IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v204y2020ics0951832020307213.html
   My bibliography  Save this article

Railway dangerous goods transportation system risk analysis: An Interpretive Structural Modeling and Bayesian Network combining approach

Author

Listed:
  • Huang, Wencheng
  • Zhang, Yue
  • Kou, Xingyi
  • Yin, Dezhi
  • Mi, Rongwei
  • Li, Linqing

Abstract

In this paper, an Interpretive Structural Modeling (ISM) and Bayesian Network (BN) combining approach is applied to analyze the relationships and interaction strengths among the risk factors or accident causes of railway dangerous goods transportation system (RDGTS), quantitatively. According to the statistical data, 17 sub-indicators are concluded and applied as the nodes in BN. Based on the 17 sub-indicators and with the help of experts, the combined ISM and BN approach is used to analyze the system risk with seven steps: establish the relation matrix, calculate the reachability matrix, divide the reachability matrix into different levels, form the directed graph, form the Bayesian Network, establish the prior marginal and conditional probabilities of the sub-indicators in the BN, causal reasoning for the BN and obtain the final probabilities of occurrence for all sub-indicators. The conditional probability tables (CPTs) are used to express the relationships and interaction strengths among the nodes and the parent node variables. The final analysis results show that the sub-indicator, staffs are lack of technical and knowledge during transportation processes, has the highest impact to the RDGTS with probability 0.74, and the sudden natural disaster has the weakest impact to the RDGTS with probability 0.05.

Suggested Citation

  • Huang, Wencheng & Zhang, Yue & Kou, Xingyi & Yin, Dezhi & Mi, Rongwei & Li, Linqing, 2020. "Railway dangerous goods transportation system risk analysis: An Interpretive Structural Modeling and Bayesian Network combining approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020307213
    DOI: 10.1016/j.ress.2020.107220
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832020307213
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2020.107220?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. Pfohl, Hans-Christian & Gallus, Philipp & Thomas, David, 2011. "Interpretive structural modeling of supply chain risks," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 55230, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Jabbarzadeh, Armin & Azad, Nader & Verma, Manish, 2020. "An optimization approach to planning rail hazmat shipments in the presence of random disruptions," Omega, Elsevier, vol. 96(C).
    3. Hazelton, Martin L., 2010. "Bayesian inference for network-based models with a linear inverse structure," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 674-685, June.
    4. Lee, Minseo & Sohn, Keemin, 2015. "Inferring the route-use patterns of metro passengers based only on travel-time data within a Bayesian framework using a reversible-jump Markov chain Monte Carlo (MCMC) simulation," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 1-17.
    5. Sajid, Zaman & Khan, Faisal & Zhang, Yan, 2017. "Integration of interpretive structural modelling with Bayesian network for biodiesel performance analysis," Renewable Energy, Elsevier, vol. 107(C), pages 194-203.
    6. Hosseini, S. Davod & Verma, Manish, 2018. "Conditional value-at-risk (CVaR) methodology to optimal train configuration and routing of rail hazmat shipments," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 79-103.
    7. Di Pietro, Laura & Guglielmetti Mugion, Roberta & Musella, Flaminia & Renzi, Maria Francesca & Vicard, Paola, 2017. "Monitoring an airport check-in process by using Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 235-247.
    8. Yang, Zhisen & Yang, Zaili & Yin, Jingbo, 2018. "Realising advanced risk-based port state control inspection using data-driven Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 38-56.
    9. Wu, Wei-Shing & Yang, Chen-Feng & Chang, Jung-Chuan & Château, Pierre-Alexandre & Chang, Yang-Chi, 2015. "Risk assessment by integrating interpretive structural modeling and Bayesian network, case of offshore pipeline project," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 515-524.
    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. Ballester-Ripoll, Rafael & Leonelli, Manuele, 2022. "Computing Sobol indices in probabilistic graphical models," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Gu, Shuang & Li, Keping & Feng, Tao & Yan, Dongyang & Liu, Yanyan, 2022. "The prediction of potential risk path in railway traffic events," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Li, Guoqi & Pu, Gang & Yang, Jiaxin & Jiang, Xinguo, 2024. "A multidimensional quantitative risk assessment framework for dense areas of stay points for urban HazMat vehicles," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Laihao Ma & Xiaoxue Ma & Jingwen Zhang & Qing Yang & Kai Wei, 2021. "Identifying the Weaker Function Links in the Hazardous Chemicals Road Transportation System in China," IJERPH, MDPI, vol. 18(13), pages 1-17, July.
    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. Huang, Wencheng & Zhang, Yue & Yin, Dezhi & Zuo, Borui & Liu, Zhanru, 2021. "Urban bus accident analysis: based on a Tropos Goal Risk-Accident Framework considering Learning From Incidents process," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. 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).
    8. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Izdebski, Mariusz & Jacyna-Gołda, Ilona & Gołda, Paweł, 2022. "Minimisation of the probability of serious road accidents in the transport of dangerous goods," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    10. Huang, Wencheng & Yin, Dezhi & Xu, Yifei & Zhang, Rui & Xu, Minhao, 2022. "Using N-K Model to quantitatively calculate the variability in Functional Resonance Analysis Method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    11. 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).
    12. 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).
    13. Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Reliability analysis & performance-based code calibration for slabs/walls of protective structures subject to air blast loading," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    14. Gangolu, Jaswanth & Kumar, Ajay & Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Probabilistic demand models and performance-based fragility estimates for concrete protective structures subjected to missile impact," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    15. Fu, Shanshan & Yu, Yuerong & Chen, Jihong & Xi, Yongtao & Zhang, Mingyang, 2022. "A framework for quantitative analysis of the causation of grounding accidents in arctic shipping," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    16. Zeinalnezhad, Masoomeh & Chofreh, Abdoulmohammad Gholamzadeh & Goni, Feybi Ariani & Hashemi, Leila Sadat & Klemeš, Jiří Jaromír, 2021. "A hybrid risk analysis model for wind farms using Coloured Petri Nets and interpretive structural modelling," Energy, Elsevier, vol. 229(C).
    17. Guo, Jian & Ma, Kaijiang, 2024. "Risk analysis for hazardous chemical vehicle-bridge transportation system: A dynamic Bayesian network model incorporating vehicle dynamics," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    18. 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).
    19. Sarkar, Mitali & Dey, Bikash Koli & Ganguly, Baishakhi & Saxena, Neha & Yadav, Dharmendra & Sarkar, Biswajit, 2023. "The impact of information sharing and bullwhip effects on improving consumer services in dual-channel retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    20. Tao, Longlong & Wu, Jie & Ge, Daochuan & Chen, Liwei & Sun, Ming, 2022. "Risk-informed based comprehensive path-planning method for radioactive materials road transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    21. 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).

    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. Qazi, Abroon & Dickson, Alex & Quigley, John & Gaudenzi, Barbara, 2018. "Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 196(C), pages 24-42.
    2. Mohri, Seyed Sina & Mohammadi, Mehrdad & Gendreau, Michel & Pirayesh, Amir & Ghasemaghaei, Ali & Salehi, Vahid, 2022. "Hazardous material transportation problems: A comprehensive overview of models and solution approaches," European Journal of Operational Research, Elsevier, vol. 302(1), pages 1-38.
    3. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
    4. Bhavsar, Nishit & Verma, Manish, 2022. "A subsidy policy to managing hazmat risk in railroad transportation network," European Journal of Operational Research, Elsevier, vol. 300(2), pages 633-646.
    5. Wang, Shuaian & Yan, Ran & Qu, Xiaobo, 2019. "Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 129-157.
    6. Sajid, Zaman & Khan, Faisal & Zhang, Yan, 2018. "A novel process economics risk model applied to biodiesel production system," Renewable Energy, Elsevier, vol. 118(C), pages 615-626.
    7. Qazi, Abroon & Quigley, John & Dickson, Alex & Ekici, Şule Önsel, 2017. "Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies," European Journal of Operational Research, Elsevier, vol. 259(1), pages 189-204.
    8. Vaezi, Ali & Verma, Manish, 2018. "Railroad transportation of crude oil in Canada: Developing long-term forecasts, and evaluating the impact of proposed pipeline projects," Journal of Transport Geography, Elsevier, vol. 69(C), pages 98-111.
    9. Junjie Fu & Xinqiang Chen & Shubo Wu & Chaojian Shi & Huafeng Wu & Jiansen Zhao & Pengwen Xiong, 2020. "Mining ship deficiency correlations from historical port state control (PSC) inspection data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-19, February.
    10. 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).
    11. Tibor Sipos & Zsombor Szabó & Mohammed Obaid & Árpád Török, 2023. "Disaster Risk Assessment Scheme—A Road System Survey for Budapest," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    12. Bruno Carbonaro & Marco Menale, 2024. "Markov Chains and Kinetic Theory: A Possible Application to Socio-Economic Problems," Mathematics, MDPI, vol. 12(10), pages 1-14, May.
    13. Eun Hak Lee & Inmook Lee & Shin-Hyung Cho & Seung-Young Kho & Dong-Kyu Kim, 2019. "A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data," Sustainability, MDPI, vol. 11(10), pages 1-18, May.
    14. Gaogeng Zhu & Guoming Chen & Jingyu Zhu & Xiangkun Meng & Xinhong Li, 2022. "Modeling the Evolution of Major Storm-Disaster-Induced Accidents in the Offshore Oil and Gas Industry," IJERPH, MDPI, vol. 19(12), pages 1-27, June.
    15. Rajiv Kumar Sharma, 2022. "Examining interaction among supplier selection strategies in an outsourcing environment using ISM and fuzzy logic approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2175-2194, October.
    16. Yang, Zhisen & Wan, Chengpeng & Yu, Qing & Yin, Jingbo & Yang, Zaili, 2023. "A machine learning-based Bayesian model for predicting the duration of ship detention in PSC inspection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    17. Xuecheng Tian & Yanxia Guan & Shuaian Wang, 2023. "Data Transformation in the Predict-Then-Optimize Framework: Enhancing Decision Making under Uncertainty," Mathematics, MDPI, vol. 11(17), pages 1-12, September.
    18. Chunchang Zhang & Hu Sun & Yuanyuan Zhang & Gen Li & Shibo Li & Junyu Chang & Gongqian Shi, 2023. "Fire Accident Risk Analysis of Lithium Battery Energy Storage Systems during Maritime Transportation," Sustainability, MDPI, vol. 15(19), pages 1-12, September.
    19. Hu, Shaolong & Dong, Zhijie Sasha & Dai, Rui, 2024. "A machine learning based sample average approximation for supplier selection with option contract in humanitarian relief," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    20. Nishat Alam Choudhary & Shalabh Singh & Tobias Schoenherr & M. Ramkumar, 2023. "Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications," Annals of Operations Research, Springer, vol. 322(2), pages 565-607, March.

    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:204:y:2020:i:c:s0951832020307213. 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.