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A Study of Risk Relevance Reasoning Based on a Context Ontology of Railway Accidents

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  • Tiancheng Cao
  • Wenxin Mu
  • Juanqiong Gou
  • Liyu Peng

Abstract

With the application of risk management and accident response in the railway domain, risk detection and prevention have become key research topics. Many dangers and associated risk sources must be considered in collaborative scenarios of heavy‐haul railways. In these scenarios, (1) various risk sources are involved in different data sources, and context affects their occurrence, (2) the relationships between contexts and risk sources in the accident cause mechanism need to be explicitly defined, and (3) risk knowledge reasoning needs to integrate knowledge from multiple data sources to achieve comprehensive results. To express the association rules among core concepts, this article constructs two ontologies: The accident‐risk ontology and the context ontology. Concept analysis is based on railway domain knowledge and accident analysis reports. To sustainably integrate knowledge, an integrated evolutionary model called scenario‐risk‐accident chain ontology (SRAC) is constructed by introducing new data sources. The SRAC is integrated through expert rules between the two ontologies, and its evolution process involves new knowledge through a new risk source database. After three versions of the upgrade process, potential risk sources can be mined and evaluated in specific contexts. To evaluate the risk source level, a long short‐term memory (LSTM) neural network model is used to capture context and risk text features. A model comparison for different neural network structures is performed to find the optimal evaluation results. Finally, new concepts, such as risk source level, and new instances are updated in the context‐aware risk knowledge reasoning framework.

Suggested Citation

  • Tiancheng Cao & Wenxin Mu & Juanqiong Gou & Liyu Peng, 2020. "A Study of Risk Relevance Reasoning Based on a Context Ontology of Railway Accidents," Risk Analysis, John Wiley & Sons, vol. 40(8), pages 1589-1611, August.
  • Handle: RePEc:wly:riskan:v:40:y:2020:i:8:p:1589-1611
    DOI: 10.1111/risa.13506
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    References listed on IDEAS

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    1. Adjetey-Bahun, Kpotissan & Birregah, Babiga & Châtelet, Eric & Planchet, Jean-Luc, 2016. "A model to quantify the resilience of mass railway transportation systems," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 1-14.
    2. Zemp, Stefan & Stauffacher, Michael & Lang, Daniel J. & Scholz, Roland W., 2011. "Classifying railway stations for strategic transport and land use planning: Context matters!," Journal of Transport Geography, Elsevier, vol. 19(4), pages 670-679.
    3. Wu, Yunna & Jia, Weibing & Li, Lingwenying & Song, Zixin & Xu, Chuanbo & Liu, Fangtong, 2019. "Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation," Energy, Elsevier, vol. 182(C), pages 397-411.
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    1. Yan, Dongyang & Li, Keping & Zhu, Qiaozhen & Liu, Yanyan, 2023. "A railway accident prevention method based on reinforcement learning – Active preventive strategy by multi-modal data," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. 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).

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