IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v224y2022ics0951832022002113.html
   My bibliography  Save this article

Quantitative analysis of freight train derailment severity with structured and unstructured data

Author

Listed:
  • Song, Bing
  • Zhang, Zhipeng
  • Qin, Yong
  • Liu, Xiang
  • Hu, Hao

Abstract

Train safety has been a top priority in the railroad industry. Understanding accident risks is of paramount importance for prioritizing effective prevention strategies. Previous work has focused on estimating the severity of derailments and various statistical models based on structured data were used. However, unstructured data records which provide considerable information about train derailments have received minimal consideration due to a lack of procedures of processing and interpreting them. To narrow this knowledge gap, this study aims to quantitatively estimate derailment severity by considering unstructured data utilizing topic modeling. A statistical model that integrates both structured and unstructured data was established to analyze U.S. freight train derailments from 1996 to 2019. The comparative results of predictions revealed that the model with combined text information outperformed the one without the unstructured data.Quantile regression was also developed to assess various statistical distributions of derailment severity. Both models with unstructured data provide a deeper understanding of derailment severity and ultimately improve railroad safety performance.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:reensy:v:224:y:2022:i:c:s0951832022002113
    DOI: 10.1016/j.ress.2022.108563
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022002113
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2022.108563?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. Sedghi, Mahdieh & Kauppila, Osmo & Bergquist, Bjarne & Vanhatalo, Erik & Kulahci, Murat, 2021. "A taxonomy of railway track maintenance planning and scheduling: A review and research trends," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Zhipeng Zhang & Xiang Liu, 2020. "Safety risk analysis of restricted-speed train accidents in the United States," Journal of Risk Research, Taylor & Francis Journals, vol. 23(9), pages 1158-1176, September.
    3. 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).
    4. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    5. Bellè, Andrea & Zeng, Zhiguo & Duval, Carole & Sango, Marc & Barros, Anne, 2022. "Modeling and vulnerability analysis of interdependent railway and power networks: Application to British test systems," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Torossian, Léonard & Picheny, Victor & Faivre, Robert & Garivier, Aurélien, 2020. "A review on quantile regression for stochastic computer experiments," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    7. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    8. Boakye, Jessica & Guidotti, Roberto & Gardoni, Paolo & Murphy, Colleen, 2022. "The role of transportation infrastructure on the impact of natural hazards on communities," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. 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).
    10. Dindar, Serdar & Kaewunruen, Sakdirat & An, Min, 2020. "Bayesian network-based human error reliability assessment of derailments," Reliability Engineering and System Safety, Elsevier, vol. 197(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. Li, Haoqian & Wang, Yong & Zeng, Jing & Li, Fansong & Yang, Zhenhuan & Mei, Guiming & Ye, Yunguang, 2024. "Virtual point tracking method for online detection of relative wheel-rail displacement of railway vehicles," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    2. 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).
    3. 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).
    4. Valcamonico, Dario & Baraldi, Piero & Zio, Enrico & Decarli, Luca & Crivellari, Anna & Rosa, Laura La, 2024. "Combining natural language processing and bayesian networks for the probabilistic estimation of the severity of process safety events in hydrocarbon production assets," Reliability Engineering and System Safety, Elsevier, vol. 241(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. Zhang, Jianhua & Min, Qinjie & Zhou, Yu & Cheng, Lilai, 2024. "Vulnerability assessments of urban rail transit networks based on extended coupled map lattices with evacuation capability," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. 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).
    3. Wen, Tao & Gao, Qiuya & Chen, Yu-wang & Cheong, Kang Hao, 2022. "Exploring the vulnerability of transportation networks by entropy: A case study of Asia–Europe maritime transportation network," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. 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).
    5. Gao, Lu & Lu, Pan & Ren, Yihao, 2021. "A deep learning approach for imbalanced crash data in predicting highway-rail grade crossings accidents," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Dong, Shangjia & Gao, Xinyu & Mostafavi, Ali & Gao, Jianxi & Gangwal, Utkarsh, 2023. "Characterizing resilience of flood-disrupted dynamic transportation network through the lens of link reliability and stability," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. Wallius, Eetu & Klock, Ana Carolina Tomé & Hamari, Juho, 2022. "Playing it safe: A literature review and research agenda on motivational technologies in transportation safety," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    8. Chai, Ming & Zhang, Xinyi & Schlingloff, Bernd-Holger & Tang, Tao & Liu, Hongjie, 2024. "Online hazard prediction of train operations with parametric hybrid automata based runtime verification," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    9. Liu, Yanyan & Li, Keping & Yan, Dongyang, 2024. "Quantification analysis of potential risk in railway accidents: A new random walk based approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    10. Najaf, Pooya & Thill, Jean-Claude & Zhang, Wenjia & Fields, Milton Greg, 2018. "City-level urban form and traffic safety: A structural equation modeling analysis of direct and indirect effects," Journal of Transport Geography, Elsevier, vol. 69(C), pages 257-270.
    11. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    12. 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).
    13. Pei, Shunshun & Zhai, Changhai & Hu, Jie, 2024. "Surrogate model-assisted seismic resilience assessment of the interdependent transportation and healthcare system considering a two-stage recovery strategy," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    14. Tang, Daogui & Fang, Yi-Ping & Zio, Enrico, 2023. "Vulnerability analysis of demand-response with renewable energy integration in smart grids to cyber attacks and online detection methods," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    15. Kurmankhojayev, Daniyar & Li, Guoyuan & Chen, Anthony, 2024. "Link criticality index: Refinement, framework extension, and a case study," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    16. Wang, Hongping & Fang, Yi-Ping & Zio, Enrico, 2022. "Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    17. Bo Yang & Yao Wu & Weihua Zhang & Jie Bao, 2020. "Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    18. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    19. Liu, Jiale & Wang, Huan, 2024. "A brain-inspired energy-efficient Wide Spiking Residual Attention Framework for intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    20. Wang, Jie & Zhang, Yangyi & Li, Shunlong & Xu, Wencheng & Jin, Yao, 2024. "Directed network-based connectivity probability evaluation for urban bridges," Reliability Engineering and System Safety, Elsevier, vol. 241(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:224:y:2022:i:c:s0951832022002113. 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.