基于合作博弈与强化学习的公交信号优先协同控制方法
Collaborative Control Method of Transit Signal Priority Based on Cooperative Game and Reinforcement Learning
AI总结：
本文提出CBQL-TSP方法，将合作博弈与Q-Learning结合用于八相位公交信号优先控制，通过Shapley值量化各相位贡献并动态调整配时，在保证公平性的同时提升优先信号控制的灵活性与效率。
摘要：
针对智能交通系统中优先信号控制效率低下的问题，本研究提出了一种新颖的八相位优先信号控制方法CBQL-TSP，该方法利用融合合作博弈理论与强化学习的混合决策框架。该方法将公交信号优先权的分配概念化为八相位信号序列上的多目标决策问题，并区分优先相位与非优先相位，采用合作博弈模型来实现这一区分。所开发的混合决策算法CBQL能够有效应对八相位信号序列中固有的多目标决策挑战。通过计算Shapley值函数，该算法量化了每个参与者的边际贡献，进而基于Shapley值比率构建状态转移概率方程。与传统控制方法相比，CBQL-TSP方法不仅维护了合作博弈理论的公平性原则，还利用了Q-Learning的自适应学习能力，能够根据实时交通状况动态调整信号配时，显著提高了优先信号控制的灵活性和效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
