논문 국내 국내전문학술지(KCI급) 드론 기반 객체 탐지와 유전 알고리즘을 활용한 적응형 교통신호 제어의 적용 가능성 연구
- 학술지 구분 국내전문학술지(KCI급)
- 게재년월 2026-06
- 저자명 추동준, 김민송, 김세헌, 도명식
- 학술지명 한국도로학회 논문집
- 발행국가 국내
- 논문언어 한국어
- 전체저자수 4
- 연구분야 공학 > 교통공학
- 원문 링크 https://doi.org/10.7855/IJHE.2026.28.3.161
홈페이지논문 초록 (Abstract)
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.