基于蚂蚁聚类算法的K-means算法在公路交通枢纽宏观规划中的应用

Yan Meng, Xiyu Liu
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引用次数: 8

摘要

公路交通的发展有效地促进了我国经济的持续快速发展。但公路和交通枢纽建设呈现出不平衡的本质。因此,必须采用聚类分析方法对高速公路主要枢纽城市进行聚类,然后进行层次划分,以便进行功能分析。K-means算法是聚类分析中应用最广泛的算法,它的聚类数和初始聚类中心都是不确定的。本文提出了基于蚁群算法的K-means算法在公路交通枢纽宏观规划中的应用。实验结果表明,该算法比k均值算法和蚂蚁聚类算法能更有效地解决聚类问题。
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Application of K-means Algorithm Based on Ant Clustering Algorithm in Macroscopic Planning of Highway Transportation Hub
Development of highway transportation promotes sustainable and rapid development in economy of our country effectively. But construction of highway and transportation hub shows the nature of unbalance. So highway main hub cities must be clustered using cluster analysis, and then divided level in order to functional analyze. K-means algorithm is the most widely used algorithm in clustering analysis, which clustering numbers and initial clustering centers are uncertain. This paper proposes application of K-means algorithm in macroscopic planning of highway transportation hub based on ant clustering algorithm. The experimental results show this algorithm can more effectively solved clustering problem than K-means algorithm and ant clustering algorithm.
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