Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation

TL;DR

This paper explores reinforcement learning algorithms for partially detected traffic signal control, demonstrating that policy-based methods adapt more efficiently to environmental changes than value-based ones.

Contribution

It compares various RL algorithms for PD-ITSC, highlighting the superior adaptability of policy-based methods in changing environments.

Findings

RL algorithms can optimize traffic signals with limited detection

Policy-based algorithms adapt more efficiently than value-based ones

Insights inform model selection for PD-ITSC systems

Abstract

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.