DocumentCode :
507668
Title :
Application Research of Road Passenger Transport Volume Forecasting Using Regression Methods
Author :
Yang, Ming ; Chen, Yan ; Li, Taoying
Author_Institution :
Coll. of Transp. & Manage., Dalian Maritime Univ., Dalian, China
Volume :
3
fYear :
2009
fDate :
Nov. 30 2009-Dec. 1 2009
Firstpage :
277
Lastpage :
280
Abstract :
Passenger Transport Volume forecasting is a hot topic in the field of transportation and is meaningful to improve the traffic situation, can also help common people on installment plan. Different linear regression models, including one variable linear regression and multivariate linear regression, and nonlinear regression models, including power function and exponential function, were introduced and their testing methods were given in this paper. Then all of those models were applied to predict road passenger transport volume and passenger person-kilometers of Yunnan Province. According to results of those regression models, average relative error method was used to compare different regression models, and then we know their precisions are different, all of them satisfy the demand of forecasting and achieve the aim of forecasting.
Keywords :
forecasting theory; regression analysis; road traffic; transportation; Yunnan Province; average relative error method; common people; exponential function; multivariate linear regression models; nonlinear regression models; passenger person-kilometers; power function; road passenger transport volume forecasting; traffic situation; transportation; Artificial neural networks; Demand forecasting; Fuzzy neural networks; Linear regression; Predictive models; Regression analysis; Road transportation; Telecommunication traffic; Testing; Traffic control; hypothesis testing; nonlinear regression; passenger transport volume forecasting; regression analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3888-4
Type :
conf
DOI :
10.1109/KAM.2009.73
Filename :
5362337
Link To Document :
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