短时交通流复杂动力学特性分析及预测  

Analysis and prediction of complex dynamical characteristics of short-term traffic flow

作　　者：张洪宾[1,2] 孙小端[1] 贺玉龙[1] 

机构地区：[1]北京工业大学交通工程北京市重点实验室,北京100124 [2]德州学院汽车工程学院,德州253023

出　　处：《物理学报》2014年第4期51-58,共8页Acta Physica Sinica

基　　金：国家重点基础研究发展计划(批准号:2012CB723303);国家自然科学基金青年科学基金(批准号:51308058)资助的课题~~

摘　　要：为揭示短时交通流的内在动态特性,利用非线性方法对交通流混沌特性进行识别,为短时交通流的预测提供基础.基于混沌理论对交通流时间序列进行相空间重构,利用C-C算法计算时间延迟和嵌入维数,采用Grassberger-Procaccia算法计算吸引子关联维数,通过改进小数据量法计算最大Lyapunov指数来判别交通流时间序列的混沌特性.针对局域自适应预测方法在交通流多步预测中预测器系数无法调节的问题,提出了交通流多步自适应预测方法.通过实测数据计算,结果表明:2,4和5 min三种统计尺度的交通流时间序列均具有混沌特性;改进的小数据量法能够准确地计算出最大Lyapunov指数;构建的交通流多步自适应预测模型能够有效地预测交通流量的变化.为智能交通系统诱导和控制提供了依据.In order to reveal the internal dynamic property of short-term traffic flow, the nonlinear analysis method is used to identify the chaotic property of traffic flow which is the basis for the prediction of the traffic flow time series. Traffic flow time series is reconstructed in phase-space based on chaos theory. The embedding dimension and delay time are first calculated via the C-C method. The correlative dimension of attractor is then calculated with the Grassberger-Procaccia method. The largest Lyapunov exponent of traffic flow set is calculated on the basis of the improved small data set method to verify the presence of the chaos in traffic flow time series. A novel multi-step adaptive prediction method is proposed to solve the problem of adjusting the filter parameters of the chaos local adaptive prediction method during traffic flow multi-step prediction. The traffic flow time series are found to have chaotic properties in different statistical scales of 2, 4, and 5 min and show that the improved small data set method can accurately evaluate the chaotic property for traffic flow time series, and that the multi-step adaptive prediction method is capable of effectively predicting its fluctuation, which provides a useful reference for traffic guidance and control.

关 键 词：交通流量 混沌 最大LYAPUNOV指数 多步自适应预测 

分 类 号：U495[交通运输工程—交通运输规划与管理] U491.112[交通运输工程—道路与铁道工程]