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Scenario Construction Model of Railway Traffic Accidents Based on Similarity Theory

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LISS 2022 (LISS 2022)

Part of the book series: Lecture Notes in Operations Research ((LNOR))

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Abstract

Scenario construction has received extensive attention in the field of the emergency management of railway traffic. In this paper, based on government documents and regulations, a scenario construction method is used to establish a similarity calculation model based on doc2vec, which solves the problem of quickly finding similar events as well as response strategies by dividing the scenario elements. The railway accidents were used as real-life examples to verify the result of the model. Finally, the historical scenarios are ranked according to the comprehensive similarity, which can effectively provide timely decision support for the related governments to respond the railway emergencies and accidents.

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References

  1. Alamdar, F., et al.: Towards multi-agency sensor information integration for disaster management. Comput. Environ. Urban Syst. 56, 68–85 (2016)

    Article  Google Scholar 

  2. Alvear, D., et al.: Decision support system for emergency management: Road tunnels. Tunn. Undergr. Space Technol. 34, 13–21 (2013)

    Article  Google Scholar 

  3. Amailef, K., Lu, J.: Ontology-supported case-based reasoning approach for intelligent M-GovernMent emergency response services. Decis. Support Syst. 55(1), 79–97 (2013)

    Article  Google Scholar 

  4. Balboa, A., et al.: Intelligent emergency management system for railway transport. Transp. Res. Procedia 58, 193–200 (2021)

    Article  Google Scholar 

  5. Botev, V., et al.: Word importance-based similarity of documents metric (WISDM): fast and scalable document similarity metric for analysis of scientific documents. In: Proceedings of the 6th International Workshop on Mining Scientific Publications. https://doi.org/10.1145/3127526.3127530 (2017)

  6. Caselles-Dupré, H., et al.: Word2vec applied to recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems. https://doi.org/10.1145/3240323.3240377 (2018)

  7. Erskine, M.A., Pepper, W.: Enhancing emergency response management using emergency description information technology (EDIT). E-Health Telemed 1264–1278 (2016)

    Google Scholar 

  8. Gómez, J., Vázquez, P.: An empirical evaluation of document embeddings and similarity metrics for scientific articles. Appl. Sci. 12(11), 5664 (2022)

    Article  Google Scholar 

  9. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International conference on machine learning. PMLR, pp. 1188–1196 (2014)

    Google Scholar 

  10. Pereira, V.A., et al.: Multi-criteria fusion of heterogeneous information for improving situation awareness on emergency management systems. Lect. Notes Comput. Sci. 3–14 (2015)

    Google Scholar 

  11. Prakash, M., et al.: Modelling the impact of dam failure scenarios on flood Inundation using SPH. Appl. Math. Model. 38(23), 5515–5534 (2014). https://doi.org/10.1016/j.apm.2014.03.011

    Article  Google Scholar 

  12. Qian, J., et al.: A cross-reasoning method for scenario awareness and decision-making support in earthquake emergency response. Web-Age Inf. Manage. 106–118 (2014)

    Google Scholar 

  13. Raman, M.: Claremont colleges emergency preparedness: An action research initiative. Syst Pract Action Res 19(3), 253–271 (2006)

    Article  Google Scholar 

  14. Rodríguez-Sanz, Á., et al.: A data-driven approach for dynamic and adaptive aircraft trajectory prediction. Transp. Res. Proced. 58, 5–12 (2021)

    Article  Google Scholar 

  15. Shahmirzadi, O., et al.: Text similarity in vector space models: a comparative study. SSRN Electr. J. https://doi.org/10.2139/ssrn.3259971 (2018)

  16. Sun, B., et al.: Fuzzy rough set on probabilistic approximation space over two universes and its application to emergency decision-making. Expert. Syst. 32(4), 507–521 (2015)

    Article  Google Scholar 

  17. Sun, X., et al.: Sentence similarity based on contexts. Trans. Assoc. Comput. Linguis. 10, 573–588 (2022)

    Article  Google Scholar 

  18. Xu, X., Li, Y.: Research on multi-granularity response model of emergency based on scene similarity. Inf. Sci. 39(2), 18–23 (2021). https://doi.org/10.13833/j.issn.1007-7634.2021.02.003

    Article  Google Scholar 

  19. Yang, F., et al.: The method for intelligence awareness in an emergency based on scenario similarity. J. China Soc. Sci. Tech. Inf. 38(5), 525–533 (2019)

    Google Scholar 

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Acknowledgements

This paper is supported by Beijing Logistics Informatics Research Base of the International Center for Informatics Research (ICIR).

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Authors and Affiliations

  1. School of Economics and Management, Beijing Jiaotong University, Beijing, China

    Dan Chang, Lei Huang & Daqing Gong

Authors
  1. Dan Chang
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  2. Lei Huang
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  3. Daqing Gong
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Corresponding author

Correspondence to Lei Huang .

Editor information

Editors and Affiliations

  1. School of Economics and Management, Beijing Jiaotong University, Beijing, China

    Xiaopu Shang

  2. Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Xiaowen Fu

  3. School of Software Engineering, Beijing Jiaotong University, Beijing, China

    Yixuan Ma

  4. School of Economics and Management, Beijing Jiaotong University, Beijing, China

    Daqing Gong

  5. School of Economics and Management, Beijing Jiaotong University, Beijing, China

    Juliang Zhang

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Chang, D., Huang, L., Gong, D. (2023). Scenario Construction Model of Railway Traffic Accidents Based on Similarity Theory. In: Shang, X., Fu, X., Ma, Y., Gong, D., Zhang, J. (eds) LISS 2022. LISS 2022. Lecture Notes in Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-99-2625-1_7

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