IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i24p4695-d1000055.html
   My bibliography  Save this article

Critical Percolation on Temporal High-Speed Railway Networks

Author

Listed:
  • Yi Liu

    (Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
    Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Senbin Yu

    (Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
    College of Engineering, Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China)

  • Chaoyang Zhang

    (Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
    Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Peiran Zhang

    (Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
    Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Yang Wang

    (School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China)

  • Liang Gao

    (Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
    Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Deeply understanding the dynamic operating characteristics of high-speed railway (HSR) systems is of essential significance in theory and practice for the planning, construction, and operational management of HSR systems. In this paper, the HSR system is described as a temporal network, and the evolution of connected clusters in the system is considered as a percolation process. The critical integration time T c of the percolation process can determine the formation of a globally connected cluster and measure the transport performance of the HSR system. The appearance time of critical edges identified at T c can significantly affect the reliability of the transport performance of an HSR system. Compared to random percolation in the static HSR network, it can be found that the critical fraction p c of the percolation process in a temporal HSR network is almost always larger. This indicates that the global connectivity and the transport performance of HSR systems is overestimated by the static network abstraction. This paper provides a promising way of understanding the dynamic characteristics of HSR systems, evaluating their transport performance, and improving their reliability.

Suggested Citation

  • Yi Liu & Senbin Yu & Chaoyang Zhang & Peiran Zhang & Yang Wang & Liang Gao, 2022. "Critical Percolation on Temporal High-Speed Railway Networks," Mathematics, MDPI, vol. 10(24), pages 1-8, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4695-:d:1000055
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/24/4695/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/24/4695/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Zhaojing & Jia, Limin & Ma, Xiaoping & Sun, Xuehui & Tang, Qianxue & Qian, Sina, 2022. "Accessibility-oriented performance evaluation of high-speed railways using a three-layer network model," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Li, Qiaoru & Zhang, Zhe & Li, Kun & Chen, Liang & Wei, Zhenlin & Zhang, Jingchun, 2020. "Evolutionary dynamics of traveling behavior in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    3. Wang, Ru & Tan, Jiang-Xia & Wang, Xin & Wang, Du-Juan & Cai, Xu, 2008. "Geographic coarse graining analysis of the railway network of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5639-5646.
    4. Wang, Wei & Cai, Kaiquan & Du, Wenbo & Wu, Xin & Tong, Lu (Carol) & Zhu, Xi & Cao, Xianbin, 2020. "Analysis of the Chinese railway system as a complex network," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    5. Hongkun Ma & Chengdong Yang, 2022. "Exponential Synchronization of Hyperbolic Complex Spatio-Temporal Networks with Multi-Weights," Mathematics, MDPI, vol. 10(14), pages 1-11, July.
    6. Zhang, Jianhua & Hu, Funian & Wang, Shuliang & Dai, Yang & Wang, Yixing, 2016. "Structural vulnerability and intervention of high speed railway networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 743-751.
    7. Liu, Weichen & Cao, Youhui & Wu, Wei & Guo, Jiaying, 2020. "Spatial impact analysis of trans-Yangtze highway fixed links: A case study of the Yangtze River Delta, China," Journal of Transport Geography, Elsevier, vol. 88(C).
    8. Jianxi Gao & Xueming Liu & Daqing Li & Shlomo Havlin, 2015. "Recent Progress on the Resilience of Complex Networks," Energies, MDPI, vol. 8(10), pages 1-24, October.
    9. Zhang, Hui & Cui, Houdun & Wang, Wei & Song, Wenbo, 2020. "Properties of Chinese railway network: Multilayer structures based on timetable data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    10. Zhang, Jianhua & Wang, Meng, 2019. "Transportation functionality vulnerability of urban rail transit networks based on movingblock: The case of Nanjing metro," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    11. Xie, Fengjie & Ma, Mengdi & Ren, Cuiping, 2022. "Research on multilayer network structure characteristics from a higher-order model: The case of a Chinese high-speed railway system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    12. Zeyu Wang & Leishan Zhou & Bin Guo & Xing Chen & Hanxiao Zhou, 2021. "An Efficient Hybrid Approach for Scheduling the Train Timetable for the Longer Distance High-Speed Railway," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xie, Fengjie & Ma, Mengdi & Ren, Cuiping, 2022. "Research on multilayer network structure characteristics from a higher-order model: The case of a Chinese high-speed railway system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    2. Wang, Longjian & Zheng, Shaoya & Wang, Yonggang & Wang, Longfei, 2021. "Identification of critical nodes in multimodal transportation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    3. Hu, Xinlei & Huang, Jie & Shi, Feng, 2022. "A robustness assessment with passenger flow data of high-speed rail network in China," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    4. Pan, Shouzheng & Yan, Hai & He, Jia & He, Zhengbing, 2021. "Vulnerability and resilience of transportation systems: A recent literature review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    5. Cuiping Ren & Bianbian Chen & Fengjie Xie & Xuan Zhao & Jiaqian Zhang & Xueyan Zhou, 2022. "Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory," IJERPH, MDPI, vol. 19(20), pages 1-13, October.
    6. Zhen, Lu & Zhang, Nianzu & Yang, Zhiyuan, 2023. "Integrated optimization for high-speed railway express system with multiple modes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    7. 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.
    8. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    9. Abedi, Amin & Gaudard, Ludovic & Romerio, Franco, 2019. "Review of major approaches to analyze vulnerability in power system," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 153-172.
    10. Tang, Zhixing & Huang, Shan & Zhu, Xinping & Pan, Weijun & Han, Songchen & Gong, Tingyu, 2023. "Research on the multilayer structure of flight delay in China air traffic network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    11. Shang, Qingxue & Guo, Xiaodong & Li, Jichao & Wang, Tao, 2022. "Post-earthquake health care service accessibility assessment framework and its application in a medium-sized city," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    12. Limsawasd, Charinee & Athigakunagorn, Nathee & Khathawatcharakun, Phattadon & Boonmee, Atiwat, 2022. "Skip-Stop Strategy Patterns optimization to enhance mass transit operation under physical distancing policy due to COVID-19 pandemic outbreak," Transport Policy, Elsevier, vol. 126(C), pages 225-238.
    13. Fauzan Hanif Jufri & Jun-Sung Kim & Jaesung Jung, 2017. "Analysis of Determinants of the Impact and the Grid Capability to Evaluate and Improve Grid Resilience from Extreme Weather Event," Energies, MDPI, vol. 10(11), pages 1-17, November.
    14. Rahimi-Golkhandan, Armin & Garvin, Michael J. & Brown, Bryan L., 2019. "Characterizing and measuring transportation infrastructure diversity through linkages with ecological stability theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 128(C), pages 114-130.
    15. Lei Che & Jiangang Xu & Hong Chen & Dongqi Sun & Bao Wang & Yunuo Zheng & Xuedi Yang & Zhongren Peng, 2022. "Evaluation of the Spatial Effect of Network Resilience in the Yangtze River Delta: An Integrated Framework for Regional Collaboration and Governance under Disruption," Land, MDPI, vol. 11(8), pages 1-20, August.
    16. Xue Lin & Caiqing Ma & Qianling Wang, 2023. "Dual Jitter Suppression Mechanism-Based Cooperation Control for Multiple High-Speed Trains with Parametric Uncertainty," Mathematics, MDPI, vol. 11(8), pages 1-16, April.
    17. Noguchi, Hiroki & Fuse, Masaaki, 2020. "Rethinking critical node problem for railway networks from the perspective of turn-back operation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    18. Hong, Liu & Ye, Bowen & Yan, Han & Zhang, Hui & Ouyang, Min & (Sean) He, Xiaozheng, 2019. "Spatiotemporal vulnerability analysis of railway systems with heterogeneous train flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 725-744.
    19. Wang, Wei & Li, Wenyao & Lin, Tao & Wu, Tao & Pan, Liming & Liu, Yanbing, 2022. "Generalized k-core percolation on higher-order dependent networks," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    20. Fang, Zheng & Tan, Xing & Liu, Genshuo & Zhou, Zijie & Pan, Yajia & Ahmed, Ammar & Zhang, Zutao, 2022. "A novel vibration energy harvesting system integrated with an inertial pendulum for zero-energy sensor applications in freight trains," Applied Energy, Elsevier, vol. 318(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4695-:d:1000055. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.