Eco-driving at signalized intersections: a parameterized reinforcement learning approach

This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections. The vehicle agent is specified by integrating the model-based car-following policy, lane-changing policy, and RL policy, to ensure the safe operation of a CV. Subsequently, a Markov Decision Process (MDP) is formulated, which enables the vehicle to perform longitudinal control and lateral decisions, jointly optimizing the car-following and lane-changing behaviours of the CVs in the vicinity of intersections. Then, the hybrid action space is parameterized as a hierarchical structure and thereby trains the agents with two-dimensional motion patterns in a dynamic traffic environment. Finally, the authors' proposed methods are evaluated in SUMO software from both a single-vehicle-based perspective and a flow-based perspective. The results show that the authors' strategy can significantly reduce energy consumption by learning proper action schemes without any interruption of other human-driven vehicles (HDVs).

• Record URL:
  https://doi.org/10.1080/21680566.2023.2215957
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/21680566
• Supplemental Notes:
  • © 2023 Hong Kong Society for Transportation Studies Limited. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Jiang, Xia
  • Zhang, Jian
  • Li, Dan
• Publication Date: 2023-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 1406-1431
• Serial:
  • Transportmetrica B: Transport Dynamics
  • Volume: 11
  • Issue Number: 1
  • Publisher: Taylor & Francis
  • ISSN: 2168-0566
  • EISSN: 2168-0582
  • Serial URL: https://www.tandfonline.com/toc/ttrb20/current

Subject/Index Terms

• TRT Terms: Connected vehicles; Ecodriving; Machine learning; Optimization; Signalized intersections; Traffic flow
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01901176
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 1 2023 9:11AM