Motivation
Existing research on TSC has primarily fallen into two categories: transportation and Reinforcement Learning (RL)-based approaches. Transportation methods primarily focus on crafting efficient heuristic algorithms, dynamically adapting traffic signal configurations based on lane-level traffic conditions. However, these methods heavily rely on manual design, demanding substantial human effort. The emergence of deep neural networks (DNNs) led to the introduction of deep RL-based techniques to address this challenge. These approaches have exhibited remarkable performance across various traffic scenarios. Nevertheless, RL-based methods also present several drawbacks. Primarily, they may struggle with limited generalization ability, particularly when transferring to larger-scale road networks or under highly uncommon scenarios (e.g., extreme high-traffic situations), as their training data only covers limited traffic situations. Additionally, RL-based methods lack interpretability due to the black-box nature of DNNs, which makes it hard to explain the rationale behind their control actions under specific traffic conditions.