Real-time traffic signal control for optimization of traffic jam probability

Cheng You Cui*, Ji Sun Shin, Michio Miyazaki, Hee Hyol Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Real-time traffic signal control is an integral part of an urban traffic control system. It can control traffic signals online according to variations of traffic flow. In this paper we propose a new method for a real-time traffic signal control system. The system uses a cellular automaton model and a Bayesian network model to predict probabilistic distributions of standing vehicles, and uses particle swarm optimization to calculate the optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed CAPSOBN real-time traffic signal control system using a micro traffic simulator.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalElectronics and Communications in Japan
Issue number1
Publication statusPublished - 2013 Jan 1


  • Bayesian network
  • cellular automaton traffic model
  • particle swarm optimization
  • predicted probabilistic distribution
  • traffic jam
  • traffic signal control

ASJC Scopus subject areas

  • Signal Processing
  • Physics and Astronomy(all)
  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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