![](/images/graphics-bg.png)
Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning
Joint Authors
Xu, Ming
Li, Duowei
Wu, Jianping
Wang, Ziheng
Hu, Kezhen
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-03
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time.
However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem.
To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning.
The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions.
The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model.
To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases.
The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network.
By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases.
The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex.
American Psychological Association (APA)
Li, Duowei& Wu, Jianping& Xu, Ming& Wang, Ziheng& Hu, Kezhen. 2020. Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1175881
Modern Language Association (MLA)
Li, Duowei…[et al.]. Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning. Journal of Advanced Transportation No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1175881
American Medical Association (AMA)
Li, Duowei& Wu, Jianping& Xu, Ming& Wang, Ziheng& Hu, Kezhen. Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1175881
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175881