Traffic chaos and its prediction based on a nonlinear car-following model
Hui FU
Jianmin XU
Lunhui XU
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.
机标关键词：traffic flowLyapunov exponentnonlinear responsedynamic propertiesGM modeldynamic behaviorneural networkbasis function
分类号：U49(交通工程与公路运输技术管理)
论文发表日期：2005-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：6( 302-307 )
英文信息