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Article

Determining the Spatial Ranges and Characteristics of Pedestrian Hot Spots in Seoul Using L-function and KDE

L-function과 KDE를 이용한 서울시 보행교통사고 잦은 곳의 공간적 범위 설정과 특성 분석

Junghyun HWANG1
,
Joonho KO2*
황 정현1
,
고 준호2*
1Ph.D. Course, Graduate School of Urban Studies, Hanyang University, Seoul 04763, Korea
2Professor, Graduate School of Urban Studies, Hanyang University, Seoul 04763, Korea
1한양대학교 도시대학원 박사과정
2한양대학교 도시대학원 교수
*Corresponding Author
30 June 2024. pp. 313-330
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Abstract

This study aimed to undertake a comprehensive spatial analysis to delineate optimal spatial ranges for identifying frequent pedestrian crash locations. The analysis focused on pedestrian crashes that occurred within the seoul metropolitan government area between 2016 and 2022, selected due to its significantly high ratio of pedestrian fatalities compared to overall traffic accident fatalities among metropolitan jurisdictions. To investigate the spatial dependence among point patterns of pedestrian crash data, ripley's k-function and l-function were employed to analyze the degree of clustering by distance. The findings indicate that pedestrian crashes each year exhibited clustered patterns characterized by spatial dependence among individual crash points, with clustering evident within distances of 30.7 meters as indicated by the rate of increases in l-function values, gradually tapering off beyond this range. Furthermore, a similar analysis conducted on non-pedestrian crashes revealed clustering within distances of 35.7 meters, approximately 1.16 times the observed clustering range for pedestrian crashes. Based on the application of l-function selected results, which identified pedestrian crash hot spots, and reviewing the kernel density estimation within 5 meter increments up to a 50 meter radius, the total count of hot spots shows a gradual increase, with less than 10% within buffer radius of 30 to 35 meters. Beyond this distance, the rate of increase diminishes more steeply. Moreover, the results indicate that expanding the buffer radius includes accidents on adjacent roads. Therefore, selecting a minimum distance of 30 meters for hot-spot analysis is meaningful. Spatial analysis of pedestrian crash hot spots based on intersection, road width, and land use metrics revealed higher proportions of hot spots along secondary and tertiary roads compared to main arteries, with higher risk associated with unsignalized roads than signalized intersections. This underscores the necessity for ongoing pedestrian safety policies targeting residential roadways.

Keywords
hot-spot
kernel density estimation
pedestrian crash
ripley’s l-function
spatial analysis

본 연구는 보행교통사고 잦은 곳의 적정 공간적 범위 설정을 위해 보행교통사고에 대한 점 패턴 분석과 보행교통사고 잦은 곳의 공간적 특성을 분석하였다. 분석은 광역자치단체 중 전체 교통사고 사망자 대비 보행자 사망자 비율이 가장 높은 서울특별시의 2016년에서 2022년까지 발생한 보행교통사고를 대상으로 하였다. 보행교통사고 데이터의 점 패턴 간 공간 의존성을 분석하기 위해 Ripley의 K-function 및 L-function을 사용하여 거리별 군집화 정도를 분석한 결과, 각 연도별 보행교통사고는 개별 사고 간의 공간적 의존성을 지닌 군집화된 상태이며 공간적 패턴의 군집화는 30.7m 거리까지 L-function 값이 급격히 증가되고 그 이상의 거리에서는 서서히 완화되는 것으로 나타났다. 또한, 동일한 방법으로 차대차 및 차량단독 교통사고의 군집화를 분석한 결과는 35.7m의 거리로 나타나 보행교통사고의 군집 패턴보다 1.16배 큰 것으로 나타났다. L-function에 의해 선정된 결과의 적용 가능성을 Buffer 반경 5m에서 50m까지 5m 간격의 Kernel Density Estimation로 분석한 보행사고 Hot Spot을 통해 검증한 결과, Hot Spot의 총량은 Buffer 반경 30m~35m에서 10% 미만으로 완만하게 증가하고 있으며 해당 거리 이상에서 총량 증가폭은 더 완만해졌다. 하지만, Buffer 반경이 커질수록 인접도로의 교통사고를 포함하는 결과를 보여주고 있어 Hot Spot을 식별하는 최소 거리로 30m를 선택하는 것은 중요한 의미를 지닌다. 30m 반경 보행교통사고 잦은 곳의 공간적 특성 분석을 위해 교차로, 도로폭, 토지이용 등의 자료와 공간분석한 결과, 광로 및 대로 보다는 중로, 소로에서 보행교통사고 잦은 곳의 비율이 더 높으며, 교차로보다는 무신호 도로에서의 보행교통사고 위험성이 더 높은 것으로 분석되어 주거생활권 도로에 대한 보행안전정책이 지속적으로 추진되어야 함을 시사하고 있다.

키워드
사고잦은곳
밀도함수
보행교통사고
리플리의 L함수
공간분석
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Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 42
  • No :3
  • Pages :313-330
  • Received Date : 2024-04-15
  • Revised Date : 2024-05-10
  • Accepted Date : 2024-06-18
  • DOI :https://doi.org/10.7470/jkst.2024.42.3.313
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