交通流的混沌特性分析及其预测

实时准确的短时交通流预测是智能交通系统中实现交通控制和诱导的关键技术之一.首先，采用饱和关联维数法和互信息量法对交通流时间序列的嵌入维数和延迟时间进行计算，并根据计算结果对交通流时间序列进行相空间重构；然后，采用wolf方法计算其最大Lyapunov指数，并对其进行功率谱分析，结果表明，交通流时间序列具有噪声；最后，分别采用基于BP神经网络和RBF神经网络的预测模型对交通流时间序列进行预测，结果表明，2种模型对短时交通流均能较好预测，但后者的预测精度较高，预测速度较快.嵌入维数；延迟时间；相空间重构；BP神经网络；RBF神经网络

Analysis and Prediction on the Chaotic Property of Traffic Flow Time Series

The real-teime and procise short-ferm traffic flow forecesting is the key factor for the realizing of traffic control and traffic guidance in the intelligent traffic system. Saturated correlation dimension method and mutual information method are used to calculate embedding dimension and delay time,and the traffic flow time series is reconstructed accordingly in phase space. Wolf method is used to calculate the largest Lyapunov exponent, and the power spectrum of traffic flow time series is analyzed. Results show that the traffic flow series is a chaotic sequence with noise. The prediction models based on BP neu- ral networks and RBF neural networks are applied to pedict traffic flow time series, which shows that the two models both have good prediction effects, with the former having higher prediction accuracy and quicker prediction speed.