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.
@article{arxiv.1910.10808,
title = {Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation},
author = {Rusheng Zhang and Romain Leteurtre and Benjamin Striner and Ammar Alanazi and Abdullah Alghafis and Ozan K. Tonguz},
journal= {arXiv preprint arXiv:1910.10808},
year = {2019}
}