Title :
Traffic state identification methods based on cloud computing model
Author :
Liu, Wei-ning ; Ma, Qing-Lu ; Sun, Di-hua ; Dan, Yu-Fang
Author_Institution :
Coll. of Comput. Sci., Chongqing Univ., Chongqing, China
Abstract :
In order to identify the traffic state of urban road network accurately, the traffic state identification methods based on cloud computing model are proposed. The macroscopic and microscopic characteristics of urban road network traffic flow are discussed in detail; and then the identification index systems are proposed. The traffic state identification models are proposed based on cloud computing theory. Numerical results of an arterial road network testified to the effectiveness of the proposed methods. The methods proposed can be applied to traffic state analysis on-line and to extract the traffic information in historical database to provide decision support for traffic management.
Keywords :
Internet; decision support systems; road traffic; arterial road network; cloud computing model; decision support; identification index systems; traffic management; traffic state identification methods; urban road network; Cloud computing; Clouds; Cognition; Computational modeling; Educational institutions; Numerical models; Roads; cloud computing; cloud mode; comprehensive identification; traffic information; traffic state; urban road network;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5553967