Excellent condition of track geometry status is the foundation to ensure train travel security. The detection data of track inspection car contains many valuable features of the track status. The technique of gray forecast and Kalman filtering can be used to investigate the problem and predict the status change of the track geometry. In this paper, gray forecast is used in qualitative analysis of track geometry status changes, and we predict the development of track geometry status change using the Kalman filter prediction model and specific recursive algorithm, established prediction model of the track geometry to make an emulation experiment to analyze the data that track inspection car has detected, and predict changing trends of track geometry the state. Experiment results show that the application model of improved Kalman filter to predict the track geometry status changes gets a higher accuracy, and it can reflect the real change tendency of the track status.

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