박사
AI 기법을 이용한 교통신호 제어 모형 개발 = Development of Traffic Signal Control Model Using AI Technique
박상민
2020년
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AI 기법을 이용한 교통신호 제어 모형 개발 = Development of Traffic Signal Control Model Using AI Technique' 의 주제별 논문영향력
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| 동일주제 총논문수 | 논문피인용 총횟수 | 주제별 논문영향력의 평균 |
| 1,050 | 0 |
0.0% |
주제별 논문영향력
' AI 기법을 이용한 교통신호 제어 모형 개발 = Development of Traffic Signal Control Model Using AI Technique' 의 참고문헌
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한음, 윤일수, 이상수, 장기태, 박병규, V2I 통신환경을 활용한 연동교차로 교통 신호 실시간 제어 연구
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한음, V2I 기반 BSM 정보를 활용한 독립교차로 교통신호 제어 방법론 개발, 아 주대학교 일반대학원
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한국교통연구원, 전국 교통혼잡비용 추정과 추이 분석
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통행시간 자료를 이용한 신호시간계획의 결정 방법
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심층강화학습 기반 지능형 교통신호제어에 대한 연구, 가천대학교 대학 원
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대기 차량 최소화를 위한 동적 교통 신호연동 모델
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기계학습
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국토교통부
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교통공학원론(상)
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경찰청
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경찰청
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경기개발연구원
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Value-based Deep Reinforcement Learning for Adaptive Isolated Intersection Signal ControlVol . 12 , Iss . 9 , pp . 1005-1010 [2018]
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Using a Deep Reinforcement Learning Agent for Traffic Signal Control[2016]
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Simple Reinforcement Learning with Tensorflow[2017]
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Reinforcement Learning with Function Approximation for Traffic SignalControlVol . 12 , No . 2 , 412-421 [2011]
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Reinforcement Learning for True Adaptive Traffic SignalControlVol . 129 , Iss . 3 , pp . 278-285 [2003]
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Reinforcement Learning An Introduction Second Edition[2007]
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Reinforcement Learning An Introduction , A bradford Book[1998]
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Natural Actor-Critic for Road Traffic Optimisation[2006]
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Multi-agent Reinforcement Learning for Traffic Signal Control[2014]
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M. C. , Cheu , R. L. , Neural Networks for Real-Time Traffic Signal ControlVol . 7 , No . 3 , pp . 261-272 [2006]
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L. Traffic Signal Timing via Deep Reinforcement LearningVol . 3 , No . 3 , pp . 247-254 [2016]
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K. W. The Sydney Coordinated Adaptive Traffic ( SCAT ) System Philosophy and Benefits29 ( 2 ) , pp . 130-137 , [1980]
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IntelliLight : A Reinforcement Learning Approach for Intelligent Traffic Light Control , KDD 2018 , pp.2496 ? 2505[2018]
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Implementation of the OPAC adaptive control strategy in a traffic signal networkpp . 195-200 [2001]
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Human-level control through deep reinforcement learning[2015]
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Evaluation of Adaptive Macimum Feature in Actuated Traffic Controller : Hardware-in-the-Loop SimulationVol . 2035 , Iss 1 [2007]
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Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based DataVol . 21 , Iss . 8 [2019]
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Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks[2018]
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Deep Reinforcement Learning Hands-On[2018]
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Deep Reinforcement Learning[2018]
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Deep Reinforcement Learing for Coordination in Traffic Light Control[2016]
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Deep Q-Network(DQN)을 이용한 도시 간선도로망 교통신호제어 연구, 중앙대학교 대학원
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D. I. , Bretherton , R. D. Winton , R. I. , SCOOT-a Traffic Responsive Method of CoordinatingSignals ( No . LR 1014 Monograph [1982]
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Cycle-by-Cycle Queue Length Estimation for Signalized Intersections Using Sampled Trajectory Data ,Vol . 2257 , pp . 87-94 [2011]
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CS234 : Reinforcement Learning Winter[2019]
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Adaptive Traffic Signal Control : Deep Reinforcement Learning Algorithm with Experience Replay and Target Network[2017]
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Adaptive Traffic Control Systems : Domestic and Foreign State of PracticeNo . Project 20-5 ( Topic 40-03 [2010]
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A Method for Stochastic Optimization[2015]
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A Group-based Traffic Signal Control with Adaptive Leaning Ability , Engineering Applications of Artificial IntelligenceVol . 65 , pp . 282-293 [2017]