mlsanityData to 14 Oct 2026

Adaptive traffic signal control optimization using a novel road partition and multi-channel state representation method

arXiv 2602.12296 ↗First posted 1 Feb 2026eess.SY cs.AI cs.SYcited by 4

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Abstract (arXiv)

This study proposes a novel adaptive traffic signal control method leveraging a Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to optimize signal timing by integrating variable cell length and multi-channel state representation. A road partition formula consisting of the sum of logarithmic and linear functions was proposed. The state variables are a vector composed of three channels: the number of vehicles, the average speed, and space occupancy. The set of available signal phases constitutes the action space, the selected phase is executed with a fixed green time. The reward function is formulated using the absolute values of key traffic state metrics - waiting time, speed, and fuel consumption. Each metric is normalized by a typical maximum value and assigned a weight that reflects its priority and optimization direction. The simulation results, using Sumo-TensorFlow-Python, demonstrate a cross-range transferability evaluation and show that the proposed variable cell length and multi-channel state representation method excels compared to fixed cell length in optimization performance.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 2 of its 19 references that are in the map. First posted on arXiv 1 Feb 2026.

Builds on

2 of 19 references.

References · 2

  1. 2016Using a Deep Reinforcement Learning Agent for Traffic Signal Controlcited by 26
  2. 2019A Reinforcement Learning Approach for Intelligent Traffic Signal Control at Urban Intersectionscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Task-Specific Efficiency Analysis: When Small Language Models Outperform Large Language Modelscited by 15
  2. 2026Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-offcited by 13
  3. 2026EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Predictioncited by 2
  4. 2026MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignmentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning Based Traffic Signal Design to Minimize Queue Lengths
  2. 2024Optimizing Traffic Signal Control using High-Dimensional State Representation and Efficient Deep Reinforcement Learning
  3. 2024Traffic Signal Control and Speed Offset Coordination Using Q-Learning for Arterial Road Networks ↗
  4. 2019Tensor-based Cooperative Control for Large Scale Multi-intersection Traffic Signal Using Deep Reinforcement Learning and Imitation Learning ↗
  5. 2025Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control
  6. 2025A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control
  7. 2024Learning Traffic Signal Control via Genetic Programming
  8. 2024Adaptive traffic signal safety and efficiency improvement by multi objective deep reinforcement learning approach