논문 상세보기
pp.61-69
This study evaluated the safety impact of automated traffic enforcement cameras targeting tailgating behavior at signalized intersections by comparing traffic conditions shortly after installation and one year later. The Kukkiwon intersection in Gangnam-gu, Seoul, South Korea was selected as the study site. Individual vehicle speeds, accelerations, and subsequent distances were extracted from video data using YOLOv8 and ByteTrack, which are advanced deep learning-based object detection and tracking algorithms. Surrogate safety measures (SSM), such as time to collision (TTC), modified time to collision (MTTC), and proportion of stopping distance (PSD), were calculated to assess changes in traffic safety. Every SSM indicated an improvement one year after the installation of enforcement cameras, suggesting a reduction in collision risks. In particular, the PSD indicator showed a notable improvement, reflecting a better maintenance of safe following distances. These results highlight the effectiveness of automated enforcement in improving intersection safety and suggest its scalability to other intersections with similar tail-gating issues. Future research should explore the long-term and multisite effects using diverse intersection types and behavioral indicators.
1. 서론
2. 선행연구
2.1. 대리안전지표
2.2. YOLO(You Only Look Once)
2.3. 시사점
3. 방법론
3.1. 연구 방법론
3.2. 연구 범위
3.3. 활용 지표
3.4. 개별 차량 데이터 추출
4. 분석 결과
4.1. 기초 통계 분석
4.2. 대리안전지표 분석 결과
5. 결론
REFERENCES
