Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation

Partially Detected Intelligent Traffic Signal Control(PD-ITSC) systems that can optimize traffic signals based on limited detected information could be a cost-efficient solution for mitigating traffic congestion in the future. In this paper, we focus on a particular problem in PD-ITSC - adaptation to changing environments. To this end, we investigate different reinforcement learning algorithms, including Q-Learning, Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Actor-Criticwith Kronecker-Factored Trust-Region (ACKTR). Our findings suggest that RL algorithms can find optimal strategies under partial vehicle detection; however, policy-based algorithms can adapt to changing environments more efficiently than value-based algorithms. We use these findings to draw conclusions about the value of different models for PD-ITSC systems.