Abstract:Urban traffic congestion presents a significant challenge for modern cities, which impacts mobility and sustainability. Traditional traffic light control systems often fail to adapt to dynamic conditions, leading to inefficiencies. This paper proposes a novel deep reinforcement learning agent for traffic light control that addresses this limitation by explicitly integrating fairness considerations for both vehicular and pedestrian traffic. Unlike prior work, our approach dynamically balances these flows based on real-time demand, moving beyond systems focused solely on vehicles. Experimental results demonstrate that our agent effectively reduces congestion while ensuring equitable service for both the categories of road users. This research contributes to a practical and adaptable solution for intelligent traffic management within the framework of smart cities, paving the way for more efficient and inclusive urban mobility.
| Comments: | Paper accepted to the 2026 IFAC World Congress, held in Busan (KOR), August 23rd-28th, 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.10170 [cs.LG] |
| (or arXiv:2605.10170v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10170 arXiv-issued DOI via DataCite (pending registration) |
From: Matteo Cederle [view email]
[v1]
Mon, 11 May 2026 08:19:18 UTC (77 KB)
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