Abstract
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 collection 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 infrastructure investments such as roadside units (RSUs) and drones, to ensure that connectivity is available across all intersections in the large network. This represents an investment that may be burdensome for the road agency. As such, this study builds on recent work to present a scalable TSC model that may reduce the number of enabling infrastructure that is required. This is achieved using graph attention networks (GATs) to serve as the neural network for deep reinforcement learning. GAT helps to maintain the graph topology of the traffic network while disregarding any irrelevant 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.
Highlights
Recognizing that until connected and automated vehicle (CAV) are deployed on public roads, CAV-related solutions proposed in the literature cannot be applied in the practice, this paper proposes an intelligent, scalable traffic control model that can be integrated into large, urban networks without using CAVs directly
Deep reinforcement learning (DRL) combines RL with deep learning, which allows for end-to-end training of multilayer models that can solve complex problems
The fog-based graphic RL (FG-RL) model for traffic signal control (TSC) presented in this paper uses a scalable and decentralized methodology
Summary
Urbanization, and automobile ownership, urban transportation networks continue to experience increasing traffic congestion, with severe consequences that include travel delay, driver frustration, increased emissions, and reduced safety. The control of traffic at urban intersections which can help reduce congestion can be classified broadly as: passive and active. Deep reinforcement learning (DRL) combines RL with deep learning, which allows for end-to-end training of multilayer models that can solve complex problems This is useful for sequential decision making such as in robotics, video games, and traffic operations [10,14–18]. Due to the data-driven nature of traffic operations, and fueled by advancements in sensor and communication protocols, RL and DRL have been increasingly utilized to address problems in the transportation engineering domain. These applications include vehicle routing, signal control, vehicle control, and traffic operations.
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