The intersection management system can increase traffic capacity, vehicle safety, and the smoothness of all vehicle movement. Platoons of connected vehicles (CVs) use communication technologies to share information with each other and with infrastructures. In this paper, we proposed a deep reinforcement learning (DRL) model that applies to vehicle platooning at an isolated signalized intersection with partial detection. Moreover, we identified hyperparameters and tested the system with different numbers of vehicles (1, 2, and 3) in the platoon. To compare the effectiveness of the proposed model, we implemented two benchmark options, actuated traffic signal control (ATSC) and max pressure (MP). The experimental results demonstrated that the DRL model has many outstanding advantages compared to other models. Through the learning process, the average waiting time of vehicles in the DRL method was improved by 20% and 28% compared with the ATSC and MP options. The results also suggested that the DRL model is effective when the CV penetration rate is over 20%.

In the future, mixed traffic flow will include two types of vehicles: connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs). CAVs emerge as new solutions to disrupt the traditional transportation system. This new solution shares real-time data with each other and the roadside units (RSU) for network management. Reinforcement learning (RL) is a promising approach for traffic signal management in complex urban areas by leveraging information gathered from CAVs. In particular, coordinating signal management at many intersections is a critical challenge in multi-agent reinforcement learning (MARL). According to this vision, we propose an approach that combines an actor–critic network–based multi-agent deep deterministic policy gradient (MADDPG) model and a rerouting technique (RT) to increase traffic performance in vehicular networks. This algorithm overcomes the inherent non-stationary of Q-learning and the high variance of policy gradient (PG) algorithms. Based on centralized learning with decentralized execution, the MADDPG model employs one actor and one critic for each agent. The actor network uses local information to execute actions, while the critic network is trained with extra information, including the states and actions of other agents. Through a centralized learning process, agents can coordinate with each other, diminishing the influence of an unstable environment. Unlike previous studies, we not only manage traffic light systems but also consider the effect of platooning vehicles on increasing throughput. Experimental results show that our model outperforms other models in terms of traffic performance in different scenarios.

Connected and automated vehicles are expected to play a key role in alleviating urban traffic congestion in transportation systems. However, coordinating traffic lights and connected and automated vehicles remains a challenging problem in mixed-autonomy environments. This paper proposes a modified proximal policy optimization–based decentralized multi-agent reinforcement learning framework to jointly optimize traffic signal control and eco-driving behavior. The model includes two types of agents, namely traffic light agents and connected and automated vehicle agents. Traffic light agents aim to improve traffic efficiency by optimizing signal-phase decisions. Connected and automated vehicle agents focus on adjusting vehicle acceleration and speed to enhance driving comfort and smoothness. Separate policy networks are designed for each agent type to capture their different control objectives. The proposed framework is evaluated on a realistic urban traffic network. Simulation results show that the decentralized model (multi-agent-proximal policy optimization) achieves the best overall performance. It significantly improves average speed compared to baseline methods. It also reduces average travel time and vehicle waiting time. Further analysis shows that increasing connected and automated vehicle penetration improves overall traffic efficiency. The best performance is achieved when all vehicles are connected and automated vehicles, indicating full system observability and coordination.