mlsanityData to 14 Oct 2026

Collaborative Control Method of Transit Signal Priority Based on Cooperative Game and Reinforcement Learning

arXiv 2404.19683 ↗First posted 30 Apr 2024cs.GTcited by 0

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

Abstract (arXiv)

To address the low efficiency in priority signal control within intelligent transportation systems, this study introduces a novel eight-phase priority signal control method, CBQL-TSP, leveraging a hybrid decision-making framework that integrates cooperative game theory and reinforcement learning. This approach conceptualizes the allocation of bus signal priorities as a multi-objective decision-making problem across an eight-phase signal sequence, differentiating between priority and non-priority phases. It employs a cooperative game model to facilitate this differentiation. The developed hybrid decision-making algorithm, CBQL, effectively tackles the multi-objective decision-making challenges inherent in the eight-phase signal sequence. By computing the Shapley value function, it quantifies the marginal contributions of each participant, which in turn inform the construction of a state transition probability equation based on Shapley value ratios. Compared to conventional control methods, the CBQL-TSP method not only upholds the fairness principles of cooperative game theory but also harnesses the adaptive learning capabilities of Q-Learning. This enables dynamic adjustments to signal timing in response to real-time traffic conditions, significantly enhancing the flexibility and efficiency of priority signal control.

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 2 of its 34 references that are in the map. First posted on arXiv 30 Apr 2024.

Builds on

2 of 34 references.

References · 2

  1. 2023Game Theoretic Application to Intersection Management: A Literature Reviewcited by 4
  2. 2018Effect of Transit Signal Priority on Bus Service Reliabilitycited by 1

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  1. 2024Integrating Transit Signal Priority into Multi-Agent Reinforcement Learning based Traffic Signal Control
  2. 2024Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment
  3. 2026Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority
  4. 2025Convergence of Multiagent Learning Systems for Traffic control
  5. 2025Reinforcement Learning Based Traffic Signal Design to Minimize Queue Lengths
  6. 2024Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control
  7. 2025Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework
  8. 2025Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control