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Exploring the Hierarchical Structure of China’s Railway Network from 2008 to 2017

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  • Shiwei Lu

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Engineering and Technology Research Center of Urbanization, Wuhan 430074, China)

  • Yaping Huang

    (School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Engineering and Technology Research Center of Urbanization, Wuhan 430074, China)

  • Zhiyuan Zhao

    (National & Local Joint Engineering Research Center of Geo-spatial Information Technology, Fuzhou University, Fuzhou 350002, China
    Key Laboratory of Spatial Data Mining & Information Sharing of MOE, Fuzhou 350002, China)

  • Xiping Yang

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
    Geomatics Technology and Application key Laboratory of Qinghai Province, Xining 810001, China)

Abstract

The analysis of transport networks is an important component of urban and regional development and planning. Based on the four main stages of China’s railway development from 2008 to 2017, this paper analyzes the hierarchical and spatial heterogeneity distribution of train flows. We found a high degree of spatial matching with the distribution of China’s main railway corridors. Then, using a classical community detection algorithm, this paper attempts to describe the functional structure and regional effects of China’s railway network. We also explore the impacts of construction policies and changes to train operations on the spatial organizing pattern and evolution of network hierarchies. The results of this empirical study reveal a clear pattern of independent communities, which in turn indicates the existence of a hierarchical structure in China’s railway network. The decreases in both the number of communities and average distance between community centers indicate that the newer high-speed rail services have shortened the connections between cities. In addition, the detected communities are inconsistent with China’s actual administrative divisions in terms of quantity and boundaries. The spatial spillover and segmentation effects cause the railway network in different regions to be self-contained. Finally, the detected communities in each stage can be divided into the categories of monocentric structure, dual-nuclei structure, and polycentric structure according to the number of extracted hubs. The polycentric structure is the dominant mode, which shows that the railway network has significant spatial dependence and a diversified spatial organization mode. This study has great significance for policymakers seeking to guide the future construction of high-speed rail lines and optimize national or regional railway networks.

Suggested Citation

  • Shiwei Lu & Yaping Huang & Zhiyuan Zhao & Xiping Yang, 2018. "Exploring the Hierarchical Structure of China’s Railway Network from 2008 to 2017," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3173-:d:167929
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    Cited by:

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    2. Yan Ma & Feng Xue, 2019. "Deciphering the Spatial Structures of City Networks in the Economic Zone of the West Side of the Taiwan Strait through the Lens of Functional and Innovation Networks," Sustainability, MDPI, vol. 11(10), pages 1-20, May.
    3. Sairong Peng & Xin Yang & Hongwei Wang & Hairong Dong & Bin Ning & Haichuan Tang & Zhipeng Ying & Ruijun Tang, 2019. "Dispatching High-Speed Rail Trains via Utilizing the Reverse Direction Track: Adaptive Rescheduling Strategies and Application," Sustainability, MDPI, vol. 11(8), pages 1-20, April.
    4. Liu, Shuli & Wan, Yulai & Zhang, Anming, 2020. "Does China’s high-speed rail development lead to regional disparities? A network perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 299-321.
    5. Rui Ding & Jun Fu & Yiming Du & Linyu Du & Tao Zhou & Yilin Zhang & Siwei Shen & Yuqi Zhu & Shihui Chen, 2022. "Structural Evolution and Community Detection of China Rail Transit Route Network," Sustainability, MDPI, vol. 14(19), pages 1-19, September.

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