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
Event15th International Symposium on Artificial Life and Robotics, AROB '10 - Beppu, Oita
Duration: 2010 Feb 42010 Feb 6

Other

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

Fingerprint

Traffic signals
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
  • 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)

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

Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10. 2010. p. 569-572.

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

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, 15th International Symposium on Artificial Life and Robotics, AROB '10, Beppu, Oita, 10/2/4.
Cui C, Shin J, Lee H. 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. 2010. p. 569-572
Cui, Chengyou ; Shin, Jisun ; Lee, HeeHyol. / 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. 2010. pp. 569-572
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