Bicycle crashes in urban areas are frequently concentrated at intersections where motor vehicles, pedestrians, and cyclists share the same roadway space. Therefore, there is an increasing need for a hotspot identification methodology that considers both the spatial characteristics of crash locations and crash severity. However, conventional hotspot identification methods primarily rely on crash frequencies aggregated by administrative districts or intersections, making it difficult to identify the specific hazardous locations within intersections where crashes actually occur. This study proposes a methodology for identifying urban bicycle crash hotspots by incorporating the spatial characteristics of bicycle crashes. Bicycle crash data collected in Gwangsan-gu, Gwangju Metropolitan City, from 2020 to 2024 were analyzed using the Traffic Accident Analysis System (TAAS) Geographic Information System (GIS). The distance between each crash location and the nearest intersection corner was calculated to represent the spatial influence of intersections. Crash severity was quantified using Equivalent Property Damage Only (EPDO) weights, while distance-based weights were established from the cumulative distribution of crash locations. A bicycle crash risk index was developed by integrating crash severity and distance weights, enabling differentiated risk evaluation even for locations with identical crash frequencies. Furthermore, sensitivity analysis was conducted by varying the distance-weighting scheme to verify the robustness and applicability of the proposed methodology. The results demonstrated that the proposed approach identified hazardous locations more precisely than conventional intersection-based methods and maintained stable ranking results. The proposed methodology can improve the objectivity and reliability of bicycle hotspot identification and provide practical support for prioritizing bicycle safety improvement projects, planning intersection safety measures, establishing transportation safety policies, and allocating limited public resources more efficiently.

ABSTRACT
1. 서론
2. 선행연구 고찰
3. 자전거 교통사고 데이터 분석 방법론
    3.1. 데이터 분석 범위 설정
    3.2. 위치기반 위험지점 선정 지표
    3.3. 심각도 가중치와 거리를 고려한 위험지점 선정
4. 자전거 위험지점 선정
5. 결론
REFERENCES

• 전우훈(한국건설기술연구원 도로교통연구본부 연구위원) | Jeon Woo Hoon Corresponding author
• 이유화(한국건설기술연구원 도로교통연구본부 연구위원) | Lee Yuhwa