TY - JOUR
T1 - High-Efficiency Urban Traffic Management in Context-Aware Computing and 5G Communication
AU - Liu, Jianqi
AU - Wan, Jiafu
AU - Jia, Dongyao
AU - Zeng, Bi
AU - Li, Di
AU - Hsu, Ching Hsien
AU - Chen, Haibo
N1 - Publisher Copyright:
© 1979-2012 IEEE.
PY - 2017/1
Y1 - 2017/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85009968749&partnerID=8YFLogxK
U2 - 10.1109/MCOM.2017.1600371CM
DO - 10.1109/MCOM.2017.1600371CM
M3 - Article
AN - SCOPUS:85009968749
SN - 0163-6804
VL - 55
SP - 34
EP - 40
JO - IEEE Communications Magazine
JF - IEEE Communications Magazine
IS - 1
M1 - 7823335
ER -