Radio resource management and path planning in intelligent transportation systems via reinforcement learning for environmental sustainability
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
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 language | English |
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| Title of host publication | Innovation and Technological Advances for Sustainability |
| Editors | Salem Al-Naemi, Rachid Benlamri, Michael Phillips, Rehan Sadiq, Aitazaz Farooque |
| Pages | 457-467 |
| Number of pages | 11 |
| Publication status | Published - 13 Nov 2024 |
| Peer-reviewed | Yes |
| Externally published | Yes |
External IDs
| Scopus | 85210867354 |
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Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- MADDPG, AoI, Path planning, Resource management, V2X