Adaptive traffic signal control using reinforcement learning for minimizing vehicle emissions = 배기가스 저감을 위한 강화학습 기반 적응형 교차로 신호제어 연구

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Adaptive Traffic Signal Control (ATSC) is a technique to reduce traffic congestion by controlling intersection traffic signal efficiently. Early ATSC controlled intersection signal with a centralized computer and it is applied on actual roads, mainly in North America and Europe. Distributed ATSC has been attracting attention recently because of the increasing demand for real-time and scalability. In my thesis, ATSC using Reinforcement Learning (RL) is introduced. ATSC using RL is one of the distributed ATSC techniques and is expected to overcome the constraints of existing technology. To implement an algorithm applicable to more realistic environments, various vehicle parameters are considered. Deep Q-Network (DQN) is used as a function approximation technique. Normal-mode algorithm is designed for general purpose of ATSC and eco-mode algorithm is designed focusing on minimizing vehicle emissions. The results are verified for three scenarios compared with the optimal signal transition following delay model using Simulation of Urban MObility (SUMO).
Advisors
Kong, Seung-Hyunresearcher공승현researcher
Description
한국과학기술원 :로봇공학학제전공,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 로봇공학학제전공, 2019.2,[vi, 60 p. :]

Keywords

Reinforcement learning▼aadaptive traffic signal control▼avehicle emission▼aDQN▼aSUMO; 강화학습▼a적응형 교차로 신호제어▼a배기가스 저감 기술▼aDQN▼aSUMO

URI
http://hdl.handle.net/10203/265997
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843067&flag=dissertation
Appears in Collection
RE-Theses_Master(석사논문)
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