Liu, J, Wan, J, Jia, DY et al. (4 more authors) (2017) High-efficiency Urban-traffic Management in Context-aware Computing and 5G Communication. IEEE Communications Magazine, 55 (1). 16598530. pp. 34-40. ISSN 0163-6804
Abstract
With the increasing number of vehicle and traffic jams, urban-traffic management is becoming a serious issue. In this article, we propose novel four-tier architecture for urban-traffic management with the convergence of vehicle ad hoc networks (VANETs), 5G wireless network, software-defined network (SDN), and mobile-edge computing (MEC) technologies. The proposed architecture provides better communication and rapider responsive speed in a more distributed and dynamic manner. The practical case of rapid accident rescue can significantly cut down the time for rescue. Key technologies with respect to vehicle localization, data pre-fetching, traffic lights control, and traffic prediction are also discussed. Obviously, the novel architecture shows noteworthy potential for alleviating the traffic congestion and improving the efficiency of urban-traffic management.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
Keywords: | Servers, Computer architecture, Urban areas, 5G mobile communication, Real-time systems, Sensors, Road traffic, Traffic management, Mobile communication, Edge computing |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number EU - European Union 713788 |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Sep 2016 13:19 |
Last Modified: | 11 Apr 2017 17:12 |
Published Version: | https://doi.org/10.1109/MCOM.2017.1600371CM |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/MCOM.2017.1600371CM |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:104579 |