Identifying Micro-Level Hotspots of Urban Network Infrastructures Using GIS Data

Seung-oh Son; Juneyoung Park

doi:10.7470/jkst.2022.40.4.585

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2022 Vol.40, Issue 4 Preview Page Next Page

Article

Identifying Micro-Level Hotspots of Urban Network Infrastructures Using GIS Data GIS 데이터를 활용한 도시부 인프라 세부위험구역 식별 방법론 개발

31 August 2022. pp. 585-598

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Abstract

This study develops a methodology for identifying micro-level hotspots of urban network infrastructures using GIS data. In general, the identifying hotspots is performed in intersections and segments. Therefore, when the particular intersections and segments are identified as a hotspot, it is common to consider safety design that takes into account the characteristics of the intersections or segments. However, since these studies are macroscopic, additional analysis is required because the characteristics inside each infrastructure are not reflected in the identification process. In particular, in terms of micro-level, a detailed analysis is needed to consider the crashes that occurred at the infrastructure, the geometry of the intersection, and the traffic characteristics. In this study, crash severity analysis for urban intersections was performed using the random forest methodology, a data mining technique, and the quantified crash scores were estimated through modeling results. The micro-level hotspots were visualized and analyzed through kernel density estimation and inverse distance weighted interpolation by mapping the estimated indicators to the GIS environment, and annual changes of micro-level hotspots were analyzed. In the future, the analysis results of this study are expected to enable detailed safety policy design and safety facility selection in consideration of the spatial and content characteristics of the micro-level hotspots of the intersection.

Keywords

crash severity model

geographic information system

inverse distance weighted interpolation

kernel density estimation

micro-level hotspot

urban intersections

본 연구에서는 GIS 데이터를 활용한 도심 내 주요 교통인프라에서의 세부위험구역 식별 방법론을 개발하였다. 일반적으로 사고위험구역 식별은 구간, 교차로 단위로 수행되며 여기서의 그룹이 분석단위로 설정된다. 따라서 특정 교차로 및 구간이 위험구역(Hotspot)으로 설정되며 해당 구간의 특성을 고려한 안전대책 설계가 고려되는 것이 일반적이다. 다만 이러한 연구는 네트워크 관점의 분석으로 각 교차로 내부의 특성이 연구에 반영되지 않아 추가적이 분석이 요구되며, 특히 내부에서 발생한 사고와 해당 시설 고유의 특성을 고려한 분석이 필요하다. 이러한 연구가 필요한 주요 인프라에는 교차로, 환승시설, 합류부, 분류부 등이 있다. 본 연구에서는 도심 내 대표적인 주요 인프라인 교차로를 분석대상으로 하여 시설 내에서의 세부위험구역 식별 연구를 수행하였다. 발생한 사고의 내용을 고려하기 위해 데이터마이닝 기법인 랜덤포레스트 방법론을 활용하여 사고심각도 분류분석을 수행하였으며, 변수중요도 결과를 통해 정량화된 사고점수를 추정하였다. 추정된 지표를 GIS 환경에 맵핑하여 커널밀도추정(Kernel density estimation)과 역거리 가중치 보간법(Inverse distance weighted interpolation)을 적용하여 세부위험구역을 분석하였고, 연도별 변화하는 위험구역에 대해서 분석을 수행하였다. 향후 본 연구의 분석결과는 도심 내 주요 인프라에서 사고 GIS 데이터를 근거로 식별된 세부위험구역이 의미하는 공간적, 내용적 특성을 고려하여 세밀한 안전정책 설계 및 안전시설물 선택에 활용이 가능할 것으로 기대된다.

키워드

사고심각도 분류분석

지리정보시스템

역거리가중치보간법

커널밀도추정

세부위험구역

도심부 인프라

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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 : 40
No :4
Pages :585-598
Received Date : 2022-06-14
Revised Date : 2022-06-27
Accepted Date : 2022-07-13
DOI :https://doi.org/10.7470/jkst.2022.40.4.585

Journal of Korean Society of Transportation ISSN:1229-1366(Print) 2234-4217(Online) 대한교통학회지

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