Title :
Applying Data Mining in Prediction and Classification of Urban Traffic
Author :
Nejad, Sedigheh Khajouei ; Seifi, Farid ; Ahmadi, Hamed ; Seifi, Nima
Author_Institution :
Dept. of Comput., Islamic Azad Univ., Sirjan, Iran
fDate :
March 31 2009-April 2 2009
Abstract :
Data mining is a branch of computer science which recently has a great use for enterprises. Applying data mining methods, huge databases have been analyzed and processed. Data mining techniques are usually hired to mine knowledge and models from enormous data sets for prediction of new events. Furthermore these techniques are commonly used in fields which generate great amount of data that can not be processed by ordinary methods. During the last decade traffic management became a new field of science which produced unlimited data, and this amount of data needed new methods to be processed. It is clear that one of the most important fields in traffic management is Traffic prediction. As a result data mining methods were chosen to generate dependable patterns. In this paper we applied Classification methods to learn traffic behavior and prediction of new events.
Keywords :
data mining; traffic engineering computing; computer science; data mining; traffic management; urban traffic classification; urban traffic prediction; Computer science; Data analysis; Data engineering; Data mining; Knowledge engineering; Sampling methods; Telecommunication traffic; Testing; Traffic control; Wireless sensor networks; Classification; Data Mining; Decision Tree; Prediction; Urban Traffic;
Conference_Titel :
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-0-7695-3507-4
DOI :
10.1109/CSIE.2009.906