Open AccessDeep Q-network-based traffic signal control models
Author(s) -
Sang Min Park,
Eum Han,
Sungho Park,
Harim Jeong,
Ilsoo Yun
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0256405
Subject(s) - synchro , intersection (aeronautics) , signal (programming language) , computer science , reinforcement learning , traffic signal , traffic generation model , artificial intelligence , signal timing , real time computing , engineering , transport engineering , electrical engineering , programming language
Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method.