mlsanityData to 14 Oct 2026

Adaptive traffic signal safety and efficiency improvement by multi objective deep reinforcement learning approach

arXiv 2408.00814 ↗First posted 1 Aug 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

This research introduces an innovative method for adaptive traffic signal control (ATSC) through the utilization of multi-objective deep reinforcement learning (DRL) techniques. The proposed approach aims to enhance control strategies at intersections while simultaneously addressing safety, efficiency, and decarbonization objectives. Traditional ATSC methods typically prioritize traffic efficiency and often struggle to adapt to real-time dynamic traffic conditions. To address these challenges, the study suggests a DRL-based ATSC algorithm that incorporates the Dueling Double Deep Q Network (D3QN) framework. The performance of this algorithm is assessed using a simulated intersection in Changsha, China. Notably, the proposed ATSC algorithm surpasses both traditional ATSC and ATSC algorithms focused solely on efficiency optimization by achieving over a 16% reduction in traffic conflicts and a 4% decrease in carbon emissions. Regarding traffic efficiency, waiting time is reduced by 18% compared to traditional ATSC, albeit showing a slight increase (0.64%) compared to the DRL-based ATSC algorithm integrating the D3QN framework. This marginal increase suggests a trade-off between efficiency and other objectives like safety and decarbonization. Additionally, the proposed approach demonstrates superior performance, particularly in scenarios with high traffic demand, across all three objectives. These findings contribute to advancing traffic control systems by offering a practical and effective solution for optimizing signal control strategies in real-world traffic situations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 44 references that are in the map. First posted on arXiv 1 Aug 2024.

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7 of 44 references.

References · 7

  1. 2015Prioritized Experience Replaycited by 1,099
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  3. 2018An Introduction to Deep Reinforcement Learningcited by 206
  4. 2020Deep Reinforcement Learning for Intelligent Transportation Systems: A Surveycited by 98
  5. 2018Deep Reinforcement Learning for Traffic Light Control in Vehicular Networkscited by 52
  6. 2021Boosted Genetic Algorithm using Machine Learning for traffic control optimizationcited by 4
  7. 2022A Spring-Mass-Damper-Based Platooning Logic for Automated Vehiclescited by 1

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  1. 2022Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing
  2. 2025A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning
  3. 2025Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control
  4. 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
  5. 2024Exploring the impact of traffic signal control and connected and automated vehicles on intersections safety: A deep reinforcement learning approach
  6. 2026Adaptive traffic signal control optimization using a novel road partition and multi-channel state representation method
  7. 2025A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control
  8. 2023A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning