Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation
This work addresses traffic congestion mitigation for urban planners, but it is incremental as it builds on existing reinforcement learning methods.
The paper tackled the problem of adapting partially detected intelligent traffic signal control systems to changing environments, finding that policy-based reinforcement learning algorithms adapt more efficiently than value-based ones.
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-Critic with 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.