Real-time traffic signal learning control using BPNN based on predictions of the probabilistic distribution of standing vehicles

Chengyou Cui, Jisun Shin, Heehyol Lee

Research output: Contribution to journalArticle

7 Citations (Scopus)


In this article, a new method to predict the probabilistic distribution of a traffic jam at crossroads and a traffic signal learning control system are proposed. First, a dynamic Bayesian network is used to build a forecasting model to predict the probabilistic distribution of vehicles in a traffic jam during each period of the traffic signals. An adjusting algorithm for traffic signal control is applied to maintain the probability of a lower limit and a ceiling of standing vehicles to get the desired probabilistic distribution of standing vehicles. In order to achieve real-time control, a learning control system based on a back-propagation neural network is used. Finally, the effectiveness of the new traffic signal control system using actual traffic data will be shown.

Original languageEnglish
Pages (from-to)58-61
Number of pages4
JournalArtificial Life and Robotics
Issue number1
Publication statusPublished - 2010 Sep 3



  • BP neural network
  • Bayesian network
  • Probabilistic distribution
  • Traffic signal control

ASJC Scopus subject areas

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

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