DocumentCode
2826932
Title
Notice of Retraction
Combined method for travel time prediction based on wavelet denoising
Author
Jinqiao Feng ; Sun Zhanquan ; Liu Wei
Author_Institution
Shandong Comput. Sci. Center, High Performance Comput. Lab., Jinan, China
Volume
3
fYear
2010
fDate
22-24 Oct. 2010
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.
The realization of the Intelligent Transportation Systems will effectively solve the problem of traffic congestion and urban traffic pollution, improve the road capacity and traffic safety. A crucial key of the realization of the ITS is the estimate and prediction of travel time: how to make and continuously update prediction of travel time for several minutes into the future using real-time data. In order to analyze the trend of the travel time accurately, combined with superiority of wavelet in dealing with time-varying information, a combined method for travel time prediction based on wavelet denoising and exponential smoothing and metabolic curve fitting model is presented in this paper. The new method has general adaptability because of the methods we selected and the combination strategy we proposed and the use of metabolic treatment in the model. We contrast the new model with models we choose at the end of the paper. And then make a clear analysis and compare on the models. The conclusion shows that the new model is applicable for the real condition and has a better result.
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.
The realization of the Intelligent Transportation Systems will effectively solve the problem of traffic congestion and urban traffic pollution, improve the road capacity and traffic safety. A crucial key of the realization of the ITS is the estimate and prediction of travel time: how to make and continuously update prediction of travel time for several minutes into the future using real-time data. In order to analyze the trend of the travel time accurately, combined with superiority of wavelet in dealing with time-varying information, a combined method for travel time prediction based on wavelet denoising and exponential smoothing and metabolic curve fitting model is presented in this paper. The new method has general adaptability because of the methods we selected and the combination strategy we proposed and the use of metabolic treatment in the model. We contrast the new model with models we choose at the end of the paper. And then make a clear analysis and compare on the models. The conclusion shows that the new model is applicable for the real condition and has a better result.
Keywords
curve fitting; driver information systems; prediction theory; road safety; road traffic; exponential smoothing; intelligent transportation systems; metabolic curve fitting model; real-time data; time-varying information; traffic congestion; traffic safety; travel time prediction; urban traffic pollution; wavelet denoising; Analytical models; Computational modeling; Fitting; Kalman filters; Laboratories; Prediction methods; Wavelet analysis; Intelligent Transportation Systems; combined prediction; component; exponential smoothing; metabolic curve fitting;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location
Taiyuan
Print_ISBN
978-1-4244-7235-2
Type
conf
DOI
10.1109/ICCASM.2010.5620015
Filename
5620015
Link To Document