摘要
提出了一种基于混沌分析的G-P(Grassberger-procaccia algorithm)算法将非平稳交通流参数时间序列近似转化为平稳时间序列的方法。首先采用自相关函数判断自由流状态、拥挤流状态和阻塞流状态下交通流基本参数时间序列的平稳性。然后应用G-P算法计算嵌入维,进行相空间重构,给出交通流参数时间序列平稳化方法。最后利用快速路交通流实测数据,对3种状态下非平稳的交通流参数时间序列的平稳化进行验证,结果表明:本文方法能够为交通流参数分析、拟合和预测提供科学合理的输入集。
Due to complicacy,randomness and nonlinearity of traffic system,traffic flow parameters are usually considered as the random time series.A method based on G-P algorithm that is helpful to convert the traffic flow time series to stationarity series was proposed.First,the autocorrelation function was adapted to evaluate the stationary of traffic flow under free traffic,congested traffic and jam traffic.Second,G-P algorithm was used to calculate the embedding dimension,reconstruct the phase space,and convert the traffic flow non-stationary parameters to stationary time series under 3 traffic states.At last,some examples illustrated the model and showed its practical applicability based on measured traffic flow data.The research can provide the input set for the traffic flow parameters analysis,fitting and prediction.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2012年第3期594-599,共6页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(51178032)
“973”国家重点基础研究发展计划项目(2006CB705500)
中国发展研究基金会2009年度“通用汽车.中国发展研究青年奖学金”项目
北京交通大学优秀博士生科技创新基金项目(141082522)
关键词
交通运输系统工程
自由流状态
拥挤流状态
阻塞流状态
G-P算法
相空间重构
engineering of communications and transportation
free traffic
congested traffic
jam traffic
G-P algorithm
phase space reconstruction
