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短时交通流复杂动力学特性分析及预测

发布时间:2016-11-05 11:05

  本文关键词:短时交通流复杂动力学特性分析及预测,,由笔耕文化传播整理发布。


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5min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.

Abstract

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.

收稿日期:2013-07-29

基金资助

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

Project supported by the National Basic Research Program of China (Grant No. 2012CB723303) and the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 51308058).

引用本文

[中文] 张洪宾, 孙小端, 贺玉龙. 短时交通流复杂动力学特性分析及预测[J]. 物理学报, 2014, 63(4): 040505. [英文] Zhang Hong-Bin, Sun Xiao-Duan, He Yu-Long. Analysis and prediction of complex dynamical characteristics of short-term traffic flow[J]. Acta Phys. Sin., 2014, 63(4): 040505.

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PACS

  • 05.45.-a (Nonlinear dynamics and chaos)
  • 01.75.+m (Science and society)
  • 05.45.Tp (Time series analysis)
  • 本文作者 阅读笔记

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      本文关键词:短时交通流复杂动力学特性分析及预测,由笔耕文化传播整理发布。



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