IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v233y2023ics0951832023000479.html
   My bibliography  Save this article

An improved density peaks clustering algorithm based on k nearest neighbors and turning point for evaluating the severity of railway accidents

Author

Listed:
  • Shi, Lingyuan
  • Yang, Xin
  • Chang, Ximing
  • Wu, Jianjun
  • Sun, Huijun

Abstract

The timely and scientific assessment of railway accident severity can provide effective support for making a suitable rescue plan. This paper proposes an improved density peaks clustering algorithm based on scenario construction for dynamically tackling the severity ranking problem of railway accidents. Firstly, the scenario is introduced into the selection of grading indicators of railway accidents, and a five-dimensional railway accident scenario expression model based on knowledge element is proposed to dynamically select and express the grading indicators. Secondly, an improved density peaks clustering algorithm based on k nearest neighbors and turning point (IDPC-KNN-TP) as the grading model of railway accidents is established, which optimizes the local density measurement, the selection of cluster center points, and the assignment strategy of density peaks clustering (DPC). Finally, the railway accidents from 2010 to 2021 in the UK as a case study are analyzed. The results show that the performance of IDPC-KNN-TP is better than others, and the grading algorithm in this paper has good practical performance. The dynamic grading strategy and the emergency disposal suggestions are put forward to provide a reference for the hierarchical response to railway accidents.

Suggested Citation

  • Shi, Lingyuan & Yang, Xin & Chang, Ximing & Wu, Jianjun & Sun, Huijun, 2023. "An improved density peaks clustering algorithm based on k nearest neighbors and turning point for evaluating the severity of railway accidents," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:reensy:v:233:y:2023:i:c:s0951832023000479
    DOI: 10.1016/j.ress.2023.109132
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023000479
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109132?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sami Naouali & Semeh Ben Salem & Zied Chtourou, 2020. "Clustering Categorical Data: A Survey," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 49-96, February.
    2. Wang, Likun & Yang, Zaili, 2018. "Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 277-289.
    3. Feng, Jian Rui & Yu, Guanghui & Zhao, Mengke & Zhang, Jiaqing & Lu, Shouxiang, 2022. "Dynamic risk assessment framework for industrial systems based on accidents chain theory: The case study of fire and explosion risk of UHV converter transformer," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. Yanlan Mei & Yan Tu & Kefan Xie & Yicheng Ye & Wenjing Shen, 2019. "Internet Public Opinion Risk Grading under Emergency Event Based on AHPSort II-DEMATEL," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    5. Rungskunroch, Panrawee & Jack, Anson & Kaewunruen, Sakdirat, 2021. "Benchmarking on railway safety performance using Bayesian inference, decision tree and petri-net techniques based on long-term accidental data sets," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Li, Baode & Lu, Jing & Li, Jing & Zhu, Xuebin & Huang, Chuan & Su, Wan, 2022. "Scenario evolutionary analysis for maritime emergencies using an ensemble belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Xuanhua Xu & Yanxia Huang & Ke Chen, 2019. "Method for large group emergency decision making with complex preferences based on emergency similarity and interval consistency," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(1), pages 45-64, May.
    8. Zhou, Xiaoyi & Lu, Pan & Zheng, Zijian & Tolliver, Denver & Keramati, Amin, 2020. "Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    9. Song, Bing & Zhang, Zhipeng & Qin, Yong & Liu, Xiang & Hu, Hao, 2022. "Quantitative analysis of freight train derailment severity with structured and unstructured data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    10. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2019. "A framework for dynamic risk assessment with condition monitoring data and inspection data," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    11. Jiang, Jianhua & Chen, Yujun & Meng, Xianqiu & Wang, Limin & Li, Keqin, 2019. "A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 702-713.
    12. Delu Wang & Yadong Wang, 2021. "Emergency Capacity of Small Towns to Endure Sudden Environmental Pollution Accidents: Construction and Application of an Evaluation Model," Sustainability, MDPI, vol. 13(10), pages 1-34, May.
    13. Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu, Jingbao & Sun, Wentao & Li, Shanyou & Yao, Kunpeng & Song, Jindong, 2024. "Threshold-based earthquake early warning for high-speed railways using deep learning," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    2. Liu, Jintao & Chen, Keyi & Duan, Huayu & Li, Chenling, 2024. "A knowledge graph-based hazard prediction approach for preventing railway operational accidents," Reliability Engineering and System Safety, Elsevier, vol. 247(C).

    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. Tan, Qiong & Fu, Ming & Wang, Zhengxing & Yuan, Hongyong & Sun, Jinhua, 2024. "A real-time early warning classification method for natural gas leakage based on random forest," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    2. Singh, Prashant & Pasha, Junayed & Moses, Ren & Sobanjo, John & Ozguven, Eren E. & Dulebenets, Maxim A., 2022. "Development of exact and heuristic optimization methods for safety improvement projects at level crossings under conflicting objectives," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Liu, Jinbiao & Tan, Lingling & Ma, Yaping, 2024. "An integrated risk assessment method for urban areas due to chemical leakage accidents," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    4. Li, Bokang & Afkhami, Payam & Khayamim, Razieh & Elmi, Zeinab & Moses, Ren & Sobanjo, John & Ozguven, Eren E. & Dulebenets, Maxim A., 2024. "A holistic optimization-based approach for sustainable selection of level crossings for closure with safety, economic, and environmental considerations," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    5. Feng, Jian Rui & Yu, Guanghui & Zhao, Mengke & Zhang, Jiaqing & Lu, Shouxiang, 2022. "Dynamic risk assessment framework for industrial systems based on accidents chain theory: The case study of fire and explosion risk of UHV converter transformer," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    7. Alsulieman, Abdullah & Ge, Xihe & Zeng, Zhiguo & Butenko, Sergiy & Khan, Faisal & El-Halwagi, Mahmoud, 2024. "Dynamic risk analysis of evolving scenarios in oil and gas separator," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    8. Wu, Xingguang & Huang, Huirong & Xie, Jianyu & Lu, Meixing & Wang, Shaobo & Li, Wang & Huang, Yixuan & Yu, Weichao & Sun, Xiaobo, 2023. "A novel dynamic risk assessment method for the petrochemical industry using bow-tie analysis and Bayesian network analysis method based on the methodological framework of ARAMIS project," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    9. Chen, Xiyuan & Ma, Xiaoping & Jia, Limin & Zhang, Zhipeng & Chen, Fei & Wang, Ruojin, 2024. "Causative analysis of freight railway accident in specific scenes using a data-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    10. Hou, Hui & Liu, Chao & Wei, Ruizeng & He, Huan & Wang, Lei & Li, Weibo, 2023. "Outage duration prediction under typhoon disaster with stacking ensemble learning," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    11. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    12. Jiang, Meizhi & Lu, Jing, 2020. "The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 139(C).
    13. Dindar, Serdar & Kaewunruen, Sakdirat & An, Min, 2022. "A hierarchical Bayesian-based model for hazard analysis of climate effect on failures of railway turnout components," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    14. Edward Kozłowski & Anna Borucka & Andrzej Świderski & Przemysław Skoczyński, 2021. "Classification Trees in the Assessment of the Road–Railway Accidents Mortality," Energies, MDPI, vol. 14(12), pages 1-15, June.
    15. Li, Huanhuan & Çelik, Cihad & Bashir, Musa & Zou, Lu & Yang, Zaili, 2024. "Incorporation of a global perspective into data-driven analysis of maritime collision accident risk," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    16. Lin Wang & Yuping Xing, 2022. "Risk Assessment of a Coupled Natural Gas and Electricity Market Considering Dual Interactions: A System Dynamics Model," Energies, MDPI, vol. 16(1), pages 1-18, December.
    17. He, Rui & Zhu, Jingyu & Chen, Guoming & Tian, Zhigang, 2022. "A real-time probabilistic risk assessment method for the petrochemical industry based on data monitoring," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    18. Tang, Ming & Liao, Huchang, 2021. "From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey," Omega, Elsevier, vol. 100(C).
    19. Hendalianpour, Ayad & Liu, Peide & Amirghodsi, Sirous & Hamzehlou, Mohammad, 2022. "Designing a System Dynamics model to simulate criteria affecting oil and gas development contracts," Resources Policy, Elsevier, vol. 78(C).
    20. Ferretto, D. & Mazzini, G. & Ambrosini, W. & Aldorf, R. & Hrehor, M., 2021. "Risk monitor implementation for the LVR-15 research reactor," Reliability Engineering and System Safety, Elsevier, vol. 208(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:eee:reensy:v:233:y:2023:i:c:s0951832023000479. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.