Research on multilayer network structure characteristics from a higher-order model: The case of a Chinese high-speed railway system

Xie, Fengjie; Ma, Mengdi; Cuiping, Ren

doi:10.1016/j.physa.2021.126473

Cited by 12 publications

(10 citation statements)

References 40 publications

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“…This is a more common problem in the bus systems in our large cities [13] . Therefore, the study of interchanges at critical stops is an important element in the study of bus systems, which has also not been addressed in the study of high-speed rail systems [8] .…”

Section: Introductionmentioning

confidence: 99%

“…These studies are based on the assumption of pairwise relationships, i.e., it is assumed that the transmission between stops depends only on the current stops location and has no path dependency. In recent years, the higher-order models newly proposed in complex network theory focus on path dependencies [6,7] and gets a lot of attention in the field of transportation [8] . Scholars have used a higher-order network model to study Chinese high-speed rail system by constructing a higher-order high-speed rail network and stratifying it to study important cities and routes [8] .…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the higher-order models newly proposed in complex network theory focus on path dependencies [6,7] and gets a lot of attention in the field of transportation [8] . Scholars have used a higher-order network model to study Chinese high-speed rail system by constructing a higher-order high-speed rail network and stratifying it to study important cities and routes [8] . Compared with pairwise relational network modeling, higher-order network models can accurately capture important information about the data dependencies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on bus route optimization based on a higher-order network model

Xie,

Huang,

Ren

et al. 2024

Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024)

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on bus route optimization based on a higher-order network model

Xie,

Huang,

Ren

et al. 2024

Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024)

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“…In the current research, multilayer networks have been used in several academic fields. Examples include the financial stock market [33], transportation systems [34], and national energy trade [35]. The complexity of many bio-logical, social, and technological systems stems from the richness of the interactions among their units [36].…”

Section: Introductionmentioning

confidence: 99%

New Insight into the Coupled Grain–Disaster–Economy System Based on a Multilayer Network: An Empirical Study in China

Jun-li

Wang

et al. 2022

IJGI

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Natural disasters occur frequently causing huge economic losses and reduced grain production. Therefore, it is important to thoroughly explore the spatial correlations between grain, disaster, and the economy. Based on inter-provincial panel data in China in 2019, this study integrates complex network and co-occurrence theory into a coupled grain–disaster–economy (GDE) multilayer network, which provides a new perspective to further explore the spatial correlation between these three systems. We identify the spatial coupled characteristics of the GDE multilayer network using three aspects: degree, centrality, and community detection. The research results show the following: (1) Provinces in the major grain-producing regions have a stronger role in allocating and controlling grain resources, and the correlation between grain and disasters in these provinces is stronger and more prone to disasters. Whereas provinces in the Beijing–Tianjin–Hebei economic zone, and the Yangtze River Delta and Pearl River Delta economic zones, such as Beijing, Tianjin, Jiangsu, Shanghai, and Zhejiang, have a high level of economic development, thereby a stronger ability to allocate economic resources. (2) The economic subsystem assumes a more important, central role compared with the grain and disaster subsystems in the formation and development of the coupled GDE multilayer network, with a stronger coordination for the co-development between the complex grain, disaster, and economy systems in the nodal provinces of the network. (3) The community modularity of the coupled GDE multilayer network is significantly higher than that of the three single-layer networks, indicating a more reasonable community division after coupling the three subsystems. The identification of the spatial characteristics of GDE using multilayer network analysis offers a new perspective on taking various measures to improve the joint sustainable development of grain, disaster, and the economy in different regions of China according to local conditions.

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“…Based on this network, the dependence among accident factors can be analyzed, which is important for identifying key causal factors and higher-order interactions. Higher-order networks have been used in the Chinese high-speed railway system [ 38 ] and will be applied to more research fields.…”

Section: Introductionmentioning

confidence: 99%

Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory

Cuiping

Chen

Xie

et al. 2022

IJERPH

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In hazardous materials transportation systems, accident causation analysis is important to transportation safety. Complex network theory can be effectively used to understand the causal factors of and their relationships within accidents. In this paper, a higher-order network method is proposed to establish a hazardous materials transportation accident causation network (HMTACN), which considers the sequences and dependences of causal factors. The HMTACN is composed of 125 first- and 118 higher-order nodes that represent causes, and 545 directed edges that denote complex relationships among causes. By analyzing topological properties, the results show that the HMTACN has the characteristics of small-world networks and displays the properties of scale-free networks. Additionally, critical causal factors and key relationships of the HMTACN are discovered. Moreover, unsafe tank or valve states are important causal factors; and leakage, roll-over, collision, and fire are most likely to trigger chain reactions. Important higher-order nodes are discovered, which can represent key relationships in the HMTACN. For example, unsafe distance and improper operation usually lead to collision and roll-over. These results of higher-order nodes cannot be found by the traditional Markov network model. This study provides a practical way to extract and construct an accident causation network from numerous accident investigation reports. It also provides insights into safety management of hazardous materials transportation.

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Research on multilayer network structure characteristics from a higher-order model: The case of a Chinese high-speed railway system

Cited by 12 publications

References 40 publications

Research on bus route optimization based on a higher-order network model

Research on bus route optimization based on a higher-order network model

New Insight into the Coupled Grain–Disaster–Economy System Based on a Multilayer Network: An Empirical Study in China

Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory

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