All Issue

2022 Vol.40, Issue 1 Preview Page

Article

Regression Analysis of Delay in Fire Mobilization: Focused on Busan Metropolitan City

화재 출동 지체의 원인 분석에 관한 연구: 부산광역시를 중심으로

Boram WOO1
,
Hoe Kyoung KIM2*
우 보람1
,
김 회경2*
1Researcher, Department of Comprehensive Transport, Korea Transport Institute, Sejong 30147, Korea
2Associate Professor, Department of Urban Planning and Engineering, Dong-A University, Busan 49315, Korea
1한국교통연구원 광역 ‧ 도시교통연구본부 연구원
2동아대학교 도시계획공학과 부교수
*Corresponding Author
28 February 2022. pp. 99-110
PDF XML Citation
Abstract

This study analyzed the area that can be reached within 5 minutes of the golden time based on the fire dispatch data of Busan Metropolitan City, and derived the factors affecting the fire dispatch time. This study is largely composed of three main parts: first, elimination of outliers in the fire dispatch data, second, estimation of fire-fighting service area within 5 minutes of golden time through GIS spatial analysis, and third, regression analysis of delay in fire mobilization. As a result, the number of intersections was found to commonly influence the extension of the fire dispatch time in six districts, implying that the introduction of the emergency vehicle preemption system is an urgent policy alternative in Busan. In addition, the ratio of the 1-lane in the fire dispatch route increases the fire dispatch time in five districts excluding the Gangseo district, requiring the increase of the number of lanes or expansion of the lane width. In particular, illegal parking is an underlying factor in the fire dispatch time increase by 550 to 600 seconds in the Gangseo and Gijang districts, demanding the strong crackdown on illegal parking. The results of this study are expected to serve as supporting data for policy establishment to secure 5 minutes of the golden time in Busan in the future.

Keywords
fire mobilization delay
fire service area
golden time
multiple regression analysis
network analysis

본 연구는 부산광역시 화재 출동 자료를 바탕으로 골든타임 5분 내에 도달 가능한 영역을 공간적으로 분석하고, 화재 출동시간을 지체시키는 원인을 도출하는 것에 그 목적이 있다. 본 연구는 크게 세 가지 내용으로 구성되어 있다. 첫째, 부산광역시 화재 출동 데이터의 이상치 제거, 둘째, GIS 공간분석을 통한 부산광역시 여섯 개의 권역별 골든타임 소방 서비스 영역 산정, 셋째, 다중회귀분석을 이용한 부산광역시 권역별 화재 출동 지체의 원인분석이다. 본 연구의 결과, 여섯 개 권역에서 화재 출동 소요시간을 지체시키는 공통적인 요인은 출동 경로 내 위치한 교차로의 수로 도출되었다. 부산광역시 화재 출동 소요시간 단축을 위해 긴급차량 우선신호체계의 도입이 시급한 정책 대안이라고 할 수 있다. 또한 강서권을 제외한 다섯 개의 권역에서 경로 내 1차로의 비율이 화재 출동 소요시간을 증가시키는 것으로 나타나 부산광역시의 도로정비계획 시 차로 수를 늘리거나 혹은 1차로의 차로 폭을 확대시키는 방안이 요구된다. 강서권과 기장권의 경우 불법 주정차 차량으로 인해 화재 출동 소요시간이 550-600초 증가하는 것으로 나타나 불법 주정차 차량 단속을 강화하여 응급차량의 접근이 지체되지 않도록 하여야 할 것이다. 본 연구의 결과는 향후 부산광역시의 골든타임 확보율을 개선하기 위한 정책 수립 기초자료의 역할을 할 것으로 기대된다.

키워드
화재 출동 지체
소방 서비스 영역
골든타임
다중회귀분석
네트워크분석
References
1
Adewuyi G. K. (2020), Assessment of Fire Service Station Response to Filling Stations Fire Outbreak and Vulnerable Healthcare Centers to Filling Stations in Urban Settlement, International Journal of Research and Review, 7(3), 10-30.
2
Asgary A., Ghaffari A., Levy J. (2010), Spatial and Temporal Analyses of Structural Fire Incidents and Their Causes: A Case of Toronto, Canada, Fire Safety Journal, 45(1), 44-57. 10.1016/j.firesaf.2009.10.002
3
Bae S. H. (2018), Spatial Clustering Analysis of Fire in Gangwon-Do, Journal of the Korean Association of Geographic Information Studies, 21(3), 93-103.
4
Brys G., Hubert M., Struyf A. (2003), A Comparison of Some New Measures of Skewness, In Developments in robust statistics, Physica, Heidelberg, 98-113. 10.1007/978-3-642-57338-5_8
5
Brys G., Hubert M., Struyf A. (2004), A Robust Measure of Skewness, Journal of Computational and Graphical Statistics, 13(4), 996-1017. 10.1198/106186004X12632
6
Busan Metropolitan City (2019), Balanced Urban Development Plan for Busan.
7
Chevalier P., Thomas I., Geraets D., Goetghebeur E., Janssens O., Peeters D. et al. (2012), Locating Fire Stations: An Integrated Approach for Belgium, Socio-Economic Planning Sciences, 46(2), 173-182. 10.1016/j.seps.2012.02.003
8
Choi H. R., Ban Y. U., Baek J. I., Lee S. H. (2018), GIS-based Spatial Analysis Method to Analyze the Characteristics of Fire Services and Identify Vulnerable Areas: The Case of Cheongju City, Korea -, Crisisonomy, 14(9), 101-109. 10.14251/crisisonomy.2018.14.9.101
9
Clarke A., Miles J. C. (2012), Strategic Fire and Rescue Service Decision Making Using Evolutionary Algorithms, Advances in Engineering Software, 50, 29-36. 10.1016/j.advengsoft.2012.04.002
10
Forkuo E. K., Quaye-Ballard J. A. (2013), GIS Based Fire Emergency Response System, International Journal of Remote Sensing and GIS, 2(1), 32-40.
11
Hubert M., Vandervieren E. (2008), An Adjusted Boxplot for Skewed Distributions, Computational Statistics & Data Analysis, 52(12), 5186-5201. 10.1016/j.csda.2007.11.008
12
Hwang E. H., Choi J. H., Choi D. M. (2018), A Study on the Effective Methods of Securing the Golden Time of Fire Engine Move Out, Journal of Disaster Management, 18(5), 119-126. 10.9798/KOSHAM.2018.18.5.119
13
Kim H. B., Kim D. M., Oh S. H. (2002), A Study on Evaluating a Disaster-vulnerable Area and Locating Optimal Emergency Facilities Based on GIS Spatial Analysis Techniques, Journal of the Korean Society of Civil Engineers D, 22(4D), 607-616.
14
Kim J. (2006), Weight Reduction Method for Outlier in Survey Sampling, The Korean Communications in Statistics, 13(1), 19-27. 10.5351/CKSS.2006.13.1.019
15
Kim M. J., Kim G. S. (2020), Analysis of the Crop Damage Area Related to Flood by Climate Change Using a Constrained Multiple Linear Regression Model, Journal of the Korean Society of Agricultural Engineers, 62(2), 1-15.
16
Kim S. J., Choi G. Y., Chang E. M., Song W. Y. (2015), Producing Firefighting Vulnerability Maps Using GIS: A Case Study of Dalseo-gu, Daegu, Journal of the Korean Association of Geographic Information Studies, 18(3), 11-20. 10.11108/kagis.2015.18.3.011
17
Kiran K. C., Corcoran J., Chhetri P. (2020), Measuring the Spatial Accessibility to Fire Stations Using Enhanced Floating Catchment Method, Socio-Economic Planning Sciences, 69, 100673. 10.1016/j.seps.2018.11.010
18
KOSIS (2020), http://kosis.kr
19
Kwon P., Lee Y. M., Hyh Y., Yu K. Y. (2015), Rearranging Emergency Medical Service Region Using GIS Network Analysis: Daejeon Metropolitan City Case Study, Journal of the Korean Society for Geospatial Information Science, 23(3), 11-21. 10.7319/kogsis.2015.23.3.011
20
Lee J. H., Ban Y. U., Kim Y. M., Kim W. S. (2019), Analysis of Causal Mapping between Urban Spatial Characteristics and Large-Scale Fire: Focused on the Relationship between Social, Economic and Environmental Factors of Cities, Crisisonomy, 15(7), 1-15. 10.14251/crisisonomy.2019.15.7.1
21
Lee J. W., Lee S. B., Lee J. S., Um K. H., Lee Y. I. (2017), A Passive Traffic Signal Priority Control Algorithm for Emergency Vehicles, The Journal of the Korea Institute of Intelligent Transportation Systems, 16(3), 592-596. 10.12815/kits.2017.16.3.110
22
Lee S. J., Lee J. Y. (2011), Vulnerability Analysis on Fire Service Zone using Map Overlay Method in GIS, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 29(1), 91-100. 10.7848/ksgpc.2011.29.1.91
23
Lee S. M., Lee J. W., Choi G. J., Kim Y. J. (2018), A Establishment of Emergency Vehicle Flow Planning in Chungju, Fire-Weak Area, Architectural Institute of Korea, 38(2), 702-706.
24
Lee Y. J. (1994), A Study on the Verification of the Main Effect in Multiple Regression Analysis including Interaction Effect, Management Research, 23(4), 183-210.
25
Liu D., Xu Z., Wang Z., Zhou Y., Fan C. (2020a), Estimation of Effective Coverage Rate of Fire Station Services Based on Real-time Travel Times, Fire Safety Journal, 103021. 10.1016/j.firesaf.2020.103021
26
Liu D., Xu Z., Yan L., Fan C. (2020b), Dynamic Estimation System for Fire Station Service Areas Based on Travel Time Data, Fire Safety Journal, 118, 103238. 10.1016/j.firesaf.2020.103238
27
Liu N., Huang B., Chandramouli M. (2006), Optimal Siting of Fire Stations Using GIS and ANT Algorithm, Journal of Computing in Civil Engineering, 20(5), 361-369. 10.1061/(ASCE)0887-3801(2006)20:5(361)
28
Murray A. T. (2013), Optimising the Spatial Location of Urban Fire Stations, Fire Safety Journal, 62, 64-71. 10.1016/j.firesaf.2013.03.002
29
Nam K. W., Kim J. G. (2007), Temporal and Spatial Distributions of Emergency Medical Services: Busan, Journal of the Korean Association of Geographic Information Studies, 10(1), 113-123.
30
Oppong J. R., Boakye K., Edziyie R., Owusu A. Y., Tiwari C. (2017), Emergency Fire Response in Ghana: The Case of Fire Stations in Kumasi, African Geographical Review, 36(3), 253-261. 10.1080/19376812.2016.1231616
31
Park J. S. (2015), A Study on the Improvement of Fire Safety in Fire Vulnerable Districts, Journal of Disaster Management, 15(5), 131-137. 10.9798/KOSHAM.2015.15.5.131
32
Seon J. Y., Kim K. Y., Kim J. H. (2019), Statistical Methods and Utilization Plan for Detecting Outliers, Health Insurance Review and Assessment Service, 1-60.
33
Seong J. K., Ha D. I. (2016), A Selection Method of Implementation Area for Emergency Vehicle Preemption System Using Dispatch Data Analysis, The Journal of The Korea Institute of Intelligent Transportation Systems, 15(2), 24-35. 10.12815/kits.2016.15.2.024
34
Shahparvari S., Fadaki M., Chhetri P. (2020), Spatial Accessibility of Fire Stations for Enhancing Operational Response in Melbourne, Fire Safety Journal, 117, 103149. 10.1016/j.firesaf.2020.103149
35
Tali J. A., Malik M. M., Divya S., Nusrath A., Mahalingam B. (2017), Location-allocation Model Applied to Urban Public Services: Spatial Analysis of Fire Stations in Mysore Urban Area Karnataka, India, International Journal of Advanced Research and Development, 2(5), 795-801.
36
Yao J., Zhang X., Murray A. T. (2019), Location Optimization of Urban Fire Stations: Access and Service Coverage, Computers, Environment and Urban Systems, 73, 184-190. 10.1016/j.compenvurbsys.2018.10.006
37
Yoo H. H., Koo S. (2013), Fire District Adjustment for Improving Fire Service Vulnerable Areas in Jinju, Journal of the Korean Society for Geospatial Information System, 21(1), 19-26. 10.7319/kogsis.2013.21.1.019
38
Yu W., Chen Y., Chen Z., Xia Z., Zhou Q. (2020), Service Area Delimitation of Fire Stations with Fire Risk Analysis: Implementation and Case Study, International Journal of Environmental Research and Public Health, 17(6), 2030. 10.3390/ijerph1706203032204408PMC7143634
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 40
  • No :1
  • Pages :99-110
  • Received Date : 2021-04-15
  • Revised Date : 2021-05-06
  • Accepted Date : 2021-10-20
  • DOI :https://doi.org/10.7470/jkst.2022.40.1.099
Journal Informaiton Journal of Korean Society of Transportation Journal of Korean Society of Transportation
Archives
: 2015 Vol.34~
  • scopus
  • NRF
  • KOFST
  • crossref cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close