mlsanityData to 14 Oct 2026

Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning

arXiv 2605.10170 ↗First posted 11 May 2026cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 23 references that are in the map. First posted on arXiv 11 May 2026.

Builds on

5 of 23 references.

References · 5

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 73
  3. 2018Deep Reinforcement Learning for Traffic Light Control in Vehicular Networkscited by 52
  4. 2017Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learningcited by 33
  5. 2024A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Servicescited by 3

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  1. 2024MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning
  2. 2025Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
  3. 2024Applying Reinforcement Learning to Optimize Traffic Light Cycles
  4. 2022CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning
  5. 2024MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control
  6. 2025A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management
  7. 2025Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning
  8. 2024X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner