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A Visualization of Traffic Accidents Hotspot along the Road Network

도로 네트워크를 따른 교통사고 핫스팟의 시각화

  • Cho, Nahye (Department of Geoinformatics, University of Seoul) ;
  • Jun, Chulmin (Department of Geoinformatics, University of Seoul) ;
  • Kang, Youngok (Department of Social Studies, Ewha Womans University)
  • 조나혜 (서울시립대학교 공간정보공학과) ;
  • 전철민 (서울시립대학교 공간정보공학과) ;
  • 강영옥 (이화여자대학교 사회과교육과)
  • Received : 2018.05.04
  • Accepted : 2018.06.27
  • Published : 2018.06.30

Abstract

In recent years, the number of traffic accidents caused by car accidents has been decreasing steadily due to traffic accident prevention activities in Korea. However, the number of accidents in Seoul is higher than that of other regions. Various studies have been conducted to prevent traffic accidents, which are human disasters. In particular, previous studies have performed the spatial analysis of traffic accidents by counting the number of traffic accidents by administrative districts or by estimating the density through kernel density method in order to identify the traffic accident cluster areas. However, since traffic accidents take place along the road, it would be more meaningful to investigate them concentrated on the road network. In this study, traffic accidents were assigned to the nearest road network in two ways and analyzed by hotspot analysis using Getis-Ord Gi* statistics. One of them was investigated with a fixed road link of 10m unit, and the other by computing the average traffic accidents per unit length per road section. As a result by the first method, it was possible to identify the specific road sections where traffic accidents are concentrated. On the other hand, the results by the second method showed that the traffic accident concentrated areas are extensible depending on the characteristic of the road links. The methods proposed here provide different approaches for visualizing the traffic accidents and thus, make it possible to identify those sections clearly that need improvement as for the traffic environment.

최근 우리나라의 경우 교통사고 예방활동으로 자동차 보유에 따른 교통사고 발생건수는 지속적으로 감소하고 있지만, 서울의 경우 다른 지역에 비해 자동차 1만대 대비 사고 건수는 전국에서 광주와 함께 가장 높게 나타나고 있다. 인적 재난인 교통사고를 예방하기 위한 다양한 연구들이 진행되어 왔다. 특히 교통사고에 대한 공간적 분석을 연구한 초기 연구들은 교통사고 클러스터 지역을 확인하기 위해 행정구역 별 교통사고 건수를 집계하거나, 커널밀도 방법을 통해 밀도를 추정하여 분석하는 경우가 다수를 이루었다. 그러나 교통사고는 도로를 따라 발생하는 사건이기 때문에 도로상에서 교통사고 다발구간을 찾는 것이 더 의미가 있을 수 있다. 따라서 본 연구는 도로 네트워크를 따라 교통사고 집중 지역을 찾고자 하였다. 본 연구에서는 2가지 방법으로 교통사고를 가장 가까운 도로 네트워크에 할당한 뒤, Getis-Ord $Gi^*$에 의한 핫스팟 분석을 통해 교통사고 다발구간을 분석하였다. 하나는 10m 단위의 일정한 도로 링크를 중심으로 분석을 수행하였으며, 다른 하나는 도로구간별 단위 길이 당 평균 교통사고를 계산하여 교통사고 밀집구간을 분석하였다. 첫 번째 방법에 의한 분석 결과 교통사고가 집중되는 특정 도로 구간을 명확하게 확인할 수 있는 반면, 두 번째 방법에 의한 분석 결과 도로링크의 특성에 따라 교통사고 집중지역이 길게 나타나는 특징을 확인할 수 있었다. 두 방법에 의한 교통사고 다발구간이 다르게 나타나는 것을 알 수 있으며, 향후 해당 지역의 교통환경을 분석하고 개선하기 위해서는 보다 명확한 구간을 파악하는 것이 유의미할 수 있다.

Keywords

References

  1. Kang YO, Son SR, Cho NH. 2017. Analysis of Traffic Accidents Injury Severity in Seoul using Decision Trees and Spatiotemporal Data Visualization. Journal of Cadastre & Land InformatiX 47(2): 233-254. https://doi.org/10.22640/LXSIRI.2017.47.2.223
  2. Sung BJ, Bae GH, Yoo HH. 2015. Analysis of Temporal and Spatial Distribution of Traffic Accidents in Jinju. The Journal of Korean Society for Geospatial Information Science. 23(2): 3-9.
  3. Son SR, Kang YO. 2017. Spatio-temporal Pattern of Traffic Accident of Female Drivers in Seoul, Journal of the Korean Cartographic Association, 17(2): 89-98.
  4. Lee SJ, Cho HS, Song WH, Sohn HG. 2015. A Study on Spatial Characteristic and Influence Factor of Traffic Accident in Seoul. Korean Society for Geospatial Information Science.. 132-133.
  5. Hong SK. 1998. Developing a Visualization System for Spatio - Temporal Linear Point Data. The Journal of Korean Urban Geographical Society. 1(1): 85-100.
  6. Anderson TK. 2009. Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention. 41(3): 359-364. https://doi.org/10.1016/j.aap.2008.12.014
  7. Ceder A, Livneh M. 1978. Further evaluation of the relationships between road accidents and average daily traffic. Accident Analysis & Prevention, 10(2): 95-109. https://doi.org/10.1016/0001-4575(78)90018-0
  8. Chen C, Zhang G, Liu XC, Ci Y, Huang H, Ma J, Chen Y, Guan H. 2016. Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation. Accid Anal Prev. 97: 69-78. https://doi.org/10.1016/j.aap.2016.07.031
  9. Elvik R. 2013. Risk of road accident associated with the use of drugs: a systematic review and meta-analysis of evidence from epidemiological studies. Accid Anal Prev. 60: 254-267. https://doi.org/10.1016/j.aap.2012.06.017
  10. Erdogan S, Yilmaz I, Baybura T, Gullu M. 2008. Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar. Accident Analysis & Prevention, 40(1): 174-181. https://doi.org/10.1016/j.aap.2007.05.004
  11. Kang YO, Cho NH, Son SR. 2018. Spatiotemporal characteristics of elderly population's traffic accidents in Seoul using space-time cube and space-time kernel density estimation. PLoS one, 13(5): e0196845. https://doi.org/10.1371/journal.pone.0196845
  12. Levine N, Kim KE, Nitz LH. 1995. Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. Accident Analysis & Prevention, 27(5): 663-674. https://doi.org/10.1016/0001-4575(95)00017-T
  13. McSwiggan G, Baddeley A, Nair G. 2017. Kernel density estimation on a linear network. Scandinavian Journal of Statistics, 44(2): 324-345. https://doi.org/10.1111/sjos.12255
  14. Ng JC, Hauer E. 1989. Accidents on rural two-lane roads: differences between seven states (with discussion and closure) (No. 1238).
  15. Okabe A, Okunuki KI, Shiode S. 2006. SANET: a toolbox for spatial analysis on a network. Geographical analysis, 38(1): 57-66. https://doi.org/10.1111/j.0016-7363.2005.00674.x
  16. Okabe A, Sugihara K. 2012. Spatial analysis along networks: statistical and computational methods. John Wiley & Sons.
  17. Openshaw S. 1984. Ecological fallacies and the analysis of areal census data. Environment and planning A, 16(1): 17-31. https://doi.org/10.1068/a160017
  18. Prasannakumar V, Vijith H, Charutha R, Geetha N. 2011. Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia Soc Behav Sci. 21: 317-325. https://doi.org/10.1016/j.sbspro.2011.07.020
  19. Romano B, Jiang Z. 2017. Visualizing Traffic Accident Hotspots Based on Spatial-Temporal Network Kernel Density Estimation. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (p. 98). 2017, November. ACM.
  20. Xie Z, Yan J. 2008. Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5): 396-406. https://doi.org/10.1016/j.compenvurbsys.2008.05.001
  21. Yamada I, Thill JC. 2004. Comparison of planar and network K-functions in traffic accident analysis. Journal of Transport Geography, 12(2): 149-158. https://doi.org/10.1016/j.jtrangeo.2003.10.006
  22. 도로교통공단, http://www.index.go.kr

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