Optimal Match Method for Milepoint Postprocessing of Track Condition Data from Subway Track Geometry Cars

Precise milepoint measurement data are essential for better subway track management and maintenance practices within railroads and subways. For milepoint estimation, dead reckoning systems of some subway track geometry cars use as pesudolite markers having no unique identification information. In such cases, milepoint measurement data have to be postprocessed. However, the postprocessing is conducted in a manual fashion and is time consuming and labor intensive. This paper presents an optimal match method to automatically postprocess milepoint measurement data. The presented method consists of three submodels: (1) dynamic-programming-based distribution-pattern match model for differentiating actual markers from false-positive ones, (2) correlation-analysis-based algorithm determining milepoints for recognized markers, and (3) a linear interpolation equation for milepoint revision. The method was applied to 124 inspection runs for 15 tracks of the Beijing subway system whose track geometry car is such a case. It is shown that the developed method outperforms the manual method in milepoint accuracy. It takes the developed method less than 3 min to complete milepoint revision for an inspection run of the longest track in Beijing subways.

Language

  • English

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Filing Info

  • Accession Number: 01598294
  • Record Type: Publication
  • Files: TRIS, ASCE
  • Created Date: Apr 5 2016 3:03PM