Pattern Extraction and Temporal Evolution of Short-Term Traffic Volume
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摘要: 提出了一种基于三步策略的方法来提取交通流模式并对其时变特征进行分析。首先,根据交通流量内部变化将其分割成非等长的一系列具有明显物理意义的子序列;然后,利用定量递归分析(recurrence quantification analysis,RQA)提取各子序列的统计参数;最后,通过聚类获得交通流典型模式。实验结果表明,此方法能有效提取交通流中隐含的4种模式及它们在全天中的时间分布和时变特征,以及它们在工作日和非工作日时态分布上的差异。Abstract: Traffic volume patterns and their temporal evolution are one of the most important issues for traffic prediction and traffic condition estimation.However,little work has been conducted on identifying and associating traffic pattern occurrence with prevailing traffic conditions.In order to extract the patterns hidden in traffic volume fluctuation as well as their temporal evolution,we propose a three-layer strategy that first segments the volume into subsequences.Then,we use the recurrence qualification analysis to determine the statistical characteristics of the subsequences and the k-means clustering is used to get the hidden traffic patterns finally.A case study using three typical weekly traffic volume data acquired from a freeway in Minnesota of USA shows that the proposed method is useful for identification of the traffic pattern,and traffic prediction as well.
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