Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

An intelligent multi-speed advisory system using improved whale optimisation algorithm

Chen, Beiran, Liu, Mingming orcid logoORCID: 0000-0002-8988-2104, Zhang, Yi, Chen, Zhengyong, Gu, Yingqi orcid logoORCID: 0000-0001-5807-6102 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2021) An intelligent multi-speed advisory system using improved whale optimisation algorithm. In: 2021 IEEE 93rd Vehicular Technology Conference - Spring, 25 - 28 Apr 2021, Helsinki, Finland (Online).

Abstract
An intelligent speed advisory system can be used to recommend speed for vehicles travelling in a given road network in cities. In this paper, we extend our previous work where a distributed speed advisory system has been devised to recommend an optimal consensus speed for a fleet of Internal Combustion Engine Vehicles (ICEVs) in a highway scenario. In particular, we propose a novel optimisation framework where the exact format of each vehicle’s cost function can be implicit, and our algorithm can be used to recommend multiple consensus speeds for vehicles travelling on different lanes in an urban highway scenario. Our studies show that the proposed scheme based on an improved whale optimisation algorithm can effectively reduce CO2 emission generated from ICEVs while providing different recommended speed options for groups of vehicles.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Speed advisory systems; Distributed algorithms; Whale Optimisation
Subjects:Computer Science > Algorithms
Engineering > Systems engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Published in: 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). . IEEE.
Publisher:IEEE
Official URL:https://doi.org/10.1109/VTC2021-Spring51267.2021.9...
Copyright Information:© 2021 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland Grant SFI/12/RC/2289 P2
ID Code:25741
Deposited On:23 Apr 2021 13:45 by Mingming Liu . Last Modified 04 Nov 2021 14:20
Documents

Full text available as:

[thumbnail of VTC2021_Final.pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
317kB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record