Hierarchical structure in the world's largest high-speed rail network

Presently, China has the largest high-speed rail (HSR) system in the world. However, our understanding of the network structure of the world's largest HSR system remains largely incomplete due to the limited data available. In this study, a publicly available data source, namely, information fr...

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Published in:PloS one 2019-02, Vol.14 (2), p.e0211052-e0211052
Main Authors: Wei, Sheng, Teng, Shuqing N, Li, Hui-Jia, Xu, Jiangang, Ma, Haitao, Luan, Xia-Li, Yang, Xuejiao, Shen, Da, Liu, Maosong, Huang, Zheng Y X, Xu, Chi
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Language:English
Subjects:
Analysis
Big Data
Biology and Life Sciences
China
Computer and Information Sciences
Engineering and Technology
Geography
High speed rail
High speed trains
Knowledge management
Life sciences
Models, Theoretical
Passenger rail services
Physical Sciences
Rail transportation
Railroads
Railway networks
Railway stations
Railways
Science Policy
Social networks
Social Sciences
Software
Structural hierarchy
Transportation systems
Urban planning
Web sites (World Wide Web)
Websites
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cited_by cdi_FETCH-LOGICAL-c692t-ec4df07fa2193775fe909d1ac28ea17839aa4639f645f2086682c3b5c0fc856a3
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creator Wei, Sheng
Teng, Shuqing N
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Huang, Zheng Y X
Xu, Chi
description Presently, China has the largest high-speed rail (HSR) system in the world. However, our understanding of the network structure of the world's largest HSR system remains largely incomplete due to the limited data available. In this study, a publicly available data source, namely, information from a ticketing website, was used to collect an exhaustive dataset on the stations and routes within the Chinese HSR system. The dataset included all 704 HSR stations that had been built as of June, 2016. A classical set of frequently used metrics based on complex network theory were analyzed, including degree centrality, betweenness centrality, and closeness centrality. The frequency distributions of all three metrics demonstrated highly consistent bimodal-like patterns, suggesting that the Chinese HSR network consists of two distinct regimes. The results indicate that the Chinese HSR system has a hierarchical structure, rather than a scale-free structure as has been commonly observed. To the best of our knowledge, such a network structure has not been found in other railway systems, or in transportation systems in general. Follow-up studies are needed to reveal the formation mechanisms of this hierarchical network structure.
doi_str_mv 10.1371/journal.pone.0211052
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However, our understanding of the network structure of the world's largest HSR system remains largely incomplete due to the limited data available. In this study, a publicly available data source, namely, information from a ticketing website, was used to collect an exhaustive dataset on the stations and routes within the Chinese HSR system. The dataset included all 704 HSR stations that had been built as of June, 2016. A classical set of frequently used metrics based on complex network theory were analyzed, including degree centrality, betweenness centrality, and closeness centrality. The frequency distributions of all three metrics demonstrated highly consistent bimodal-like patterns, suggesting that the Chinese HSR network consists of two distinct regimes. The results indicate that the Chinese HSR system has a hierarchical structure, rather than a scale-free structure as has been commonly observed. 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subjects Analysis
Big Data
Biology and Life Sciences
China
Computer and Information Sciences
Engineering and Technology
Geography
High speed rail
High speed trains
Knowledge management
Life sciences
Models, Theoretical
Passenger rail services
Physical Sciences
Rail transportation
Railroads
Railway networks
Railway stations
Railways
Science Policy
Social networks
Social Sciences
Software
Structural hierarchy
Transportation systems
Urban planning
Web sites (World Wide Web)
Websites
title Hierarchical structure in the world's largest high-speed rail network
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