Hua Cheng, Rana Jafari, April Russell, Russell Klopfer, Edmond Lu, Benjamin Striner, Matthew Gormley. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Intelligent Traffic Signal Control (ITSC) systems have attracted the attention of researchers and the general public alike as a means of alleviating traffic congestion. Recently, the vehicular wireless technologies have enabled a cost-efficient way to achieve ITSC by detecting vehicles using Vehicle to Infrastructure (V2I) wireless communications. Traditional ITSC algorithms, in most cases, assume that every vehicle is detected, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles equipped with wireless communications capability. We examine a family of transportation systems, which we will refer to as `Partially Detected Intelligent Transportation Systems'. An algorithm that can perform well under a small detection rate is highly desirable due to gradual increasing penetration rates of the underlying technologies such as Dedicated Short Range Communications (DSRC) technology. Reinforcement Learning (RL) approach in Artificial Intelligence (AI) could provide indispensable tools for such problems where only a small portion of vehicles are detected by the ITSC system. In this paper, we report a new RL algorithm for Partially Detected Intelligent Traffic Signal Control (PD-ITSC) systems. The performance of this system is studied under different car flows, detection rates, and types of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate, thus reducing the travel time of vehicles.

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.