An Improved Multi-agent based Data Driven Distributed Adaptive Cooperative Control in Traffic Network Signal Timing

For the traffic network signal control scenario, this paper proposes an improve multi-agent data-driven distributed adaptive coordination control algorithm (I-MA-DACC), which outputs the distributed adaptive green time at each intersection for the purpose of dynamic queue balancing. The queuing length obtained by the front-end information collection devices and the green time of the current cycle are the system input and output data, respectively. Meanwhile, queue balancing control of multi-directional traffic flow on signal-controlled traffic networks is considered in the framework of multi-agent system. As a result, the proposed I-MA-DACC is an improved multi-agent based data-driven control strategy. Firstly, the parameter and control learning law are deployed for each intersection enhancing the scalability and adaptive ability. Secondly, the algorithm can be applied to different congestion scenarios in large-scaled traffic network. Finally, it is verified by the Sumo-Python simulation platform with open-source superiority. Compared with other distributed adaptive cooperative control methods, the simulation results show the advantage of I-MA-DACC in reducing of the queuing delays, waiting count and time loss.