350 rub
Journal Neurocomputers №3 for 2011 г.
Article in number:
Adaptive traffic light control based on neuro-fuzzy system with reinforcement learning
Keywords: fuzzy-net system the control supporting training
Authors:
S. E. Shvets, A. N. Fomin, A. V. Protodyakonov
Abstract:
The problem of adaptive neuro-fuzzy traffic light control was described. The paper includes structure of the system and learning algorithm. There is a comparison of the effectiveness of the adaptive system and optimal pre-timed on a model of a real crossroad.
Pages: 57-63
References
  1. Zhang, Y., Xie, Y., Ye, Z., Development and Evaluation of a Multi-Agent Based Neuro-Fuzzy. Texas Transportation Institute. 2007. Technical Report. SWUTC/07/473700-00092-1.
  2. Jouffe, L., Actor-Critic Learning Based on Fuzzy Inference System // Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. 1996. C. 339-344.
  3. Koonce P., Traffic Signal Timing Manual. 2008.
  4. Yu, X., Recker, W., Stochastic Adaptive Control Model for Traffic Signal Systems // Transportation Research PartC. V. 14, 2006. № 4. С. 263-282.
  5. Sutton, R. S., Barto, A. G., Reinforcement learning: An introduction. Cambridge, MA: MITPress. 1998.
  6. Редько В. Г. Проблема происхождения интеллекта и модели адаптивного поведения // Научная сессия МИФИ - 2006. VIII Всероссийская научно-техническая конференция «Нейроинформатика-2006»: Лекции по нейроинформатике. М.: МИФИ. 2006. С. 112-170.
  7. Thorpe, T. L., Vehicle Traffic Light Control Using SARSA. 1997.
  8. Bingham, E., Reinforcement Learning in Neurofuzzy Traffic Signal Control. // European Journal of Operational Research. 2001. V. 131. № 2. С. 232-241.
  9. Abdulhai B., Pringle R., Karakoulas G. Reinforcement Learning for True Adaptive Traffic Signal Control // Journal of Transportation Engineering. 2003. V. 129. № 3.С. 278-285.