Traffic signal control based on a predicted traffic jam distribution

Cheng You Cui, Ji Sun Shin, Fumihiro Shoji, Hee Hyol Lee

Research output: Contribution to journalArticle

1 Citation (Scopus)


In this article, we propose a new method of traffic signal control based on the predicted distribution of traffic jams. First, we built a forecasting model to predict the probability distribution of vehicles being in a traffic jam during each period of the traffic signals. A dynamic Bayesian network was used as the forecasting model, and this predicted the probability distribution of the number of standing vehicles in a traffic jam. According to calculations by the dynamic Bayesian network, a prediction of the probability distribution of the number of standing vehicles at each time will be obtained, and a control rule to adjust the split and cycle of the signals to maintain the probability of a lower limit and a ceiling of standing vehicles is deduced. Through a simulation using the actual traffic data of a city, the effectiveness of our method is shown.

Original languageEnglish
Pages (from-to)134-137
Number of pages4
JournalArtificial Life and Robotics
Issue number2
Publication statusPublished - 2009 Nov 1



  • Dynamic Bayesian network
  • Forecasting model
  • Probabilistic distribution
  • Traffic jam
  • Traffic signal control

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

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