Adaptive Traffic Signal Control with Deep Reinforcement Learning An Exploratory Investigation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2019
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| _version_ | 1866916532893777920 |
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| author | Muresan, Matthew Fu, Liping Pan, Guangyuan |
| author_facet | Muresan, Matthew Fu, Liping Pan, Guangyuan |
| contents | This paper presents the results of a new deep learning model for traffic signal control. In this model, a novel state space approach is proposed to capture the main attributes of the control environment and the underlying temporal traffic movement patterns, including time of day, day of the week, signal status, and queue lengths. The performance of the model was examined over nine weeks of simulated data on a single intersection and compared to a semi-actuated and fixed time traffic controller. The simulation analysis shows an average delay reductions of 32% when compared to actuated control and 37% when compared to fixed time control. The results highlight the potential for deep reinforcement learning as a signal control optimization method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1901_00960 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Adaptive Traffic Signal Control with Deep Reinforcement Learning An Exploratory Investigation Muresan, Matthew Fu, Liping Pan, Guangyuan Systems and Control This paper presents the results of a new deep learning model for traffic signal control. In this model, a novel state space approach is proposed to capture the main attributes of the control environment and the underlying temporal traffic movement patterns, including time of day, day of the week, signal status, and queue lengths. The performance of the model was examined over nine weeks of simulated data on a single intersection and compared to a semi-actuated and fixed time traffic controller. The simulation analysis shows an average delay reductions of 32% when compared to actuated control and 37% when compared to fixed time control. The results highlight the potential for deep reinforcement learning as a signal control optimization method. |
| title | Adaptive Traffic Signal Control with Deep Reinforcement Learning An Exploratory Investigation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/1901.00960 |