Title :
Traffic State Information Extraction Methods Based on Granular Computing
Author :
Ji, Xiaofeng ; Cheng, Wei ; Yang, Jun
Author_Institution :
Fac. of Transp. Eng., Kunming Univ. of Sci. & Technol., Kunming, China
fDate :
Nov. 30 2009-Dec. 1 2009
Abstract :
In order to extract traffic state information and provide decision support for traffic management, granular computing theory was applied in traffic information processing. Traffic information granule and its granularity were defined, and then a methodology that provides a framework of traffic management and decision-making was presented based on GrC. A method was proposed for traffic state information granule construction based on vague sets, and then travel state identification model was proposed based on traffic state information granule similarity. The methods of traffic state information granule construction and their granularity were discussed based on a demonstration network in detail. The results show that the existing traffic information processing methods could be integrated based on GrC, and the proposed methodology can satisfy the demand of traffic management decision-making.
Keywords :
artificial intelligence; information retrieval; traffic information systems; granular computing theory; traffic information processing; traffic management decision making; traffic state information extraction methods; travel state identification model; Data mining; Fuzzy sets; Information analysis; Information processing; Knowledge acquisition; Knowledge engineering; Knowledge management; Telecommunication traffic; Traffic control; Transportation; granular computing; information extraction; information granule; traffic state;
Conference_Titel :
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3888-4
DOI :
10.1109/KAM.2009.308