Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey (2005.00935v1)

Abstract: 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.

• The paper presents a comprehensive survey of deep RL approaches, emphasizing traffic signal control for intelligent transportation systems.
• It reviews key models like DQN and Actor-Critic, detailing state representation, actions, and reward definitions in complex simulations.
• The study highlights challenges in scaling from simulation to real-world deployment and calls for robust, adaptive RL solutions.

Overview of Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

The paper "Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey" by Ammar Haydari and Yasin Yilmaz provides a detailed examination of the current state of deep reinforcement learning (RL) as applied to intelligent transportation systems (ITS). It systematically categorizes and summarizes various approaches within this burgeoning field, focusing predominantly on traffic signal control (TSC) applications.

In recent years, the merging of AI with ITS has revolutionized transportation management by enabling adaptive and autonomous control systems. This combination aims at optimizing traffic flow and enhancing the safety and efficiency of transportation networks. The authors meticulously review deep RL methodologies developed for managing the complexity of TSC, offering insights into different RL formulations, parameters, and simulation environments.

Key Contributions

1. Comprehensive Survey of RL and Deep RL Applications: The paper is positioned as a significant reference point by presenting the first comprehensive survey of RL-based approaches in ITS, emphasizing TSC applications. It delineates the traditional RL techniques that predate deep RL, underscoring their foundation within the field.
2. Theoretical Background and Model Descriptions: It provides a rich theoretical overview of RL and deep RL, covering models such as Deep Q-Networks (DQN), Actor-Critic methods, and asynchronous methods, defining their operation within control systems. This foundation is crucial for understanding how these models are adapted for ITS applications.
3. Deep RL in Traffic Signal Control: The paper identifies key components crucial to setting up RL for TSC: state representation, action and reward definitions, neural network structures, and simulation settings. It highlights how varied implementations of these components lead to different methodologies for traffic management.
4. Comparison and Categorization: The work categorizes existing research based on their problem formulations and controllers, providing a tabulated comparison to juxtapose the efficacy of various approaches against benchmarks like SOTL and traditional traffic management systems.
5. Challenges and Open Questions: The authors delve into outstanding challenges in transitioning from simulation to real-world deployment. The potential issues related to scalability, system failures, and real-time adaptability of RL-based ITS solutions are critically examined.

Implications and Future Perspectives

From a practical standpoint, the surveyed literature indicates significant enhancements in intersection management through deep RL, highlighting its role in reducing wait times and improving traffic flow. Nonetheless, challenges remain, such as incorporating real-time reliability in dynamically changing environments and moving beyond simulation environments to actual road settings.

A crucial implication of this paper is the call to bridge the gap between theoretical applications of deep RL and their real-world implementations. It encourages researchers to focus on improving the adaptability and resilience of RL algorithms under varying and unpredictable comlex conditions that characterize urban traffic systems.

Future avenues in deep RL for ITS could explore more unified frameworks that integrate various autonomous and control functions. Embracing these challenges will be pivotal in deploying fully autonomous, safe, and efficient intelligent transportation systems. The survey forms a foundation for subsequent work aiming to create robust, flexible, and scalably deployable ITS solutions.

In conclusion, this paper effectively underscores the dynamic interplay of deep RL and ITS, articulating the complex challenges and potential of reinforcement learning in managing intelligent transportation networks. It serves as a robust starting point for new research directions, offering a window into future integrations of autonomous technologies with urban mobility solutions.