논문 상세보기
pp.113-122
PURPOSES : In this study, the existence of an optimal pattern among transition methods applied during changes in traffic signal timing was investigated. We aimed to develop this pattern into an artificial intelligence reinforcement-learning model to assess its effectiveness METHODS : By developing various traffic signal transition scenarios and considering 19 different traffic signal transition situations that can be applied to these scenarios, a simulation analysis was performed to identify patterns through statistical analysis. Subsequently, a reinforcement-learning model was developed to select an optimal transition time model suitable for various traffic conditions. This model was then tested by simulating a virtual experimental center environment and conducting performance comparison evaluations on a daily basis. RESULTS : The results indicated that when the change in the traffic signal cycle length was less than 50% in the negative direction, the subtraction method was efficient. In cases where the transition was less than 15% in the positive direction, the proposed center method for traffic signal transition was found to be advantageous. By applying the proposed optimal transition model selection, we observed that the transition time decreased by approximately 70%. CONCLUSIONS : The findings of this study provide guidance for the next level of traffic signal transitions. The importance of traffic signal transition will increase in future AI-based traffic signal control methods, requiring ongoing research in this field.
1. 서론
2. 선행연구 점검 및 본 연구 의의
3. 연구 방법
3.1. 다양한 교통조건별 최적 전이기법 패턴 파악
3.2. 최적 전이기법 선택 AI 모형 개발
4. 연구 결과
4.1. 적정 교통신호 전이 패턴 분석 결과
4.2. 전이기법 자동 선택 모형 개발
4.3. 최적 전이기법 적용 효과
5. 결론
감사의 글
REFERENCES
