DocumentCode :
3327274
Title :
Railway Passenger Volume Forecasting Based on Support Vector Machine and Genetic Algorithm
Author :
Chen, Xiaogang
Author_Institution :
Digital Manuf. Technol. Lab., Huaiyin Inst. of Technol., Huaian, China
fYear :
2009
fDate :
6-7 June 2009
Firstpage :
282
Lastpage :
284
Abstract :
A new prediction approach for the railway passenger volume is put forward by means of support vector machine optimized by genetic algorithm (GA-SVM). In GA-SVM model, GA is used to determine training parameters of support vector machine. GA has strong global search capability, which can get optimal solution in short time. Railway passenger volume of China from 1985-2002 is used to illustrate the performance of the proposed GA-SVM model. The experimental results indicate that the GA-SVM method can achieve greater forecasting accuracy than artificial neural network in railway passenger volume forecasting.
Keywords :
forecasting theory; genetic algorithms; rail traffic; support vector machines; traffic engineering computing; genetic algorithm; railway passenger volume forecasting; support vector machine; Artificial neural networks; Computer aided manufacturing; Fault tolerance; Genetic algorithms; Learning systems; Parallel processing; Rail transportation; Statistical learning; Support vector machines; Technology forecasting; forecasting method; genetic algorithm; railway passenger volume; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Future Computer and Communication, 2009. FCC '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3676-7
Type :
conf
DOI :
10.1109/FCC.2009.81
Filename :
5235649
Link To Document :
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