Deep Reinforcement Learning for Traffic Signal Control Model and Adaptation Study

Deep reinforcement learning provides a new approach to solving complex signal optimization problems at intersections. Earlier studies were limited to traditional traffic detection techniques, and the obtained traffic information was not accurate. With the advanced in technology, we can obtain highly...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-11, Vol.22 (22), p.8732
Main Authors: Tan, Jiyuan, Yuan, Qian, Guo, Weiwei, Xie, Na, Liu, Fuyu, Wei, Jing, Zhang, Xinwei
Format: Article
Language:English
Subjects:
Acclimatization
Algorithms
Communication
Data collection
Deep learning
deep reinforcement learning
Efficiency
Information Storage and Retrieval
Intersections
Learning
Methods
Optimization
Reinforcement, Psychology
Researchers
Roads & highways
sensor technology
Sensors
signal optimization
state space
Traffic congestion
Traffic control
Traffic flow
Traffic models
Traffic signals
Vehicles
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Summary:Deep reinforcement learning provides a new approach to solving complex signal optimization problems at intersections. Earlier studies were limited to traditional traffic detection techniques, and the obtained traffic information was not accurate. With the advanced in technology, we can obtain highly accurate information on the traffic states by advanced detector technology. This provides an accurate source of data for deep reinforcement learning. There are many intersections in the urban network. To successfully apply deep reinforcement learning in a situation closer to reality, we need to consider the problem of extending the knowledge gained from the training to new scenarios. This study used advanced sensor technology as a data source to explore the variation pattern of state space under different traffic scenarios. It analyzes the relationship between the traffic demand and the actual traffic states. The model learned more from a more comprehensive state space of traffic. This model was successful applied to new traffic scenarios without additional training. Compared our proposed model with the popular SAC signal control model, the result shows that the average delay of the DQN model is 5.13 s and the SAC model is 6.52 s. Therefore, our model exhibits better control performance.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22228732