Journal of the Korean Society of Safety (한국안전학회지)

The Korean Society of Safety (한국안전학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Mobility Constraint Factors of Fire Engines in Vulnerable Areas : A Case Study of Difficult-to-access Areas in Seoul

화재대응 취약지역에서의 소방특수차량 이동제약요인 분석 : 서울시의 진입곤란지역을 대상으로

  • Yeoreum Yoon (Department of Architectural and Urban Systems Engineering, Ewha Womans University) ;
  • Taeeun Kim (Department of Architectural and Urban Systems Engineering, Ewha Womans University) ;
  • Minji Choi (Department of Architectural Engineering, Inha University) ;
  • Sungjoo Hwang (Department of Architectural and Urban Systems Engineering, Ewha Womans University)
  • 윤여름 (이화여자대학교 건축도시시스템공학전공) ;
  • 김태은 (이화여자대학교 건축도시시스템공학전공) ;
  • 최민지 (인하대학교 건축학부) ;
  • 황성주 (이화여자대학교 건축도시시스템공학전공)
  • Received : 2023.11.30
  • Accepted : 2024.01.02
  • Published : 2024.02.29
Download PDF
⟨ Previous Next ⟩

Abstract

Ensuring swift on-site access to fire engines is crucial in preserving the golden time and minimizing damage. However, various mobility constraints in alleyways hinder the timely entry of fire engines to the fire scene, significantly impairing their initial response capabilities. Therefore, this study analyzed the significant mobility constraints of fire engines, focusing on Seoul, which has many old town areas. By leveraging survey responses from firefighting experts and actual observations, this study quantitatively assessed the frequency and severity of mobility constraint factors affecting the disaster responses of fire engines. Survey results revealed a consistent set of top five factors regarding the frequency and disturbance level, including illegally parked cars, narrow paths, motorcycles, poles, and awnings/banners. A comparison with actual road-view images showed notable consistency between the survey and observational results regarding the appearance frequency of mobility constraint factors in vulnerable areas in Seoul. Furthermore, the study emphasized the importance of tailored management strategies for each mobility constraint factor, considering its characteristics, such as dynamic or static. The findings of this study can serve as foundational data for creating more detailed fire safety maps and advancing technologies that monitor the mobility of fire engines through efficient vision-based inference using CCTVs in the future.

Keywords

Acknowledgement

This research was supported by a grant (20019382, AI Technology for Analyzing Fire Engines' Accessibility to Fire Site) of Regional Customized Disaster-Safety R&D Program funded by Ministry of the Interior and Safety (MOIS, Korea) and the Seoul Metropolitan Government. This work was also supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2023-00210164).

References

  1. J. S. Won, "Enhancing Fire Response Capabilities in Seoul: Policy Report" Seoul Institute, Vol. 239, pp. 1-21, 2017.
  2. Y. J. Kim, T. E. Kim, S. Y. Kim and S. J. Hwang, "A Study on the Analysis of Constraints on the Mobility of Fire Engine in Fire Vulnerable Areas and Analysis of the Dataset for AI Inference" Proceedings of the Architectural Institute of Korea Annual Conference, Vol. 42, No. 2, pp. 883-886, 2022.
  3. Y. H. Lee, M. S. Kim and H.Y. Kim"Location Selection for Installation of Emergency Firefighting Devices to Resolve Vulnerable Areas to Fire Response" Fire Science and Engineering, Vol. 36, No. 5, pp. 106-113, 2022.
  4. M. Cordts et al., "The Cityscapes Dataset for Semantic Urban Scene Understanding" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213-3223, 2016.
  5. K. Horak and R. Sablatnig, "Deep Learning Concepts and Datasets for Image Recognition: Overview" Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), SPIE, 11179, pp. 484-491, 2019.
  6. Y. K. Hwang and S. C. Lee, "A Study On Approach of the Fire Engines in the Apartment House" Proceedings of Annual Conference, Korea Institute of Fire Science & Engineering, pp. 65-66, 2019.
  7. E. H. Hwang, J. H. Choi and D, M. Choi, "A Study on the Effective Methods of Securing the Golden Time of Fire Engine Move Out" Journal of The Korean Society of Hazard Mitigation, Vol. 18, No. 5, pp. 119-126, 2018.
  8. S. M., Lee, J. W. Lee, G. J. Choi and Y. J. Kim "Establishment of Emergency Vehicle Flow Planning in Chungju, Fire-Weak Area" Proceedings of the Architectural Institute of Korea Annual Conference, Vol. 38, No. 2, pp. 702-706, 2018. 2
  9. M. J. Choi, "Advancement Planning of Disaster Response Assistant System in Seoul: Focusing on Seoul Fire Safety Map" Proceedings of the Architectural Institute of Korea Annual Conference, Vol. 41, No. 1, pp. 739-740, 2021.
  10. K.Jang, S. B. Cho, Y. S. Cho and S. Son, "Development of Fire Engine Travel Time Estimation Model for Securing Golden Time" The Journal of The Korea Institute of Intelligent Transport Systems, Vol. 19, No. 6, pp. 1-13, 2020.
  11. H. S. Kim, "Estimating Accessibility of Fire Engines to Secure Golden Time" Review of Architecture and Building Science, Architectural Institute of Korea, Vol. 67, No. 4, 32-35, 2023.
  12. Y. Qu, Z. Lin, H. Li and X. Zhang, "Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data" IEEE Access, 7, 170106-170115, 2019.
  13. Z. Guo, Indoor Risk Assessment through Deep Learning-based Object Identification in Disaster Situations, Thesis, Kyungpook National University, 2022.
  14. C. H. So, A Study on Dispatch of Continuous Patrol for System of Preemptive Response to Fire Scene, Thesis, Wonkwang University, 2020.
  15. G. E. Lee, Prediction of the Possibility of Entering a Fire Engine in an Alleyway Based on the Detection Results of Illegal Parking Based on Deep Learning, Thesis, Hanyang University, 2023.
  16. J. A. Martilla and J. C. James, "Importance-Performance Analysis." Journal of Marketing, Vol. 41, No. 1, p. 77, 1977.
  17. C. J. Van Westen, D. Alkema, M. C. J.Damen, N. Kerle and N. C. Kingma, Multi-hazard Risk Assessment, United Nations University-ITC School on Disaster Geoinformation Management, 2011.
  18. H. Kim and J. Yun, "Analysis of Streetscape Characteristics utilizing Road-View Images of Portal Sites in Urban Rehabilitation" GRI Review, Vol. 14, No. 1, pp. 279-304, 2012.
  19. T. Y. Linet al., "Microsoft COCO: Common objects in context" Proceedings of the European Conference on Computer Vision (ECCV), Springer, Cham, 8693, pp. 740-755, 2014.
  20. M. Menze and A. Geiger, "Object Scene Flow for Autonomous Vehicles" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3061-3070, 2015.
  21. F. Yu, W. Xian, Y. Chen, F. Liu, M. Liao, V. Madhavan and T. Darrell, "Bdd100k: A Diverse Driving Video Database with Scalable Annotation Tooling" arXiv preprint arXiv:1805.04687, Vol. 2, No. 5, p. 6, 2018.
  22. T. Chen et al., "PSI: A Pedestrian Behavior Dataset for Socially Intelligent Autonomous Car", arXiv Preprint arXiv:2112.02604, 2021.
  23. P. F. Proenca and P. Simoes, "Taco: Trash Annotations in Context for Litter Detection", arXiv Preprint arXiv:2003.06975, 2020.
  24. A. Kuznetsova et al., "The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale" International Journal of Computer Vision, Vol. 128, No. 7, pp. 1956-1981, 2020.