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2026 Vol.44, Issue 5 Preview Page

Article

Identification and Spatial Analysis of Risky Parking by Shared E-Scooter Using YOLOv8

YOLOv8 기반 공유 전동킥보드 위험주차 식별 및 유형･공간 분석

Doyun KIM1
,
Yeonghyeon LEE2
,
Minwook KIM3
,
Tai-jin SONG4*
김 도윤1
,
이 영현2
,
김 민욱3
,
송 태진4*
1Graduate Student, Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Korea
2M.E., Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Korea
3Ph.D. Student, Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Korea
4Associate Professor, Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Korea
1충북대학교 도시공학과 학석사연계과정
2충북대학교 도시공학과 석사
3충북대학교 도시공학과 석박사통합과정
4충북대학교 도시공학과 부교수
*Corresponding Author
31 October 2026. pp. 832-850
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Abstract

While dockless shared e-scooters have significantly enhanced urban mobility, their disorderly parking behavior continues to threaten pedestrian movement and overall urban safety. Existing studies have primarily focused on regulatory compliance, leaving a conceptual definition and quantitative identification of actual risky parking underexplored. This study redefines risky parking into location-related, state-related, and mixed risks, and analyzes 21,936 return images collected in Dongtan-gu, Hwaseong-si, Korea. To process the large-scale observational data automatically, a YOLOv8-based object detection approach combined with rule-based evaluation was applied, and the spatial distribution and type-specific determinants of risky parking were examined through Local Moran’s I and multinomial logistic regression. The results show that risky parking is not randomly distributed but is spatially clustered around transit nodes and commercial centers. Parking on tactile paving blocks accounted for 49% of all risky parking cases, emerging as the single most dominant cause, and the contributing factors of risky parking were found to vary across types under different environmental conditions. Spatial overlay analysis with complaint data further revealed that the identified hotspots, accounting for only 16.09% of all grid cells, captured 43.12% of all complaints, demonstrating the practical relevance of the identification results. This study contributes by presenting a novel analytical framework that integrates computer vision and spatial analysis to quantitatively identify and understand the structural mechanisms of risky parking.

Keywords
personal mobility
risky parking
shared E-Scooter
spatial analysis
YOLOv8

도크리스 기반 공유 전동킥보드는 도시 내 이동 편의성을 크게 향상시켰으나, 무질서한 주차로 인해 보행자 통행과 도시 안전을 저해하는 문제가 지속적으로 제기되고 있다. 그러나 기존 연구는 주로 법규 위반 여부에 초점을 맞추어 왔기 때문에, 합법적 위치에 주차되었더라도 보행 안전을 위협하는 위험주차를 체계적으로 정의하고 정량적으로 식별하는 데 한계를 지녔다. 이에 위험주차를 주차위치 위험, 주차상태 위험, 복합 위험으로 재정의하고, 화성시 동탄구 일대에서 수집된 21,936건의 반납 이미지 데이터를 분석하였다. 대규모 관측 자료의 자동화 처리를 위해 YOLOv8 기반 객체 탐지 모델과 규칙 기반 평가를 결합한 식별 프레임워크를 적용하였으며, Local Moran’s I와 다항 로지스틱 회귀분석을 통해 위험주차의 공간적 분포 특성과 유형별 유발요인을 분석하였다. 분석 결과, 위험주차는 무작위로 발생하지 않고 교통 결절점과 상업기능 밀집 지역을 중심으로 강한 공간적 군집 패턴을 보였다. 또한 점자블록 위 주차가 전체 위험주차의 49%를 차지하며 단일 원인으로 가장 큰 비중을 차지하였고, 위험주차 유발요인은 유형별로 상이한 환경 조건에서 형성되는 것으로 나타났다. 민원데이터와의 공간적 정합성 분석에서는 위험주차 핫스팟 지역이 전체 격자의 16.09%에 불과함에도 전체 민원의 43.12%가 이 지역에 집중되어, 식별 결과의 현실 적합성이 확인되었다. 컴퓨터 비전 기술과 공간 분석을 융합하여 공유 전동킥보드의 위험주차를 정량적으로 식별하고 그 구조적 특성을 규명하는 새로운 분석 프레임워크를 제시한다는 점에서 의의를 가진다.

키워드
개인형 이동수단
위험주차
공유 전동킥보드
공간 분석
YOLOv8
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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 : 44
  • No :5
  • Pages :832-850
  • Received Date : 2026-04-30
  • Revised Date : 2026-05-27
  • Accepted Date : 2026-07-09
  • DOI :https://doi.org/10.7470/jkst.2026.44.5.832
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