Notice of RetractionThe application and research in reducing the errors of traditional traffic volume prediction using an improved BP neural network

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.

Traffic analysis and prediction is one of the core contents in the feasibility study of highway construction project [1]. It has the vital significance to the highway construction and road networks development. The traditional traffic volume prediction, as four steps prediction method [2] represented, have many uncertain factors to make the deviation between final forecast results and actual situation is larger, and unable to achieve the expected effect. This paper takes that reducing the errors of the indefinite factors influencing the results as a starting point, improves the standard BP neural network [3] to solve the problems appearing in training, and puts it into the traffic volume prediction model which applied in engineering instances. Forecasting results show that this method predicts accurately and efficiently, and achieves the purpose of reducing prediction errors.