mlsanityData to 7 Oct 2026

SIGMA: Symmetry-aware, Intelligent, Geometric, Multi-objective Adaptive Control for Robust, Dependable Traffic Management

arXiv 2608.18263 ↗First posted 18 Aug 2026cs.LG stat.MLcited by 0

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Abstract (arXiv)

Traffic signal control is a complex sequential decision-making problem requiring real-time adaptation and trade-offs among throughput, delay fairness, signal stability, and emergency vehicle priority. Existing RL methods often fix objectives, ignore dynamic priority changes, and fail to generalize across geometrically similar intersections.We propose SIGMA (Symmetry-aware, Intelligent, Geometric, Multi-objective Adaptive traffic control), an RL framework enhanced with a large language model (LLM) for adaptive objective tuning and orientation-invariant learning. SIGMA converts natural-language emergency commands into priority vectors for a multi-objective actor-critic controller, avoiding manual reward engineering. Rotational augmentation improves transferability across four-way intersections, while offline-to-online learning ensures stable initialization and gradual adaptation to changing traffic.We define reliability properties covering emergency service levels, graceful degradation under LLM failures, and demand sensitivity, validated via bootstrap statistics. Evaluated in SUMO on four Kolkata-based urban intersections against fixed-time, actuated, and DQN controllers, SIGMA reduces average/emergency waiting times and queue lengths, and boosts throughput. Ablation studies confirm robustness to component failures and geometric rotations. Overall, SIGMA offers a reliable, language-guided, multi-objective traffic control system with statistical reliability assurance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 58 references that are in the map. First posted on arXiv 18 Aug 2026.

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6 of 58 references.

References · 6

  1. 2019CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenariocited by 114
  2. 2019Learning Phase Competition for Traffic Signal Controlcited by 84
  3. 2023LLMLight: Large Language Models as Traffic Signal Control Agentscited by 73
  4. 2017Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learningcited by 33
  5. 2019Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learningcited by 33
  6. 2017Implementing the Deep Q-Networkcited by 10

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  1. 2025Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems
  2. 2025Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
  3. 2025Evolutionary Discovery of Heuristic Policies for Traffic Signal Control
  4. 2025EvolveSignal: A Large Language Model Powered Coding Agent for Discovering Traffic Signal Control Strategies
  5. 2023A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
  6. 2026Safe Urban Traffic Control via Uncertainty-Aware Conformal Prediction and World-Model Reinforcement Learning
  7. 2025VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture
  8. 2025A Dual Large Language Models Architecture with Herald Guided Prompts for Parallel Fine Grained Traffic Signal Control