Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation

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.

arXiv commentAccepted by ICMLA 2019

References in corpus (5)

• Trust Region Policy Optimization
• Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
• Using a Deep Reinforcement Learning Agent for Traffic Signal Control
• Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control
• Using Ultra-Wideband Technology in Vehicles for Infrastructure-free Localization

Cited by in corpus (2)

• Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
• Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control