| Traffm chaos and its prediction based on a nonlinear car-following model |
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| 作者姓名： | Hui FU Jianmin XU Lunhui XU |
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| 作者单位： | College of Traffic and Communication, South China University of Technology, Guangzhou Guangdong 510640, China |
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| 摘 要： | This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car＇s movement in a single lane. Traffic chaos is a promising field, and chaos theory has been applied to identify and predict its chaotic movement. A simulated traffic flow is generated using a car-following model（ GM model）, and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN （radial basis function neural network） is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent. The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series
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| 关 键 词： | 汽车模型 交通管理 交通预测 无序系统 神经网络 |
| 收稿时间： | 2005-01-06 |
| 修稿时间： | 2005-05-13 |
Traffic chaos and its p rediction based on a nonlinear car-f ollowing model |
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| Hui FU,Jianmin XU,Lunhui XU.Traffm chaos and its prediction based on a nonlinear car-following model[J].Journal of Control Theory and Applications,2005,3(3):302-307. |
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| Authors: | Hui FU Jianmin XU Lunhui XU |
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| Affiliation: | College of Traffic and Communication ,South China University of Technology ,Guangzhou Guangdong 510640 , China |
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| Abstract: | This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car's movement in a single lane.Traffic chaos is a promising field,and chaos theory has been applied to identify and predict its chaotic movement.A simulated traffic flow is generated using a car-following model(GM model),and the distance between two cars is investigated for its dynamic properties.A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model.A new algorithm using a RBF NN (radial basis function neural network) is proposed to predict this traffic chaos.The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent.The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow {series.} |
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| Keywords: | Car-following model Chaos Traffic prediction Radial basis function neural network ( RBF NN) |
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