The control of traffic signals is crucial for improving transportation
efficiency. Recently, learning-based methods, especially Deep Reinforcement
Learning (DRL), garnered substantial success in the quest for more efficient
traffic signal control strategies. However, the design of rewards in DRL highly
demands domain knowledge to converge to an effective policy, and the final
policy also presents difficulties in terms of explainability. In this work, a
new learning-based method for signal control in complex intersections is
proposed. In our approach, we design a concept of phase urgency for each signal
phase. During signal transitions, the traffic light control strategy selects
the next phase to be activated based on the phase urgency. We then proposed to
represent the urgency function as an explainable tree structure. The urgency
function can calculate the phase urgency for a specific phase based on the
current road conditions. Genetic programming is adopted to perform
gradient-free optimization of the urgency function. We test our algorithm on
multiple public traffic signal control datasets. The experimental results
indicate that the tree-shaped urgency function evolved by genetic programming
outperforms the baselines, including a state-of-the-art method in the
transportation field and a well-known DRL-based method.

该研究提出了一种基于学习的方法，用于复杂交叉口的信号控制，通过设计一种可解释的树状紧急程度函数，并利用遗传编程进行优化，相比交通领域中的先进方法和一种已知的深度强化学习方法，实验结果表明这种方法能够提高交通信号控制的性能。