Efficient traffic signal control (TSC) has been one of the most useful ways for reducing urban road congestion. Key to the challenge of TSC includes 1) the essential of real-time signal decision, 2) the complexity in traffic dynamics, and 3) the network-level coordination. Recent efforts that applied reinforcement learning (RL) methods can query policies by mapping the traffic state to the signal decision in real-time, however, is inadequate for unexpected traffic flows. By observing real traffic information, online planning methods can compute the signal decisions in a responsive manner. We propose an explicit multiagent coordination (EMC)-based online planning methods that can satisfy adaptive, real-time and network-level TSC. By multiagent, we model each intersection as an autonomous agent, and the coordination efficiency is modeled by a cost (i.e., congestion index) function between neighbor intersections. By network-level coordination, each agent exchanges messages with respect to cost function with its neighbors in a fully decentralized manner. By real-time, the message passing procedure can interrupt at any time when the real time limit is reached and agents select the optimal signal decisions according to the current message. Moreover, we prove our EMC method can guarantee network stability by borrowing ideas from transportation domain. Finally, we test our EMC method in both synthetic and real road network datasets. Experimental results are encouraging: compared to RL and conventional transportation baselines, our EMC method performs reasonably well in terms of adapting to real-time traffic dynamics, minimizing vehicle travel time and scalability to city-scale road networks.

翻译：高效的交通信号控制（TSC）是缓解城市道路拥堵最有效的方法之一。TSC面临的挑战包括：1）信号决策的实时性要求，2）交通动态的复杂性，以及3）网络级协调。近年来，应用强化学习（RL）的方法通过将交通状态映射到信号决策来实现实时策略查询，然而对于意外交通流表现出不足。通过观测实时交通信息，在线规划方法能够以响应式方式计算信号决策。我们提出一种基于显式多智能体协调（EMC）的在线规划方法，可满足自适应、实时和网络级TSC需求。在多智能体层面，我们将每个交叉口建模为自主智能体，并通过相邻交叉口间的成本函数（即拥堵指数）建模协调效率。在网络级协调方面，每个智能体以完全去中心化方式与邻居交换成本函数相关信息。在实时性方面，消息传递过程可在达到实时时限时随时中断，智能体根据当前消息选择最优信号决策。此外，我们借鉴交通领域思想证明了EMC方法能保证网络稳定性。最后，我们在合成路网和真实路网数据集上测试了EMC方法。实验结果表明：与RL及传统交通基线方法相比，我们的EMC方法在适应实时交通动态、最小化车辆行驶时间以及可扩展至城市规模路网方面均表现优异。