2019
DOI: 10.1371/journal.pone.0211052
|View full text |Cite
|
Sign up to set email alerts
|

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

Abstract: 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 … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
15
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
9

Relationship

3
6

Authors

Journals

citations
Cited by 16 publications
(15 citation statements)
references
References 43 publications
0
15
0
Order By: Relevance
“…In recent years, the development of social sensing technology has produced a rich set of high-resolution data, allowing for the quantitative characterization of human activities in detail [22,23]. Many social sensing data (such as social media data, public transport data, and location-based service data) have been mined, giving rise to a range of interesting socioeconomic structures and dynamics that have been previously impossible to reveal [22,24]. One particular useful source is the Point of Interest (POI) data, which provides location-based geographic information and other detailed information for each geographic element on the map [25].…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, the development of social sensing technology has produced a rich set of high-resolution data, allowing for the quantitative characterization of human activities in detail [22,23]. Many social sensing data (such as social media data, public transport data, and location-based service data) have been mined, giving rise to a range of interesting socioeconomic structures and dynamics that have been previously impossible to reveal [22,24]. One particular useful source is the Point of Interest (POI) data, which provides location-based geographic information and other detailed information for each geographic element on the map [25].…”
Section: Introductionmentioning
confidence: 99%
“…In this static representation, two nodes are connected if there is at least a path between them. In spite of its extensive use in modelling complex transportation networks [7,[14][15][16], static representations can be too simplistic for some purposes, specially when computing and interpreting measures obtained from shortest paths among nodes, or evaluating connectivity between two nodes (i.e., assessing which pairs of nodes are connected by a path in the network). First, static representations are frequently unweighted.…”
Section: Introductionmentioning
confidence: 99%
“…Known network measures, such as various centrality measures [ 6 , 7 , 32 ], are purely based on network structure and often fail to address dynamic aspects of flow and time which are crucial when evaluating the real behaviour of the system and the related risks. To study these dynamics, we approximate a nonlinear high dimensional system with a series of linear operators and accordingly employ time series of systemic risk measures.…”
Section: Resultsmentioning
confidence: 99%