Radio resource management and path planning in intelligent transportation systems via reinforcement learning for environmental sustainability

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

  • Sajede Norouzi - , Tarbiat Modares University (Author)
  • Negar Azarasa - , Tarbiat Modares University (Author)
  • Mohammad Abedi - , Tarbiat Modares University (Author)
  • Nader Mokari - , Tarbiat Modares University (Author)
  • Seyedehsan Seyedabrishami - , Tarbiat Modares University (Author)
  • Hamid Saeedi - , University of Doha for Science and Technology , Tarbiat Modares University (Author)
  • E.A. Jorswieck - , Technical University of Braunschweig (Author)

Abstract

Efficient and dynamic path planning has become an important topic for urban areas with larger density of connected vehicles (CV) which results in reduction of travel time and directly contributes to environmental sustainability through reducing energy consumption. CVs exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the vehicle-to-infrastructure (V2I) messages to the Base-station (BS) to improve situation awareness on urban roads. In this paper, we investigate radio resource management (RRM) in such a framework to minimize the age of information (AoI) so as to enhance path planning results. We use the fact that V2I messages with lower AoI value result in less error in estimating the road capacity and more accurate path planning. Through simulations, we compare road travel times and volume over capacity (V/C) against different levels of AoI and demonstrate the promising performance of the proposed framework.

Details

Original languageEnglish
Title of host publicationInnovation and Technological Advances for Sustainability
EditorsSalem Al-Naemi, Rachid Benlamri, Michael Phillips, Rehan Sadiq, Aitazaz Farooque
Pages457-467
Number of pages11
Publication statusPublished - 13 Nov 2024
Peer-reviewedYes
Externally publishedYes

External IDs

Scopus 85210867354

Keywords

Sustainable Development Goals

Keywords

  • MADDPG, AoI, Path planning, Resource management, V2X