Optimizing the Alignment of Inspection Data from Track Geometry Cars

Peng Xu, Quanxin Sun, Rengkui Liu, Reginald R. Souleyrette, Futian Wang

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Track geometry car inspections play a vital role in assessing railroad track quality and scheduling track maintenance. Effective use of inspection data depends on accurate location measurement. Field surveys reveal that measured milepoint positions can be off by up to 200 m. Previous efforts to correct milepoint errors resulted in the development of the Key Equipment Identification model, which reduces errors to below 5 m and in most cases to below 1 m for new inspections. However, analysis of track segment deterioration requires the alignment of historical inspection data. This article presents an improved method for aligning these historical inspections. The core of the approach is an optimization model termed Dynamic Sampling Position Matching (DSPM). DSPM overcomes limitations of existing methods and their assumptions that milepoint shifts between inspections are constant and that no track maintenance is carried out between inspections. A case study is presented using inspection data from the Jinan Bureau of China Railways demonstrating improved performance over two widely used inspection alignment models. Results indicate that DSPM better tolerates noisy measurements and that inspections processed by DSPM align precisely. And it takes the developed model 2.82 seconds on average to align inspection data for a track segment of 1 km.

Original languageEnglish
Pages (from-to)19-35
Number of pages17
JournalComputer-Aided Civil and Infrastructure Engineering
Volume30
Issue number1
DOIs
StatePublished - Jan 1 2015

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics

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