DocumentCode
2370392
Title
Combined short-term traffic flow forecast model for Beijing Traffic Forecast System
Author
Dong, Shen ; Sun, Linguang ; Chang, Tanghsien ; Lu, Huapu
Author_Institution
Res. Inst. of Civil Aviation Safety, Civil Aviation Univ. of China, Tianjin, China
fYear
2011
fDate
5-7 Oct. 2011
Firstpage
638
Lastpage
643
Abstract
A short-term traffic flow forecasting model is studied for Beijing Traffic Forecast System. From a practical view, a combined forecast model is considered, including Discrete Fourier Transform model, Autoregressive model and Neighborhood Regression model. In order to update weight real-timely, the Bayesian approach is utilized to adjust weights of each sub-model. A large amount of data test is carried out among all sub-models and combined model. It shows advantages of combined model.
Keywords
Bayes methods; autoregressive processes; discrete Fourier transforms; forecasting theory; regression analysis; road traffic; Bayesian approach; Beijing traffic forecast system; autoregressive model; combined model; discrete Fourier transform model; neighborhood regression model; short-term traffic flow forecasting model; Conferences; Intelligent transportation systems; USA Councils;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on
Conference_Location
Washington, DC
ISSN
2153-0009
Print_ISBN
978-1-4577-2198-4
Type
conf
DOI
10.1109/ITSC.2011.6083041
Filename
6083041
Link To Document