• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 27 Issue 4
Jul.  2014
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Article Contents
HU Yucong, CHEN Haiwei. Algorithm for Detecting Modular Structures and Diagnosing Hub Sections in Urban Road Network[J]. Journal of Southwest Jiaotong University, 2014, 27(4): 706-711. doi: 10.3969/j.issn.0258-2724.2014.04.023
Citation: HU Yucong, CHEN Haiwei. Algorithm for Detecting Modular Structures and Diagnosing Hub Sections in Urban Road Network[J]. Journal of Southwest Jiaotong University, 2014, 27(4): 706-711. doi: 10.3969/j.issn.0258-2724.2014.04.023

Algorithm for Detecting Modular Structures and Diagnosing Hub Sections in Urban Road Network

doi: 10.3969/j.issn.0258-2724.2014.04.023
  • Received Date: 25 Nov 2012
  • Publish Date: 25 Aug 2014
  • In order to detect the complexities of topology and discover the key road sections in urban road network, the clustering feature of urban road network was analyzed by modular structure theory, and a GN-T algorithm was proposed for dividing the modular structures and diagnosing hub sections in the urban road network. By iterative removal of links with the maximum intermediate values from road network, this algorithm split the whole network into modular structures and found out hub sections. In addition, an improved modularity function was also proposed for determining the optimal number of modular structures in the urban road network. As a case study, the urban road network of Wuchang city was used to test and verify the algorithm. The results show that the maximal value of modularity in the network is 0.41, indicating that the urban road network of Wuchang city possesses obvious modular structure characteristics. In addition, the hub sections derived from the algorithm is consistent with the reality. All these demonstrate the effectiveness and practicability of the GN-T algorithm.

     

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