Structure and robustness of China’s railway transport network

ABSTRACT

Railway is a fundamental transportation mode for medium and long-distance travel in China. China’s railway transport network (CRTN) has become increasingly complex. Clarifying the structure of the CRTN and its robustness to failures is important for ensuring safe operations. This paper uses train schedule data to construct a railway physical network (RPN) and a train service network (TSN), and proposes a method to simulate the CRTN change processes under different attack strategies, clarify its robustness, and identify its backbone. The results show: First, the RPN is a typical scale-free and small-world network, while the TSN presents a complex hierarchical structure; Second, the RPN is robust to random attacks but vulnerable to targeted attacks, and attacks based on the betweenness centrality as evaluated in the RPN is the most effective mode; Third, the backbone network consists 62 cities including Beijing, Tianjin, and Shijiazhuang.

KEYWORDS:

• China’s railway transport network (CRTN)
• network robustness
• network attack simulation
• topological structure
• trunk network

Acknowledgments

The authors wish to thank the editors and reviewers for their detailed and valuable comments on an earlier version of this paper, which helped significantly improve its quality. Thanks to Degang Zhang from the Aerospace Information Research Institute and Guangchao Liu from Wuhan University for their help with the programming and codes for this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability

The data used to support the findings of this study are available from China Railway Corporation (https://www.12306.cn/index/).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China, the Second Tibetan Plateau Scientific Expedition and Research Program (STEP), and the Strategic Priority Research Program of the Chinese Academy of Sciences [42071153, 2019QZKK1007, XDA20080000].