Draft:Traffic Signal Control

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Instructions · What links here · Traffic Signal Control (talk: + · bio) · (log) · Copyvios report · reFill · Citation Bot · (Search: Google, Wikipedia) · Submitted 37 days ago by Gpnupaper (talk: D · +) · Last edited 0 seconds ago

Comment: In accordance with Wikipedia's Conflict of interest policy, I disclose that I have a conflict of interest regarding the subject of this article. Gpnupaper (talk) 08:28, 4 April 2025 (UTC)

Traffic signal control refers to the use of traffic lights and optimization algorithms to manage vehicle and pedestrian flow at intersections, aiming to reduce congestion, improve safety, and enhance fuel efficiency. Modern approaches leverage computational methods such as reinforcement learning (RL), genetic programming (GP), and multi-agent systems, with recent advancements focusing on explainability and urban network scalability^[1]^[2].

^ Yuan, Hao; Yu, Haiyang; Gui, Shurui; Ji, Shuiwang (May 2023). "Explainability in Graph Neural Networks: A Taxonomic Survey". IEEE Transactions on Pattern Analysis and Machine Intelligence. 45 (5): 5782–5799. arXiv:2012.15445. doi:10.1109/TPAMI.2022.3204236. ISSN 1939-3539. PMID 36063508. Retrieved 2025-03-11.
^ Xie, Guorui; Li, Qing; Jiang, Yong (2021-09-04). "Self-attentive deep learning method for online traffic classification and its interpretability". Computer Networks. 196: 108267. doi:10.1016/j.comnet.2021.108267. ISSN 1389-1286. Retrieved 2025-03-11.

[1] Yuan, Hao; Yu, Haiyang; Gui, Shurui; Ji, Shuiwang (May 2023). "Explainability in Graph Neural Networks: A Taxonomic Survey". IEEE Transactions on Pattern Analysis and Machine Intelligence. 45 (5): 5782–5799. arXiv:2012.15445. doi:10.1109/TPAMI.2022.3204236. ISSN 1939-3539. PMID 36063508. Retrieved 2025-03-11.

[2] Xie, Guorui; Li, Qing; Jiang, Yong (2021-09-04). "Self-attentive deep learning method for online traffic classification and its interpretability". Computer Networks. 196: 108267. doi:10.1016/j.comnet.2021.108267. ISSN 1389-1286. Retrieved 2025-03-11.

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