Green Light Optimal Speed Advisory (GLOSA) system suggests speeds to vehicles to assist them in passing through intersections during green intervals, thus reducing traffic congestion and fuel consumption by minimizing the number of stops and idle times at intersections. However, previous research has focused on optimizing the GLOSA algorithm, neglecting the frequency of speed advisory by the GLOSA system. Specifically, some studies provide speed advisory profile at each decision step, resulting in redundant advisory, while others calculate the optimal speed for the vehicle only once, which cannot adapt to dynamic traffic. In this paper, we propose an Adaptive Frequency GLOSA (AF-GLOSA) model based on Hybrid Proximal Policy Optimization (H-PPO) method, which employs an actor-critic architecture with a hybrid actor network. The hybrid actor network consists of a discrete actor that outputs control gap and a continuous actor that outputs acceleration profiles. Additionally, we design a novel reward function that considers both travel efficiency and fuel consumption. The AF-GLOSA model is evaluated in comparison to traditional GLOSA and learning-based GLOSA methods in a three-lane intersection with a traffic signal in SUMO. The results demonstrate that the AF-GLOSA model performs best in reducing average stop times, fuel consumption and CO2 emissions.

翻译：绿灯最优速度建议（GLOSA）系统通过向车辆建议速度，帮助其在绿灯间隔内通过交叉口，从而通过减少停车次数和怠速时间来降低交通拥堵和燃油消耗。然而，以往的研究主要集中在优化GLOSA算法上，忽视了GLOSA系统速度建议的频率。具体而言，一些研究在每个决策步骤提供速度建议轮廓，导致冗余建议，而另一些研究仅计算一次车辆的最优速度，无法适应动态交通。本文提出了一种基于混合近端策略优化（H-PPO）方法的自适应频率GLOSA（AF-GLOSA）模型，该模型采用具有混合演员网络的演员-评论家架构。混合演员网络由输出控制间隔的离散演员和输出加速度轮廓的连续演员组成。此外，我们设计了一种同时考虑行驶效率和燃油消耗的新型奖励函数。在SUMO中带交通信号的三车道交叉口场景下，将AF-GLOSA模型与传统GLOSA和基于学习的GLOSA方法进行了对比评估。结果表明，AF-GLOSA模型在减少平均停车次数、燃油消耗和CO2排放方面表现最佳。