Real time traffic signal learning control using BPNN based on prediction for probabilistic distribution of standing vehicles

Chengyou Cui*, Jisun Shin, Heehyol Lee

*この研究の対応する著者

研究成果: Conference contribution

抄録

In this paper, a new method to predict the probabilistic distribution of traffic jam at crossroads and a traffic signal learning control system are proposed. First, the Dynamic Bayesian Network is used for build a forecasting model to predict the probabilistic distribution of vehicles for traffic jam during the each period of traffic signal. The adjusting algorithm of traffic signal control is applied to maintain the probability of a lower limit and ceiling of the standing vehicles to get the desired probabilistic distribution of the standing vehicles. In order to achieve the real time control, a learning control system based on the Back Propagation Neural Network is used. Finally, the effectiveness of the new traffic signal control system using the actual traffic data will be shown.

本文言語English
ホスト出版物のタイトルProceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10
ページ569-572
ページ数4
出版ステータスPublished - 2010 12 1
イベント15th International Symposium on Artificial Life and Robotics, AROB '10 - Beppu, Oita, Japan
継続期間: 2010 2 42010 2 6

出版物シリーズ

名前Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10

Other

Other15th International Symposium on Artificial Life and Robotics, AROB '10
国/地域Japan
CityBeppu, Oita
Period10/2/410/2/6

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 人間とコンピュータの相互作用

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