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Deep Reinforcement Learning for Traffic Signal Control Model and Adaptation Study
Open accessAbstract
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.
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Most existing reinforcement-learning traffic signal control models require centralized control, which can become ineffective and susceptible to communication latency in extensive city networks.
“Most of these models, though, require centralized control, and it can become ineffective and susceptible to communication latency in the case of extensive city networks [8,9].”
From Multi-agent Reinforcement Learning for Real-time Traffic Control in 6G-Enabled VANET Environments (2025) · cites this paper as [8]
The Introduction says that cities have many intersections and that training a separate model for each takes significant time. It does not say that most reinforcement-learning traffic signal models require centralized control or that communication latency makes such control ineffective in extensive networks. Searches for centralized control, distributed control, and communication latency found no passage addressing those points. (LLM, conf=0.76)
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Metadata
- Authors
- Tan, J., Yuan, Q., Guo, W., Xie, N., Liu, F., Wei, J., & Zhang, X.
- Affiliations
- North China University of Technology (CN)
- Central University of Finance and Economics (CN)
- Corresponding author
- Xie, Na
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-11-11
- Volume
- 22
- Issue
- 22
- Pages
- 8732
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9693472
- OpenAlex
- W4308971790
- Cited by
- 32
- References
- 28
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 7 · 2023: 2
- FWCI
- 2.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- the National Key Research and Development Program of China (2018YFB1601100, CIT&TCD201904013, CIT&TCD201804006, KM202010009007)
- the Youth Top Talent Training Program of Beijing (2018YFB1601100, CIT&TCD201904013, CIT&TCD201804006, KM202010009007)
- the General Program of Science and Technology Plan of Beijing Municipal Education Commission (2018YFB1601100, CIT&TCD201904013, CIT&TCD201804006, KM202010009007)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Social Sciences › Transportation › Traffic Prediction and Management Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- traffic signal control, deep reinforcement learning, deep Q-network, soft actor-critic, traffic state estimation, state-space model, vehicle delay
- MeSH terms
- Acclimatization, Learning, Reinforcement, Psychology, Information Storage and Retrieval
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