DocumentCode
553983
Title
Notice of Retraction
Short-term traffic volumes forecasting of road network based on nonparametric regression
Author
Xing-yi Li ; Yu-ba Jiang ; Hua-ji Shi
Author_Institution
Sch. of Comput. Sci. & Telecommun. Eng., Jiangsu Univ., Zhenjiang, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
228
Lastpage
231
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Taking traffic volumes of multi -section as the research object, expanding the traffic volumes forecasting of single-spot to multi-section, proposing a new forecasting method of multi-section based on SOM network. The SOM can capture the nonlinear relationship among each section of a road network, which outperforms the traditional linear mechanism, the SOM can solve the weaknesses of slow search of traditional nonparametric regression because of effect of clustering and dimensionality- reduction as well. Finally, using the data of a road network to validate the effectiveness of proposed forecasting models, the results show that: the proposed forecasting model is effective and reliable.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Taking traffic volumes of multi -section as the research object, expanding the traffic volumes forecasting of single-spot to multi-section, proposing a new forecasting method of multi-section based on SOM network. The SOM can capture the nonlinear relationship among each section of a road network, which outperforms the traditional linear mechanism, the SOM can solve the weaknesses of slow search of traditional nonparametric regression because of effect of clustering and dimensionality- reduction as well. Finally, using the data of a road network to validate the effectiveness of proposed forecasting models, the results show that: the proposed forecasting model is effective and reliable.
Keywords
forecasting theory; nonparametric statistics; regression analysis; road traffic; SOM network; clustering effect; dimensionality reduction; multisection traffic volume; nonparametric regression; road network; short-term traffic volume forecasting; Databases; Forecasting; Prediction algorithms; Predictive models; Roads; Time series analysis; SOM; nonparametric regression; short-term forecasting; traffic flow;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022077
Filename
6022077
Link To Document