Notice of RetractionData Disposal in Traffic Flow Theory Researching

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.