  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.
