Traffic Track Dynamic Data Analysis Based on BP Neural Network Model

The prediction of track irregularity requires the mileage correction of the data collected by the track inspection car. Mileage correction of track data can directly affect the evaluation of railway management personnel on track diseases. Mileage correction of track data can directly affect the evaluation of railway management personnel on track diseases. In this paper, the construction of background value, the selection of initial value and the optimization model of weight proportion direction of detection data are applied to capture the trend change law of track shape with greater advantages. In the part of stochastic term development, BP neural network model is introduced to modify the residual sequence of track irregularity. The final experimental results show that the relative prediction error of the proposed model is reduced by 18. 2% and 15. 7% respectively, compared with the single gray model. The validity and robustness of the model are verified. The improved track irregularity prediction model has good prediction accuracy, and the preventive maintenance decision scheme of railway tracks can be realized according to the model.