摘要The chaos control of freeway mainline was studied by using variable speed limits and fuzzy-neural networks (FNNs) based on subtractive clustering. Based on the uncertainty and nonlinearity of a traffic system, the establishment of a knowledge base of a mainline chaos controller for freeway was proposed by using data mining technology. The chaos control principle of mainline variable speed limits in freeway was briefly introduced. The Takagi-Sugeno FNNs chaos controller was designed, where traffic density, upstream traffic volume, and maximal Lyapunov exponent were selected as the input variables, whereas mainline speed upper limit was selected as the output variable of the controller. Subtractive clustering was used to determine the controller structure, including the extraction of fuzzy rules and generation of initial parameters. The radius of the clustering centers was optimized using the genetic algorithm, and the parameters of the fuzzy controller were optimized using FNN. The simulation result indicated that order motion on freeway can be realized by using the mainline intelligent chaos controller designed based on the proposed method to suppress traffic jam and to enhance traffic volume.
Abstract：The chaos control of freeway mainline was studied by using variable speed limits and fuzzy-neural networks (FNNs) based on subtractive clustering. Based on the uncertainty and nonlinearity of a traffic system, the establishment of a knowledge base of a mainline chaos controller for freeway was proposed by using data mining technology. The chaos control principle of mainline variable speed limits in freeway was briefly introduced. The Takagi-Sugeno FNNs chaos controller was designed, where traffic density, upstream traffic volume, and maximal Lyapunov exponent were selected as the input variables, whereas mainline speed upper limit was selected as the output variable of the controller. Subtractive clustering was used to determine the controller structure, including the extraction of fuzzy rules and generation of initial parameters. The radius of the clustering centers was optimized using the genetic algorithm, and the parameters of the fuzzy controller were optimized using FNN. The simulation result indicated that order motion on freeway can be realized by using the mainline intelligent chaos controller designed based on the proposed method to suppress traffic jam and to enhance traffic volume.
基金资助:Supported by the National Natural Science Foundation of China (No.50478088);and the Natural Science Foundation of Hebei Province of China (No.E2011202073)
通讯作者:
PANG Ming-bao, pmbpgy@sina.com
E-mail: pmbpgy@sina.com
引用本文:
庞明宝, 任莎莎, 王彦虎, 陈培. 基于减法聚类的高速公路主线可变速度FNN混沌控制[J]. Journal of Highway and Transportation Research and Development, 2013, 7(3): 90-98.
PANG Ming-bao, REN Sha-sha, WANG Yan-hu, CHEN Pei. Chaos Control of Freeway Mainline Using Variable Speed Limits with Fuzzy-neural Networks Based on Subtractive Clustering. Journal of Highway and Transportation Research and Development, 2013, 7(3): 90-98.
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2013, Vol. 7 
