KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
권호 표지
DOI QR코드

DOI QR Code

Betweenness Centrality-based Evacuation Vulnerability Analysis for Subway Stations: Case Study on Gwanggyo Central Station

매개 중심성 기반 지하철 역사 재난 대피 취약성 분석: 광교중앙역 사례연구

  • Jeong, Ji Won (Hongik University) ;
  • Ahn, Seungjun (Hongik University) ;
  • Yoo, Min-Taek (Gachon University)
  • 정지원 (홍익대학교 건설환경공학과) ;
  • 안승준 (홍익대학교 건설환경공학과) ;
  • 유민택 (가천대학교 건설환경공학과)
  • Received : 2023.11.20
  • Accepted : 2024.01.10
  • Published : 2024.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

Over the past 20 years, there has been a rapid increase in the number and size of subway stations and underground structures worldwide, and the importance of safety for subway users has also continuously grown. Subway stations, due to their structural characteristics, have limited visibility and escape routes in disaster situations, posing a high risk of human casualties and economic losses. Therefore, an analysis of disaster vulnerabilities is essential not only for existing subway systems but also for deep underground facilities like GTX. This paper presents a case study applying a betweenness centrality-based disaster vulnerability analysis framework to the case of Gwanggyo Central Station. The analysis of Gwanggyo Central Station's base model and various disaster scenarios revealed that the betweenness centrality distribution is symmetrical, following the symmetrical spatial structure of the station, with high centrality concentrated in the central areas of basement levels one and two. These areas exhibited values more than 220% above the average, indicating a high likelihood of bottleneck phenomena during evacuation in disaster situations. To mitigate this vulnerability, scenarios were proposed to distribute evacuation flows concentrated in the central areas, enhancing the usability of peripheral areas as evacuation routes by connecting staircases continuously. This modification, when considered, showed a decrease in centrality concentration, confirming that the proposed addition of evacuation paths could effectively contribute to dispersing the flow of evacuation in Gwanggyo Central Station. This case study demonstrates the effectiveness of the proposed framework for assessing evacuation vulnerability in enhancing subway station user safety and can be effectively applied in disaster response and management plans for major underground facilities.

지난 20년 동안 전 세계적으로 지하철 역사와 지하 구조물의 수와 규모가 급증하면서 지하철 이용자의 안전에 대한 중요성도 지속적으로 증가하였다. 지하철 역사는 구조적 특성으로 인해 재난 상황에서 시야 확보와 탈출 경로가 제한되어 인명 피해와 경제적 손실의 위험이 높다. 이에 따라 기존 지하철 시스템뿐만 아니라 GTX와 같은 대심도 지하 시설에 대한 재난 대피 취약성 분석이 필수적이다. 본 논문은 광교중앙역 사례를 중심으로 매개 중심성 기반의 대피 취약성 분석 프레임워크를 적용한 사례 연구를 제시한다. 구체적으로, 역사의 공간 구문론에 기반한 네트워크 모델을 구축하고 매개 중심성 분석을 통해 병목 지점을 평가하며, 이를 통해 재난 대피 취약성을 평가하였다. 이는 공간 네트워크에서 높은 매개 중심성을 가진 지점은 전체 공간 간 연결구조를 중개함을 시사하며, 재난 대피 상황에 있어서는 대피 인원이 집중되고 전체 대피 흐름을 통제하는 공간으로 해석될 수 있기 때문이다. 광교중앙역 기본 모델 및 재난 발생 시나리오 별 분석결과, 대칭적인 광교중앙역 공간 구조에 따라 매개 중심성 분포는 대칭성을 띄며, 지하 1층 및 지하 2층의 중앙구역에 매개 중심성이 집중되어 있음이 발견되었다. 이들 구역은 평균값에 비해 약 220 % 이상 높은 값을 보이며, 이는 재난 상황 시 대피 흐름이 교차되며 병목 현상의 발생 가능성이 높음을 시사한다. 이러한 광교중앙역의 재난 대피 취약성을 개선하기 위해서 중앙구역에 집중되는 재난 대피 흐름을 분산시키기 위해 가장자리 구역의 재난 대피로로써의 활용성을 증대시키도록 계단실을 연속적으로 연결하는 구조변경을 시나리오로 제시하였다. 이러한 이동통로 변경이 고려될 경우, 매개 중심성의 분산이 감소하며 제안된 대피통로 추가가 광교중앙역의 대피 흐름 분산에 효과적으로 기여할 수 있음을 확인하였다. 이 사례 연구는 지하철 역사 사용자 안전성 향상에 있어 제안된 대피 취약성 평가 프레임워크의 효과성을 입증하며, 주요 지하 시설물의 재난 대응 및 관리 계획에도 효과적으로 활용될 수 있음을 보인다.

Keywords

Acknowledgement

This research was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant No. RS-2023-00238018). This paper has been written by modifying and supplementing the KSCE 2023 CONVENTION paper.

References

  1. Adger, W. N. (2006). "Vulnerability." Global Environmental Change, Elsevier, Vol. 16, No. 3, pp. 268-281, https://doi.org/10.1016/j.gloenvcha.2006.02.006.
  2. Amani, A. M. and Jalili, M. (2021). "Power grids as complex networks: Resilience and reliability analysis." IEEE Access, IEEE, Vol. 9, pp. 119010-119031, https://doi.org/10.1109/ACCESS.2021.3107492.
  3. Balica, S. F., Wright, N. G. and Van der Meulen, F. (2012). "A flood vulnerability index for coastal cities and its use in assessing climate change impacts." Natural Hazards, Springer, Vol. 64, pp. 73-105, https://doi.org/10.1007/s11069-012-0234-1.
  4. Barthelemy, M. (2004). "Betweenness centrality in large complex networks." The European Physical Journal B, EDP Sciences, Vol. 38, No. 2, pp. 163-168, https://doi.org/10.1140/epjb/e2004-00111-4.
  5. Blakely, E. J. (2007). Urban planning for climate change, Lincoln Institute of Land Policy, New Orleans, LA.
  6. Cerba, O., Jedlicka, K., Cada, V. and Charvat, K. (2017). "Centrality as a method for the evaluation of semantic resources for disaster risk reduction." ISPRS International Journal of Geo-Information, MDPI, Vol. 6, No. 8, 237, https://doi.org/10.3390/ijgi6080237.
  7. Chen, H., Zhang, L. and Ran, L. (2021). "Vulnerability modeling and assessment in urban transit systems considering disaster chains: A weighted complex network approach." International Journal of Disaster Risk Reduction, Elsevier, Vol. 54, 102033, https://doi.org/10.1016/j.ijdrr.2020.102033.
  8. Coursey, K. and Mihalcea, R. (2009). "Topic identification using wikipedia graph centrality." Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American, Association for Computational Linguistics, Colorado, USA, pp. 117-120.
  9. Cui, C., Xu, M., Xu, C., Zhang, P. and Zhao, J. (2023). "An ontology-based probabilistic framework for comprehensive seismic risk evaluation of subway stations by combining Monte Carlo simulation." Tunnelling and Underground Space Technology, Elsevier, Vol. 135, 105055, https://doi.org/10.1016/j.tust.2023.105055.
  10. Dursun, P. (2007). "Space syntax in architectural design." Proceedings of 6th International Space Syntax Symposium, Istnabul Tbknlk University, Istanbul, Tuikiye, pp. 1-56.
  11. Few, R. (2003). "Flooding, vulnerability and coping strategies: local responses to a global threat." Progress in Development Studies, Sage, Vol. 3, No. 1, pp. 43-58, https://doi.org/10.1191/1464993403ps049ra.
  12. Forero-Ortiz, E., Martinez-Gomariz, E., Canas Porcuna, M., Locatelli, L. and Russo, B. (2020). "Flood risk assessment in an underground railway system under the impact of climate change-a case study of the Barcelona Metro." Sustainability, MDPI, Vol. 12, No. 13, 5291, https://doi.org/10.3390/su12135291.
  13. Gueret, C., Groth, P., Stadler, C. and Lehmann, J. (2012). "Assessing linked data mappings using network measures." Proceedings of the Semantic Web: Research and Applications: 9th Extended Semantic Web Conference, Springer, Heraklion, Crete, Greece, pp. 87-102. Springer Berlin Heidelberg.
  14. Jeong, J. W., Ahn, S. J. and Yoo, M. T. (2023). "Evacuation Convenience Analysis Framework for Underground Facilities Based on Betweenness Centrality." Journal of Construction Automation and Robotics, KSARC, Vol. 2, No. 2, pp. 8-13, https://doi.org/10.55785/JCAR.2.2.8 (in Korean).
  15. Khademi, N., Balaei, B., Shahri, M., Mirzaei, M., Sarrafi, B., Zahabiun, M. and Mohaymany, A. S. (2015). "Transportation network vulnerability analysis for the case of a catastrophic earthquake." International Journal of Disaster Risk Reduction, Elsevier, Vol. 12, pp. 234-254, https://doi.org/10.1016/j.ijdrr.2015.01.009.
  16. Kim, K. T. and Song, J. M. (2016). "Analysis of the Road Network Characteristics in the Flood Prone Areas in Seoul using Network Centrality Indices." Journal of the Korean Regional Development Association, KRDA, Vol. 28, No. 3, pp. 89-108 (in Korean).
  17. Lee, S. H. (2017). "A study on the disaster management vulnerability." Korean Journal of Safety Culture, Korea Safety Culture Society, Vol. 1, pp. 1-11 (in Korean).
  18. Lissner, T. K., Holsten, A., Walther, C. and Kropp, J. P. (2012). "Towards sectoral and standardised vulnerability assessments: the example of heatwave impacts on human health." Climatic Change, Springer, Vol. 112, pp. 687-708, https://doi.org/10.1007/s10584-011-0231-5.
  19. Ministry of Land, Infrastructure and Transport (MOLIT) (2021). The Fourth National Railway Network Development Plan, Republic of Korea (in Korean).
  20. Mizgier, K. J., Juttner, M. P. and Wagner, S. M. (2013). "Bottleneck identification in supply chain networks." International Journal of Production Research, Taylor & Francis, Vol. 51, No. 5, pp. 1477-1490, https://doi.org/10.1080/00207543.2012.695878.
  21. Moore, S., Eng, E. and Daniel, M. (2003). "International NGOs and the role of network centrality in humanitarian aid operations: a case study of coordination during the 2000 Mozambique floods." Disasters, ODI, Vol. 27, No. 4, pp.305-318, https://doi.org/10.1111/j.0361-3666.2003.00235.x.
  22. Mortula, M. M., Ahmed, M. A., Sadri, A. M., Ali, T., Ahmad, I. and Idris, A. (2020). "Improving resiliency of water supply system in arid regions: Integrating centrality and hydraulic vulnerability." Journal of Management in Engineering, ASCE, Vol. 36, No. 5, 05020011, https://doi.org/10.1061/(ASCE)ME.1943-5479.0000817.
  23. Newman, M. (2010). Networks: An Introduction, 1st ed, Oxford University Press, UK.
  24. Park, M. Y., Koo, W.Y., Park, W.S., Park, E., Moon, B. G. and Kwon, S.G. (2016). "A study on development of Interactive Smart Network(IoT)-based subway platform disaster response system." Journal of Korean Society of Disaster & Security, KSDS, Vol. 9, No. 1, pp. 19-24, https://doi.org/10.21729/ksds.2016.9.1.19 (in Korean).
  25. Sherly, M. A., Karmakar, S., Parthasarathy, D., Chan, T. and Rau, C. (2015). "Disaster vulnerability mapping for a densely populated coastal urban area: an application to Mumbai, India." Annals of the Association of American Geographers, American Association of Geographers, Vol. 105, No. 6, pp. 1198-1220, https://doi.org/10.1080/00045608.2015.1072792.
  26. Wisner, B., Blaikie, P., Cannon, T. and Davis, I. (2004). At Risk: Natural Hazards, People's Vulnerability, and Disasters, 2nd edition, Routledge, New York.
  27. Wasserman, S. and Faust, K. (1994). Social network analysis: Methods and applications, Cambridege University Press, UK.
  28. Wu, J., Fang, W., Hu, Z. and Hong, B. (2018a). "Application of bayesian approach to dynamic assessment of flood in urban underground spaces." Water, MDPI, Vol. 10, No. 9, 1112, https://doi.org/10.3390/w10091112.
  29. Wu, J., Hu, Z., Chen, J. and Li, Z. (2018b). "Risk assessment of underground subway stations to fire disasters using Bayesian network." Sustainability, MDPI, Vol. 10, No. 10, 3810, https://doi.org/10.3390/su10103810.
  30. Yu, H. and Guo, Z. (2022). "Design of underground space intelligent disaster prevention system based on multisource data deep learning." Wireless Communications and Mobile Computing, Hindawi, Vol. 2022, https://doi.org/10.1155/2022/3706392.
  31. Zarghami, S. A. and Dumrak, J. (2021). "Unearthing vulnerability of supply provision in logistics networks to the black swan events: Applications of entropy theory and network analysis." Reliability Engineering & System Safety, Elsevier, Vol. 215, 107798, https://doi.org/10.1016/j.ress.2021.107798.