초록 ▼

A method for aligning measured track data collected from a railroad track to correct geographic location information for geometric parameters in the measured track data includes steps for (a) obtaining track geography data for use as reference data in data alignment; (b) reconstructing the track geo

A method for aligning measured track data collected from a railroad track to correct geographic location information for geometric parameters in the measured track data includes steps for (a) obtaining track geography data for use as reference data in data alignment; (b) reconstructing the track geography data to simulate in form and in coverage of length the measured track data to be aligned, (c) comparing the reconstructed reference data to the measured track data to identify a relative misalignment value between the data types; and (d) using the value identified through comparison to correct the geographic location information contained in the measured track data.

대표청구항 ▼

1. A computerized system for aligning measured track data collected from a length of railroad track to correct geographic location information for geometric features contained in the data comprising: a first data repository containing track geography data; a second data repository containing the me

1. A computerized system for aligning measured track data collected from a length of railroad track to correct geographic location information for geometric features contained in the data comprising: a first data repository containing track geography data; a second data repository containing the measured track data; and a processing component for comparing the measured tack data to the track geography data; characterized in that the track geography data is reconstructed to match in format and track length to the measured track data and then cross-correlated reference data to the measured track data, the cross correlation made in whole and or in matching portions thereof for purpose of identifying shift in alignment between the data types, the shift relating to misalignment of geometric and geographic signatures present in both data types including shift identified as odometer error value in the measured track data, the identified shifts used to correct geometric, geographic, and odometer error misalignment in the measured track data with respect to the reference data. 2. The system of claim 1 maintained in and accessible from a track-geometry test vehicle. 3. The system of claim 1 maintained externally from but accessible in part to a track-geometry test vehicle. 4. The system of claim 1 wherein the geometric data used for alignment comprises one or a combination of curvature data, cross-level data, gage data, super-elevation data, rail twist data, and rough feature location information. 5. The system of claim 1 wherein the track geography data is available from and taken from a known Railway Information System data repository. 6. The system of claim 1 wherein the measured track data after shift correction is subsequently used as previously aligned data for reference used in further alignment of data recorded at a later date over the same track length. 7. The system of claim 1 wherein data reconstruction of the track geography data includes data reformatting to simulate the data format of the measured track data. 8. The system of claim 7 wherein data reconstruction construction of the track geography data includes segmentation to produce segments of track geography data representing data occurring over a specified track length. 9. The system of claim 1 wherein shift in alignment due to odometer error is identified through linear regression. 10. A method for aligning measured track data collected from a railroad track to correct geographic location information for geometric parameters in the measured track data comprising steps of: (a) obtaining track geography data for use as reference data in data alignment; (b) reconstructing the track geography data to simulate in form and in coverage of length the measured track data to be aligned; (c) cross-correlating the reconstructed reference data to the measured track data to identify a relative misalignment value between the data types; and (d) using the value identified through comparison to correct the geographic location information contained in the measured track data. 11. The method of claim 10 wherein in step (a) the track geography data is available from and taken from a known Railway Information System data repository. 12. The method of claim 10 wherein in step (a) the track geography data contains feature location information and at least some if not all data types describing curvature data, cross-level data, gage data, and super-elevation data. 13. The method of claim 10 wherein in step (b) the track geography data is reconstructed to produce segments of track geography data representing data occurring over a specified track length including geometric data of features and feature location information located along she specified length. 14. The method of claim 10 wherein in step (c) primary parameter to be compared is curvature data. 15. The method of claim 10 wherein step (c) the primary parameter to be compared is super-elevation. 16. The method of claim 10 where in in step (c) the primary parameter to be compared is cross-level measurement. 17. The method of claim 10, wherein in step (c) the primary parameter to be compared is gage measurement. 18. The method of claim 10 wherein in step (b) the track geography data lacks curvature information of curves contained therein and the reconstruction thereof uses the ratio between super-elevation and curvature data to predict type direction and magnitude of curves. 19. The method of claim 10 wherein in step (b) track geography data is divided into segments of pre-determined track lengths using a constrained optimization algorithm wherein the total length of segments not satisfying geometric constraints is minimized over a length of track for alignment consideration. 20. In a data alignment process for aligning measured track data collected along a length of railroad track to a reference data set for the same length of track, a method for coarse estimation of odometer error manifest along the track length of measured track data and refining the coarse estimate to produce a final estimate used in correcting the actual odometer error manifest in the measured track data comprising steps of: (a) creating a plurality of simulated data sets from the measured track data, each data set simulating a different odometer error value, each value taken at a different predetermined interval point along a predetermined maximum error range applied to the measured track data set, the range having a zero interval point at center thereof; (b) cross-correlating each of the simulated data sets against the reference data set at each interval point along the maximum range allowed obtaining a coefficient value for each of the simulated data sets; (c) identifying a single best coefficient value from those obtained in step (b) that defines a best alignment to data contained in the reference data set; and (d) repeating steps (a) through (c) using a smaller range having smaller intervals, the smaller range centered over the range interval in the first range of the measured track data associated the best coefficient identified. 21. The method of claim 20 wherein in step (a) the error shifts are created by shrinking the measured track data to produce shift intervals along the negative side of the range and stretching the data to produce shift intervals along the positive side of the range. 22. The method of claim 21 wherein in step (a) shrinking the measured track data is accomplished by deleting a record from the data at uniform intervals a number of times until a desired amount of shrinking is produced and stretching the measured track data is accomplished by duplicating a record in the data at uniform intervals a number of times until a desired amount of stretching is produced. 23. The method of claim 20 wherein in step (a) the maximum shift range exceeds maximum odometer error manifestation possible for the specified length of the track measured. 24. The method of claim 20 wherein in step (b) the coefficient values define linear association strength between correlating interval points along the range. 25. The method of claim 21 wherein in step (c) the single coefficient value produces a coarse odometer error values. 26. The method of claim 20 wherein in step (d) the best coefficient found after correlating all of the simulated data sets of the smaller range intervals against the reference data set produces a final odometer error estimate for the measured track data set. 27. In a data alignment process for aligning measured track data collected along a length of railroad track to a reference data set for the same length of track, a method for estimating a value of odometer error manifest along the track length of measured track data comprising steps of: (a) cross-correlating the entire set of measured track data to the entire set of reference data to identify a relative misalignment value; (b) filtering the measured track data set to remove references to certain geo metric features; (c) dividing the length of the measured and reference data sets into smaller portions; (d) cross-correlating the smaller portions of measured data against associated portions of reference data to find relative misalignment values for each portion; (e) using line regression, fitting a line through the found misalignment values plotted sequentially for each correlated data portion on a graph; and (f) determining the magnitude and direction of slope of the fitted line indicative of the magnitude and direction of the actual calibration error manifest in the measured track data. 28. The method of claim 27 wherein in step (a) the reference data comprises previously aligned measured track data aligned to track geography data as reference data. 29. The method of claim 27 wherein in step (a) geometric features and location information contained in both data sets are used to align the data sets. 30. The method of claim 27 wherein in step (b) the geometric data references removed describe curvature data and those retained describe one or both of cross-level features and gage measurement features. 31. The method of claim 28 wherein in step (d) the geometric parameter for alignment is cross-level measurement. 32. The method of claim 28 wherein in step (d) the geometric parameter for alignment is gage measurement. 33. The method of claim 28 wherein steps (a) through (f) are carried out in batch mode using multiple measured track data sets as input and a same previously aligned data set as reference data for a same length of track, each measured track data set collected at different test runs performed at different times.