  Optimizing traffic signal control (TSC) at intersections continues to pose a
challenging problem, particularly for large-scale traffic networks. It has been
shown in past research that it is feasible to optimize the operations of
individual TSC systems or a small number of such systems. However, it has been
computationally difficult to scale these solution approaches to large networks
partly due to the curse of dimensionality that is encountered as the number of
intersections increases. Fortunately, recent studies have recognized the
potential of exploiting advancements in deep and reinforcement learning to
address this problem, and some preliminary successes have been achieved in this
regard. However, facilitating such intelligent solution approaches may require
large amounts of infrastructural investments such as roadside units (RSUs) and
drones in order to ensure thorough connectivity across all intersections in
large networks, an investment that may be burdensome for agencies to undertake.
As such, this study builds on recent work to present a scalable TSC model that
may reduce the number of required enabling infrastructure. This is achieved
using graph attention networks (GATs) to serve as the neural network for deep
reinforcement learning, which aids in maintaining the graph topology of the
traffic network while disregarding any irrelevant or unnecessary information. A
case study is carried out to demonstrate the effectiveness of the proposed
model, and the results show much promise. The overall research outcome suggests
that by decomposing large networks using fog-nodes, the proposed fog-based
graphic RL (FG-RL) model can be easily applied to scale into larger traffic
networks.
