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)

Abstract

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
Volume15
Issue number1
DOIs
Publication statusPublished - 2010

Fingerprint

Traffic signals
Learning
Control systems
Ceilings
Real time control
Bayesian networks
Backpropagation
Neural networks

Keywords

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

ASJC Scopus subject areas

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

Cite this

Real-time traffic signal learning control using BPNN based on predictions of the probabilistic distribution of standing vehicles. / Cui, Chengyou; Shin, Jisun; Lee, HeeHyol.

In: Artificial Life and Robotics, Vol. 15, No. 1, 2010, p. 58-61.

Research output: Contribution to journalArticle

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