Railway track is one of the main railway facilities and the basis for trains to operate on. In order to keep passenger and freight trains moving safely, stably and uninterruptedly, the railroads must ensure track roughness low. The launch of high-speed and high-haul trains requires lower roughness of track, and consequently expects the railroads to accurately maintain or repair railway track. In this paper, base on the characteristics of changes in track irregularity, we have developed a mathematical model called SRPM which uses track waveform data generated by track geometry car (TGC) to predict the track irregularity over a short track section with the length of 100m for each day in a future short-range period of time. For verify the effectiveness of SRPM, we applied the model to 25 sets of TGC-generated track waveform data from Beijing-Shanghai railway (Jing-Hu railway) administrated by Jinan Railway Bureau (JRB) to make short-range predictions for track irregularity over each unit section of the line segment. Finally, these SRPM predictions were analyzed in both spatial and temporal dimensions. From the analysis results, we come to the conclusion that SRPM developed in this paper can fairly accurately make short-range predictions for track irregularity over each unit track section of the JRB-administrated Jing-Hu railway.
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