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How to connect with each other between roads? An empirical study of urban road connection properties

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  • Wang, Shiguang
  • Yu, Dexin
  • Lin, Ciyun
  • Shang, Qiang
  • Lin, Yu

Abstract

This paper aims to discover how to connect with each other between roads from the perspective of degree–degreecorrelation in network science. Named street approach and stroke-like analysis were first combined to define the road from the perspective of the cognition of the residents. Then, we constructed a series of new measures to characterize urban road networks based on lane properties. We applied this method to the road network in Xiamen, China. Based on a standard method from statistical physics, we examined in more detail the distributions of these new measures and found that the original degree still conforms to the power-law distribution under this road definition. Then, a clear parity value classification was found in hierarchical measure, and both of them have a good performance in power-law fitting. For the improved degree and adjusted degree, their whole rounding estimates are in good agreement with the Gaussian distribution, and the power-law effect of their long tail is significant. In considering the connection properties of networks, no solo assortative or disassortative characteristic exists. Based on ANND (average nearest neighbour degree) and LC (link coefficient), they present a distinct segmentation property that is different from existing conclusions.

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  • Wang, Shiguang & Yu, Dexin & Lin, Ciyun & Shang, Qiang & Lin, Yu, 2018. "How to connect with each other between roads? An empirical study of urban road connection properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 775-787.
    • Handle: RePEc:eee:phsmap:v:512:y:2018:i:c:p:775-787
    DOI: 10.1016/j.physa.2018.08.115
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    2. Wang, Shiguang & Yu, Dexin & Kwan, Mei-Po & Zheng, Lili & Miao, Hongzhi & Li, Yongxing, 2020. "The impacts of road network density on motor vehicle travel: An empirical study of Chinese cities based on network theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 144-156.
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    4. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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