To alleviate traffic congestion at road intersections, this work proposes a co operative control method integrating traffic l ight and p latooning (CoLP). It explicitly considers platoon-related traffic information and employs the proximal policy optimization (PPO) algorithm to optimize traffic signal phases dynamically. Simultaneously, it guides platoons to flexibly adjust their speed and structure based on signal status, thereby enhancing intersection throughput and reducing delay. The effectiveness of CoLP is validated using simulation of urban mobility (SUMO), one of the most widely used open-source microscopic traffic simulators. Experimental results demonstrate that compared with methods that optimize traffic signal or connected autonomous vehicles (CAVs) control independently, CoLP achieves superior traffic flow optimization. Furthermore, compared with methods that perform cooperative control of traffic signals and individual CAVs without further considering the cooperation between signal and platoons, CoLP demonstrates better performance in enhancing intersection throughput, reducing delay, and effectively lowering energy consumption.
Dong et al. (Mon,) studied this question.