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

Chengyou Cui, Jisun Shin, HeeHyol Lee

研究成果: Conference contribution

抜粋

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.

元の言語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
出版物ステータスPublished - 2010
イベント15th International Symposium on Artificial Life and Robotics, AROB '10 - Beppu, Oita
継続期間: 2010 2 42010 2 6

Other

Other15th International Symposium on Artificial Life and Robotics, AROB '10
Beppu, Oita
期間10/2/410/2/6

    フィンガープリント

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

これを引用

Cui, C., Shin, J., & Lee, H. (2010). Real time traffic signal learning control using BPNN based on prediction for probabilistic distribution of standing vehicles. : Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10 (pp. 569-572)