测绘学报 ›› 2014, Vol. 43 ›› Issue (1): 97-104.

• 学术论文 • 上一篇    下一篇

对偶图节点重要度的道路网自动选取方法

刘刚1,李永树1,杨骏张喜平1   

  • 收稿日期:2013-01-21 修回日期:2013-12-03 出版日期:2014-01-20 发布日期:2014-01-20
  • 通讯作者: 刘刚 E-mail:liuganggis@sina.com
  • 基金资助:

    高等学校博士学科点专项科研基金;2013年西南交通大学博士研究生创新基金;2013年西南交通大学优秀博士学位论文培育项目;中央高校基本科研业务费专项资金;自然科学基金项目;中国博士后科学基金面上项目;中央高校基本科研业务费科技创新项目;重庆市教委科学技术研究项目(自然科学类)

Auto-selection Method of Road Networks Based on Evaluation of Node Importance for Complex Traffic Network

  • Received:2013-01-21 Revised:2013-12-03 Online:2014-01-20 Published:2014-01-20

摘要:

采用对偶拓扑方法构建基于广义路网拓扑的复杂交通网络,通过引入m阶邻居节点概念,考虑复杂交通网络中节点度、介中心及节点间距离等因素,顾及节点自身及1到m阶邻居节点的重要度贡献,定义了节点重要度评价模型,进而提出一种基于m阶邻居节点重要度贡献的道路网自动选取方法,并基于网络最小规模原则保持选取道路的连通性。为检验方法的有效性,引入度分布评价所选取道路对路网整体结构及拓扑特性的保持程度,并采用实际城市路网进行试验,结果表明本文方法选取的路网能较好地保持了原始路网的整体结构、拓扑特性及道路连通性,且算法稳定、可靠。

关键词: 地图综合, 道路选取, 重要度贡献, 介中心,

Abstract:

The complex traffic network is constructed based on the generalized topological road network by using dual topology method. By introducing the concept of m-order neighbors and taking into account the factors of the node’s degree, betweenness centrality and distance on the complex traffic network, we consider the importance contributions of the node-self and first to m-order neighbors and define the evaluation model for node importance. Based on this, a road selection process based on the importance contribution of m-order neighbors is proposed, and the selection set is guaranteed to be connected through adopting the minimization principle to network size. In order to verify the efficiency of this process, the degree distribution is introduced to evaluate the level of maintaining the global structure and topological characteristics of road network, and real urban road network is used for experiments of selection. The results show that this road selection process can maintain the global structure of the original road network, maintain the topological characteristics and keep the selected road network well connected, and also this method is stable and reliable.

Key words: map generalization, road selection, importance contribution, betweenness centrality, degree

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