DocumentCode
2218262
Title
Notice of Retraction
Data Disposal in Traffic Flow Theory Researching
Author
Xiaodong Zang
Author_Institution
Sch. of Civil Eng., Guangzhou Univ., Guangzhou, China
fYear
2009
fDate
26-28 Dec. 2009
Firstpage
829
Lastpage
832
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 field data are the base of traffic flow theory, but to get accurate data is difficult, especially, in the data disposal, this is because the data collection period is very long, the quantity of sample is very large, and the data are surveyed by automatic apparatuses generally now, and there are many abnormal data in all data sequence. During the data are disposed, to distinguish the abnormal data is very difficult, and to dispose the data by people is unpractical, using a automatic method is necessary. After analyzing and contrasting, the traditional methods, such as the method of probability and data smoothness, were proved to be unfit for data disposal, then the RBF artificial neural networks is introduced to do this because of its characteristics of nonlinear mapping, dealing with parallel data and self-studying, and a RBF of three layers with two nerve cells in input layer and one nerve cell in output layer is built, after training based on the actual data, using the RBF to dispose the raw data, the result shows that the method can distinguish the abnormal data from all the raw data, and the method is feasible.
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 field data are the base of traffic flow theory, but to get accurate data is difficult, especially, in the data disposal, this is because the data collection period is very long, the quantity of sample is very large, and the data are surveyed by automatic apparatuses generally now, and there are many abnormal data in all data sequence. During the data are disposed, to distinguish the abnormal data is very difficult, and to dispose the data by people is unpractical, using a automatic method is necessary. After analyzing and contrasting, the traditional methods, such as the method of probability and data smoothness, were proved to be unfit for data disposal, then the RBF artificial neural networks is introduced to do this because of its characteristics of nonlinear mapping, dealing with parallel data and self-studying, and a RBF of three layers with two nerve cells in input layer and one nerve cell in output layer is built, after training based on the actual data, using the RBF to dispose the raw data, the result shows that the method can distinguish the abnormal data from all the raw data, and the method is feasible.
Keywords
radial basis function networks; traffic engineering computing; RBF artificial neural networks; abnormal data; data collection; data disposal; data smoothness; nonlinear mapping; parallel data; traffic flow theory; Artificial neural networks; Civil engineering; Data engineering; Filters; Information science; Laboratories; Microwave theory and techniques; Neural networks; Telecommunication traffic; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-4909-5
Type
conf
DOI
10.1109/ICISE.2009.446
Filename
5454953
Link To Document