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ZHANG Lin, DU Wen, SONG Jun-ji, XIANG Hong-yan. Traffic assignment method based on dynamic fuzzy control and fuzzy decision making[J]. Journal of Traffic and Transportation Engineering, 2010, 10(3): 110-117. doi: 10.19818/j.cnki.1671-1637.2010.03.019
Citation: ZHANG Lin, DU Wen, SONG Jun-ji, XIANG Hong-yan. Traffic assignment method based on dynamic fuzzy control and fuzzy decision making[J]. Journal of Traffic and Transportation Engineering, 2010, 10(3): 110-117. doi: 10.19818/j.cnki.1671-1637.2010.03.019
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Traffic assignment method based on dynamic fuzzy control and fuzzy decision making

doi: 10.19818/j.cnki.1671-1637.2010.03.019
  • 1.

    School of Traffic and Transportation, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

  • 2.

    School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang 330013, Jiangxi, China

  • 3.

    Technology Office, Jiangxi College of Foreign Studies, Nanchang 330013, Jiangxi, China

More Information
  • Author Bio:

    ZHANG Lin(1973-), male, lecturer, doctoral student, +86-28-87602441, zagln0@163.com

    DU Wen (1943-), male, professor, +86-28-87602441, Duwen00168@163.com

  • Received Date: 2010-01-06
  • Publish Date: 2010-06-25
    Abstract
  • In order to improve the operational efficiency of whole traffic network, a nonlinear programming model was established, and the corresponding nonlinear fuzzy programming model was further obtained by using dynamic fuzzy control methods.According to the magnitudes and directions of adjacent intersection traffic flows, intersection signal dynamic phases were reasonably allocated by using different elastic signal periods and maximum membership degree principle.Based on the observation path of driver, the minimum detection impedance path was obtained, the impedance was compared with the path impedances of intelligent transportation system, and the greatest satisfaction path was chosen.Analysis result indicates that the traffic assignment based on dynamic fuzzy control and fuzzy decision making method is consistent with the shortest path method, and the control effectiveness of signal intersection increases by 50% or more based on fuzzy optimal control, dynamic fuzzy control and fuzzy decision making method can accurately reflect human behavior, and is an effective measurement for signal intersection control.

     

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