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.

本研究提出了一种基于多智能体协作的在线规划方法，旨在实现适应性、实时和网络级交通信号控制（TSC），并证明这种方法能够有效提高交通流量的控制和性能。