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. The authors survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, they discuss the challenges and open questions regarding deep RL-based transportation applications.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2020.3008612
• Record URL:
  • https://ieeexplore.ieee.org/document/9146378
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • © 2021, IEEE.
• Authors:
  • Haydari, Ammar
  • Yilmaz, Yasin
• Publication Date: 2022-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 11-32
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 1
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Intelligent transportation systems; Machine learning; Markov processes; Traffic signal control systems
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01840517
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 28 2022 10:29AM