논문 상세보기
pp.161-170
Urban traffic congestion continues to intensify owing to rapid urbanization and growing vehicle ownership, highlighting the limitations of fixed-time signal control systems. This paper proposes a real-time traffic signal optimization framework that integrates drone-based object detection and tracking with a genetic algorithm and rolling horizon structure. Traffic data were collected at the Hakha intersection in Daejeon, South Korea, using DJI M300 RTK and Mavic 3E drones during the evening peak hours. Vehicle detection and tracking were performed using YOLOv8n and ByteTrack, achieving an average detection accuracy of 88–98% for the total approach volume and 84–94% for the through-movement volume. The extracted traffic parameters (volume, delay, and queue length) were incorporated into a multi-objective fitness function with weights determined via the analytic hierarchy process. The optimized signal plans were validated using VISSIM microsimulation against a fixed-time baseline. Results show that the proposed framework reduced the average vehicle delay by 31.4% (37.91 to 25.99 sec/veh), stop delay by 37.1%, and average queue length by 34.8% (8.41 to 5.48 m), while improving the intersection level of service from D to C without sacrificing network throughput. This study demonstrated the practical feasibility of an integrated framework combining drone-based mobile sensing and metaheuristic optimization for real-time adaptive signal control.
1. 서론
2. 선행연구 고찰
2.1. 드론 영상 기반 교통 데이터 수집 관련 연구
2.2. 메타휴리스틱 기반 교통신호 최적화 연구
2.3. 연구의 차별성
3. 방법론
3.1. 데이터 수집
3.2. 드론 객체 탐지 및 추적 방법
3.3. 실시간 신호 최적화 방법
4. 결과 및 고찰
4.1. 객체 탐지 모델 성능 평가
4.2. 차량 지체와 대기행렬길이 평가
4.3. 실시간 신호 최적화 결과
5. 결론
감사의 글
REFERENCES
