Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail. Different problem formulations, RL parameters, and simulation environments for TSC are discussed comprehensively. In the literature, there are also several autonomous driving applications studied with deep RL models. Our survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, we discuss the challenges and open questions regarding deep RL-based transportation applications.

I. Introduction

With increasing urbanization and latest advances in autonomous technologies, transportation studies evolved to more intelligent systems, called intelligent transportation systems (ITS). Artificial intelligence (AI) tries to control systems with minimal human intervention. Combination of ITS and AI provides effective solutions for the 21st century transportation studies. The main goal of ITS is providing safe, effective and reliable transportation systems to participants. For this purpose, optimal traffic signal control (TSC), autonomous vehicle control, traffic flow control are some of the key research areas.