
CountryUnited States
CityCambridge, MA
Period8/5/028/7/02

ASJC Scopus subject areas

  • Engineering(all)

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    Girianna, M., & Benekohal, R. F. (2002). Intelligent signal coordination on congested networks using parallel micro genetic algorithms. 191-198. Paper presented at Proceedings of the seventh International Conference on: Applications of Advanced Technology in Transportation, Cambridge, MA, United States.