The invention provides an intelligent adaptive traffic signal control method combining reinforcement learning and model prediction control, and belongs to the technical field of intelligent traffic. In the aspect of boundary signal lamp control, a Q-Learning algorithm is utilized, and an MFD combination selection strategy is continuously optimized through online learning, so that the system can dynamically adapt to different traffic flow modes and change conditions. The Q-Learning algorithm is combined with an epsilon-greedy selection strategy, the control effect is gradually improved through a reward mechanism, and the timing efficiency of the boundary signal lamp is effectively improved. In the aspect of signal optimization of an internal area, a Cyclic MaxPresure algorithm is adopted, signal lamp timing is dynamically adjusted according to real-time traffic flow distribution, the randomness and complexity of traffic flow can be quickly responded, and therefore the congestion condition of a local intersection is effectively relieved.
本发明提出了一种将强化学习与模型预测控制相结合的智能化自适应交通信号控制方法,属于智能交通技术领域。在边界信号灯控制方面,本发明利用Q‑Learning算法,通过在线学习不断优化MFD组合选择策略,从而使系统能够动态适应不同的交通流量模式和变化情况。Q‑Learning算法结合了ε‑贪心选择策略,通过奖励机制逐步改善控制效果,有效提高了边界信号灯的配时效率。在内部区域的信号优化方面,本发明采用Cyclic MaxPressure算法,根据实时的交通流分布动态调整信号灯配时,使其能够快速响应交通流的随机性和复杂性,从而有效缓解了局部路口的拥堵情况。
Traffic signal control method and system combining reinforcement learning and model prediction control
结合强化学习和模型预测控制的交通信号控制方法及系统
25.04.2025
Patent
Elektronische Ressource
Chinesisch
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