期刊文献+

基于SFSTFC的单路口交通信号控制

A Traffic Signal Control Algorithm Based on Scale Factor Self-tuning Fuzzy Controller for Single Intersection
下载PDF
导出
摘要 针对城市单路口的交通信号控制问题,提出了一种基于比例因子自调整模糊控制器的控制方法。首先分析了比例因子对模糊控制系统性能的影响;然后,根据当前路口交通流红灯相位的损失指数和绿灯相位的增益指数,给出了比例因子自调整模糊算法,可依据路口动态车流信息在线调整比例因子;最后,基于提出的比例因子自调整模糊控制器,给出了一种单路口交通信号控制策略。仿真结果表明,相对于定时控制,以及普通模糊控制,比例因子自调整方法能够有效地降低路口平均车辆延误,提高了交通信号控制系统的控制精度。 Aiming at the urban single intersection traffic signal control problem,a traffic signal control algorithm based on scale factor self-tuning fuzzy controller is proposed. Firstly,the influence of scale factor on the performance of fuzzy control system is analyzed. Then,according to the loss index of red light phase and the gain index of green light phase of the current intersection traffic flow information,the self-tuning fuzzy algorithm of scale factor is proposed,which can adjust the scale factor online according to the intersection dynamic traffic information. At last,base on the proposed self-tuning fuzzy controller of scale factor,a single intersection traffic signal control strategy is obtained. The simulation results show that,compared with the timing control and ordinary fuzzy control,the proposed method in this paper can effectively reduce the intersection average vehicle delay and improve the control precision of the traffic signal control system.
出处 《计算机仿真》 CSCD 北大核心 2015年第3期197-201,共5页 Computer Simulation
关键词 交通信号控制 模糊控制器 比例因子 自调整 SFSTFC Fuzzy controller Scale factor Self-tuning
  • 相关文献

参考文献11

  • 1杨文臣,张轮,何兆成..城市单路口交通信号两级模糊优化控制与仿真[J].中山大学学报(自然科学版),2012,51(6):41-47. 被引量:4
  • 2燕乐纬,陈洋洋,周云.一种改进的微种群遗传算法[J].中山大学学报(自然科学版),2012,51(1):50-54. 被引量:7
  • 3P J Ballester, J N Carter. A parallel real-coded genetic algorithm for history matching and its application to a real petroleum reservoir [ J ]. Journal of Petroleum Science and Engineering, 2007,59 (3) : 157-168.
  • 4J J Henry, J L Farges, J L Gallego. Neuro-fuzzy techniques for traffic control[ J]. Control Engineering Practice, 1998,6 (6) :755 -761.
  • 5E Bingham. Reinforcement learning in neuro-fuzzy traftic signal control[ J]. European Journal of Operation Search, 2001,131 (2) :232-241.
  • 6SYED MASIUR Rahman,NEDAL T. Ratrout.基于模糊逻辑的交通信号控制方法综述及在沙特阿拉伯的应用前景(英文)[J].交通运输系统工程与信息,2009,9(5):58-70. 被引量:5
  • 7D A Lekova, L Mikhailov, Boyadjie. Redundant fuzzy rules exclu- sion by genetic algorithms [ J]. Fuzzy Sets and Systems, 1998,100 ( 1 ) :235-243.
  • 8J W Kim, B M Kim, J Y Kim. Genetic algorithm simulation ap- proach to determine mebership functions of fuzzy traffic controller [J]. Electronics Letters, 1998,34(20) :1982- 983.
  • 9杨祖元,黄席樾,刘鸿飞,杜长海.基于改进遗传算法的交叉口模糊控制研究[J].计算机应用研究,2009,26(9):3330-3333. 被引量:5
  • 10D Flynn, et al. A self-tuning automatic voltage regulator de- signed for an industrial environment[ J]. IEEE Trans EC, 1996, 11 (2) : 429-434.

二级参考文献47

  • 1陈建勤,吕剑虹,陈来九.模糊控制系统的闭环模型及稳定性分析[J].自动化学报,1994,20(1):1-9. 被引量:27
  • 2陈丹,高孝洪,张本.城市道路交叉口交通信号控制系统的发展及现状[J].交通科技,2005,15(6):76-78. 被引量:9
  • 3李瑞敏,陆化普,史其信.基于交通需求强度的路口多层模糊控制模型研究[J].武汉理工大学学报(交通科学与工程版),2006,30(1):1-4. 被引量:17
  • 4陈洪,陈森发.单路口交通实时模糊控制的一种方法[J].信息与控制,1997,26(3):227-233. 被引量:61
  • 5HAWAS Y E. A fuzzy-based system for incident detection in urban street networks[ J]. Trans on Research Part C: Emerging Technologies, 2007,15(2) :69-95.
  • 6PAPPIS C P, MAMDANI E H. A fuzzy logic controller for a traffic junction[J]. IEEE Trans on Systems, Man, and Cybernetics, 1977 ,SMC-7(10) :707-717.
  • 7HSING-HAN L, PAU-LO H. Design and simulation of adaptive fuzzy control on the traffic network[ C ]//Pore of SICE-ICASE International Joint Conference. Korea: [ s. n. ] , 2006:4961-4966.
  • 8NAIR B M, CAI Jin-hai. A fuzzy logic controller for isolated signalized intersection with traffic abnormality considered [ C ]//Proc of IEEE Intelligent Vehicles Symposium. Turkey: IEEE press, 2007: 1229-1233.
  • 9ALCALA R, ALCALA-FDEZ J, HERRERA F, et al. Genetic learning of accurate and compact fuzzy rule based systems based on the 2- tuples linguistic representation [ J ]. International Journal of Approximate Reasoning, 2007,44( 1 ) :45-64.
  • 10CHEN S M, HUANG C M. A new approach to generate weighted fuzzy rules using genetic algorithms for estimating null values[ J]. Expert Systems with Applications, 2007,5 (3) :905- 917.

共引文献26

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部