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

3 Citations (Scopus)


Real-time traffic signal control is an integral part of urban traffic control system. It can control traffic signals online according to variation of traffic flow. In this paper, we propose a new method for the 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 a Particle Swarm Optimization method to calculate optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed real-time traffic signal control system CAPSOBN using a micro traffic simulator.

Original languageEnglish
Pages (from-to)21-31+2
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number1
Publication statusPublished - 2012


  • Bayesian Network
  • Cellular Automaton traffic model
  • Particle Swarm Optimization
  • Predicted Probabilistic Distribution
  • Traffic jam
  • Traffic signal control

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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