Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control

Appropriate traffic state representation is crucial for learning traffic signal control policies. However, most of the current traffic state representations are heuristically designed, with insufficient theoretical support. In this paper, we (1) develop a flexible, efficient, and theoretically grounded method, namely generalized phase pressure (G2P) control, which takes only simple lane features into consideration to decide which phase to be actuated; 2) extend the pressure control theory to a general form for multi-homogeneous-lane road networks based on queueing theory; (3) design a new traffic state representation based on the generalized phase state features from G2P control; and 4) develop a reinforcement learning (RL)-based algorithm template named G2P-XLight, and two RL algorithms, G2P-MPLight and G2P-CoLight, by combining the generalized phase state representation with MPLight and CoLight, two well-performed RL methods for learning traffic signal control policies. Extensive experiments conducted on multiple real-world datasets demonstrate that G2P control outperforms the state-of-the-art (SOTA) heuristic method in the transportation field and other recent human-designed heuristic methods; and that the newly proposed G2P-XLight significantly outperforms SOTA learning-based approaches. Our code is available online.

Emergent Crowd Grouping via Heuristic Self-Organization

Modeling crowds has many important applications in games and computer animation. Inspired by the emergent following effect in real-life crowd scenarios, in this work, we develop a method for implicitly grouping moving agents. We achieve this by analyzing local information around each agent and rotating its preferred velocity accordingly. Each agent could automatically form an implicit group with its neighboring agents that have similar directions. In contrast to an explicit group, there are no strict boundaries for an implicit group. If an agent's direction deviates from its group as a result of positional changes, it will autonomously exit the group or join another implicitly formed neighboring group. This implicit grouping is autonomously emergent among agents rather than deliberately controlled by the algorithm. The proposed method is compared with many crowd simulation models, and the experimental results indicate that our approach achieves the lowest congestion levels in some classic scenarios. In addition, we demonstrate that adjusting the preferred velocity of agents can actually reduce the dissimilarity between their actual velocity and the original preferred velocity. Our work is available online.