Collaborative Control Method of Transit Signal Priority Based on Cooperative Game and Reinforcement Learning
<|reference_start|>Collaborative Control Method of Transit Signal Priority Based on Cooperative Game and Reinforcement Learning: 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.<|reference_end|>
@article{qin2024collaborative,
    title={Collaborative Control Method of Transit Signal Priority Based on
  Cooperative Game and Reinforcement Learning},
    author={Hao Qin, Weishi Zhang},
    journal={arXiv preprint arXiv:2404.19683},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19683},
    primaryClass={cs.GT}
}