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2019 Vol.37, Issue 4 Preview Page

Article

Estimation of Pedestrian Crash Exposure Considering Floating Population

유동인구를 고려한 보행사고 노출률 추정

Hasik LEE1
,
Eui-Jin KIM2
,
Su Jin PARK3
,
Seung-Young KHO4
,
Ho-Chul PARK5*
이 하식1
,
김 의진2
,
박 수진3
,
고 승영4
,
박 호철5*
1Master, Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
2Ph.D Candidate, Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
3Ph.D Course, Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
4Professor, Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
5Postdoctoral Researcher, Department of Civil, Environmental and Construction Engineering, University of Central Florida, Olrando, FL 32816-2450, United States
1서울대학교 건설환경공학부 석사과정
2서울대학교 건설환경공학부 박사수료
3서울대학교 건설환경공학부 박사과정
4서울대학교 건설환경공학부 교수
5University of Central Florida 박사후연구원
* Corresponding Author
31 August 2019. pp. 302-321
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Abstract

In vehicle-pedestrian crashes, it is necessary to reflect the crash exposure, which can be divided into vehicle traffic volume and pedestrian traffic volume, in order to accurately estimate the crash. However, it is difficult to measure the pedestrian traffic volume due to the pedestrian characteristics compared to vehicle traffic volume. Previous studies have estimated the pedestrian traffic volume by using a travel survey or demographics of traffic zones, but those are costly and have a limitation in reflecting pedestrian traffic patterns well. In this study, we estimate the pedestrian traffic volume by using smart card data in which actual pedestrian traffic patterns are reflected, and suggest a pedestrian safety performance functions based on this. The pedestrian traffic volume is derived by using the floating population around the public transit nodes, and the vehicle traffic volume is reflected as the length of the hierarchically classified road. The suggested method is applied to the city of Seoul, which has high public transportation mode shares, smart card data, and road GIS information. In this study, the Seoul area is divided into a grid of 500m×500m, and the number of expected crash occurrences in the grid sections is estimated by negative binomial regression. As a result, the McFadden pseudo R2 of the estimated model is 0.65, which indicates that the model could explain the vehicle-pedestrian crash well, and all the variables are statistically significant. In addition, based on the model, we select vulnerable sections of the vehicle-pedestrian crash and analyze the causes. This study shows that pedestrian safety evaluation based on smart card data can be utilized fully.

Keywords
negative binomial regression
pedestrian exposure
safety performance function
smart card data
vehicle-pedestrian crash

보행 교통사고를 정확히 추정하기 위해서는 사고 노출률인 보행 및 차량 통행량을 정확히 반영해야 한다. 그러나 보행사고 노출률에 있어 차량통행량에 비해 보행통행량은 보행특성으로 인해 측정에 어려움이 있다. 이를 보완하기 위해 기존 연구에서는 보행통행량을 직접 조사하거나 인구지표 등을 활용하여 추정하였으나 이는 많은 비용이 소요되며 보행 통행패턴을 반영하기에 한계가 있다. 본 연구에서는 이를 개선하기 위해 사람들의 통행패턴이 반영된 교통카드 자료를 이용하여 보행교통량을 산정하며 이를 바탕으로 보행안전성능함수를 제안한다. 보행교통량은 정류장 유동인구를 통해 추정되며, 차량교통량은 정해진 구간 내 위계별 도로 길이로써 반영하였다. 제안된 방법론은 대중교통 수단분담율이 높고, 교통카드 및 도로 GIS 정보가 구축되어 있는 서울시를 대상으로 적용된다. 본 연구에서는 서울시를 500m×500m 크기의 격자로 나누고, 해당 격자구간의 기대사고건수를 음이항 회귀분석을 통해 추정한다. 분석 결과 구축한 모형의 McFadden’s Pseudo R2의 값이 0.65로 나타나 보행사고건수에 대한 설명력이 높음을 확인하였고, 사용된 보행사고 노출률 변수들도 통계적으로 유의한 결과를 보인다. 또한 분석 결과를 이용하여 서울시 보행사고 및 교통약자 보행사고 취약 구간을 선정하고, 그 원인을 분석한다. 본 연구는 교통카드 자료를 기반으로 한 보행사고 안전성 평가가 충분히 활용될 수 있음을 보여준다.

키워드
음이항 회귀분석
보행노출률
안전성능함수
교통카드 자료
보행사고
References
1
American Association of State Highway and Transportation Officials (2010), Highway Safety Manual 1st Edition, Washington DC, United States of America.
2
Beck T. H., Park M. K., Park B. H. (2014), Pedestrian Accident Severity and Modeling by Arterial Road Function, Int. J. Highw. Eng., 16(4), Korean Society of Road Engineers, 111-118.
10.7855/IJHE.2014.16.4.111
3
Hauer E. (1982), Traffic Conflicts and Exposure, Accid. Anal. Prev., 14(5), Pergamon Press, 359-364.
10.1016/0001-4575(82)90014-8
4
Jeong Y., Moon T. (2014), Analysis of Seoul Urban Spatial Structure Using Pedestrian Flow Data: Comparative Study with ‘2030 Seoul Plan, Journal of the Korean Regional Development Association, 26(3), The Korean Regional Development Association, 139-158.
5
Keall M. D. (1995), Pedestrian Exposure to Risk of Road Accident in New Zealand, Accid. Anal. Prev., 27(5), Elsevier, 729-740.
10.1016/0001-4575(95)00019-V
6
Kim K., Lee I. (2017), A Public Transportation OD Estimation Method Using Smart Card Data, J. Korean Soc. Railways, 20(5), The Korean Society for Railway, 692-702.
10.7782/JKSR.2017.20.5.692
7
Kim M. (2012), The Research on the Accident with Pedestrian Danger Section Method for Choosing, University of Seoul, Master Degree Thesis.
8
Korea Highway Traffic Research Institute (2013), Highway Traffic Safety Manual, Hwaseong, South Korea.
9
Lam W. W. Y., Yao S., Loo B. P. Y. (2014), Pedestrian Exposure Measures: A Time-space Framework, Travel Behav. Soc. 1(1), Elsevier, 22-30.
10.1016/j.tbs.2013.10.004
10
Lee C., Abdel-Aty M. (2005), Comprehensive Analysis of Vehicle-pedestrian Crashes at Intersections in Florida, Accid. Anal. Prev., 37(4), Elsevier, 775-786.
10.1016/j.aap.2005.03.01915869737
11
Lee S., Kim C., Kim D. K., Lee C. (2013), Identifying Hotspots on Freeways Using the Continuous Risk Profile With Hierarchical Clustering Analysis, J. Korean Soc. Transp., 31(4), Korean Society of Transportation, 85-94.
10.7470/jkst.2013.31.4.085
12
Park C. Y., Lee S. (2016), An Analysis of the Characteristics of Street Environment Affecting Pedestrian Accidents, Journal of the Urban Design Institute of Korea Urban Design, 17(3), Urban Design Institute of Korea, 105-121.
13
Rhee K. A., Kim J. K., Lee Y. I., Ulfarsson G. F. (2016), Spatial Regression Analysis of Traffic Crashes in Seoul, Accid. Anal. Prev., 91, Elsevier, 190-199.
10.1016/j.aap.2016.02.02326994374
14
Seoul Metropolitan Government, Seoul 1 Day Traffic Statistics (Shares) by Transportation in 2017, http://data.seoul. go.kr/dataList/datasetView.do?infId=250&srvType=S&serviceKind=2&currentPageNo=1&searchValue=&searchKey=null, 2018.9.4.
15
The Road Traffic Authority (2013), Highway Traffic Safety Handbook, Won-ju, South Korea.
16
The Road Traffic Authority (2017), Comparison of Traffic Accidents in OECD Member Countries, Won-ju, South Korea.
17
World Health Organization (2015), Global Status Report On Road Safety 2015, Italy.
18
Yao S., Loo B. P. Y., Lam W. W. Y. (2015), Measures of Activity-based Pedestrian Exposure to the Risk of Vehicle-pedestrian Collisions: Space-time Path vs. Potential Path Tree Methods, Accid. Anal. Prev., 75, Elsevier, 320-332.
10.1016/j.aap.2014.12.00525555021
19
Zeeger C. V., Buschell M. (2012), Pedestrian Crash Trends and Potential Countermeasures from Around the World, Accid. Anal. Prev., 44, Elsevier, 3-11.
10.1016/j.aap.2010.12.00722062330
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 37
  • No :4
  • Pages :302-321
  • Received Date : 2019-04-08
  • Revised Date : 2019-05-15
  • Accepted Date : 2019-08-30
  • DOI :https://doi.org/10.7470/jkst.2019.37.4.302
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