IEEE Communications Magazine · 2017 · 145 citations · 14 references
ComputingComputer SystemsInformation TechnologyEdge ComputingMobile ComputingTransportationCongestion ManagementIntelligent Transportation SystemsMobile Edge ComputingSmart CitiesUrban TransportVehicle-to-everything CommunicationLocalization In Vehicular NetworksMobile Edge Computing ArchitectureMulti-tier ArchitectureRescueTraffic CongestionTraffic ManagementTraffic PredictionUrban Traffic Congestion
Urban traffic management is increasingly challenged by rising vehicle numbers and congestion. The study proposes a novel four‑tier architecture that integrates VANETs, 5G, SDN, and MEC for urban traffic management. The architecture employs vehicle localization, data pre‑fetching, traffic‑light control, and traffic‑prediction techniques. The architecture improves communication, speeds response, shortens accident‑rescue time, and reduces congestion, enhancing overall traffic efficiency.
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 VANETs, 5G networks, software-defined networks, and mobile edge computing technologies. The proposed architecture provides better communication and more rapid responsive speed in a more distributed and dynamic manner. The practical case of rapid accident rescue can significantly shorten the rescue time. 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 traffic congestion and improving the efficiency of urban traffic management.
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Nick McKeown, Tom Anderson, Hari Balakrishnan et al. · ACM SIGCOMM Computer Communication Review · 2008 · 8.4K citations