一种基于小数据量的快速识别短时交通流混沌特性的方法

对短时交通流进行预测、诱导和控制是智能交通控制系统的重要研究内容。由于对短时交通流进行混沌特性识别时,存在实时性与样本数量之间的矛盾。因此,本文基于混沌时间序列分析理论,提出了一种快速计算短时交通流时间序列最大Lyapunov指数的小数据量方法,用于识别短时交通流中是否存在混沌特性。该方法首先将短时交通流时间序列在相空间中进行重构,以充分提取短时交通流中的相关信息。并结合庞卡来截面法对识别结果进行了验证。从而为对短时交通流进行分析、预测和控制时所采用的相应方法提供了可靠的理论依据。对实测短时交通流行为进行识别的结果表明,该方法具有计算量小、实时性好,对小数据量可靠且容易操作等优点。

A Method of Rapidly Identifying Chaos Characteristics of Short-term Traffic Flow Based on Small-data-sets

The forecasting,inducement and control of the short-termtraffic flowis veryi mportant for theintel-ligent transportation control system(ITS).Because thereis a paradox betweenthe real-ti me and sampling datawhen we identifythe chaotic characteristics of short-termtraffic flow,this paper presents a fast and small-data-set method for calculatingthe largest Lyapunov exponent fromshort-termtraffic flowti me series based on cha-otic ti me series analysis,whichis used toidentify whether or not chaotic characteristics exit in the short-termtraffic flow.This method reconstructs the ti me series of the short ti me traffic flowin the phase space firstly,and the correlativeinformationinthe traffic flowis extracted richly.Furthermore,the method's results are val-idated using the Poincare section.Therefore,this method provides us a reliably theoretical basis for adoptingthe corresponding method to analyze,predict and control the short-termtraffic flow.Theidentifying results ofreal short-ti me traffic flowshowthat this method has many advantages,such as fast,good real-ti me,reliablefor small data sets and easy toi mplement.