ACK-Means Clustering of Expressway Congested Segments Based on Traffic Congested Information
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NETL
NSTL
万方数据
为了充分利用实际高速公路路段交通拥堵信息,更合理地聚类交通拥堵的内在规律和特征变化,提出自适应确定聚类中心C和类别K值(adaptive center andK-means value,ACK-Means)的聚类算法,进行高速公路拥堵路段聚类.ACK-Means算法借助簇类密度、簇类间距以及簇类强度,同时又考虑到数据样本的偶然性,对离群点进行合理分配,ACK-Means算法可实现自适应确定聚类中心C和类别K值.基于实际交通拥堵信息构建数据集,Python编程实现高速公路拥堵路段ACK-Means聚类,巧妙解决了高速公路拥堵路段聚类数目K和聚类中心C设定问题.聚类结果表明,ACK-Means算法实现高速公路拥堵路段无监督聚类,聚类结果完全基于实际的高速公路交通拥堵信息,具有更高的实用性.
In order to make full use of the actual expressway traffic congested information and to cluster the internal laws and characteristic changes of the traffic congestion more reasonably,an adaptive center and K-Means value(ACK-Means)clustering algorithm to cluster congested segments of expressways was proposed.ACK-Means algorithm assigns outliers reasonably by means of cluster density,cluster spacing and cluster strength,and considering the contingency of data samples.The ACK-Means algorithm determine the initial cluster center C and the number of clusters K-value adaptively.The data set was constructed based on the actual expressway congested information,and the ACK-Means algorithm was implemented by Python programming to cluster the expressway congested segments.The problem of clustering number K and initial clustering center C setting for the expressway congested segments was solved skillfully.Clustering results show that the ACK-Means algorithm realizes the unsupervised clustering of expressway congested segments,and the clustering results are based on the actual expressway congested information,which has higher practicability.
traffic congestion clusteringACK-Means algorithmadaptive clustering centeradaptive K valuetraffic congested information
NETL
NSTL
万方数据
