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

Chengyou Cui, Jisun Shin, Heehyol Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10
Pages569-572
Number of pages4
Publication statusPublished - 2010 Dec 1
Event15th International Symposium on Artificial Life and Robotics, AROB '10 - Beppu, Oita, Japan
Duration: 2010 Feb 42010 Feb 6

Publication series

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

Other

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

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Keywords

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

ASJC Scopus subject areas

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

Cite this

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