Key Equipment Identification model for correcting milepost errors of track geometry data from track inspection cars

Track geometry data from track inspection cars is an important data source for maintenance-of-way departments to evaluate track geometry irregularities. However, there are errors in mileposts of track geometry data. The milepost errors not only increase work intensity of track maintenance workers and decrease track maintenance window utilization, but also have negative influences on predictive track maintenance technique researches currently carried out around the world. To reduce milepost errors, milepost correction systems employing modern positioning technologies, such as Global Positioning System (GPS), Differential Global Positioning System (DGPS) and Radio Frequency Identification (RFID), have been incorporated into track geometry measurement systems on the track inspection cars. Due to various factors peculiar to Chinese railroad systems, these correcting systems don’t seem to offer satisfactory solutions to the milepost correction problem. Field investigations have revealed that the milepost position could be off up to 200 m. To address the issue, a model named Key Equipment Identification (KEI) has been developed in this article using maintenance-of-way infrastructure data and track geometry data. KEI can automatically identify curves in main tracks and sidetracks, and diverging tracks of turnouts. These three kinds of equipment are referred to as Key Equipment (KE). This identified equipment list provides accurate references for correcting mileposts of track geometry data. Following KEI development, performance of KEI and milepost errors of corrected track geometry data are analyzed using maintenance-of-way infrastructure data and track geometry data collected from the Jinan bureau of China Railroads, one of 18 railroad bureaus in China. The analysis results show that all passed KEs in the course of inspections are accurately identified from track geometry data and are precisely positioned in the inspection data. After mileposts of track geometry data are corrected according to outputs of KEI, differences between actual mileposts and corrected mileposts are far below 5 m and milepost differences of sampling points between two corrected inspections fall in the range of 1 m.

[1]  G Riboulet,et al.  Maintenance optimization for a system with a gamma deterioration process and intervention delay: Application to track maintenance , 2009 .

[2]  Xin Yao,et al.  A Computational Intelligence Approach to Railway Track Intervention Planning , 2008, Evolutionary Computation in Practice.

[3]  Eckehard Schnieder,et al.  A Heuristic Approach to Railway Track Maintenance Scheduling , 2010 .

[4]  Mauro Dell’Orco,et al.  New Decision Support System for Optimization of Rail Track Maintenance Planning Based on Adaptive Neurofuzzy Inference System , 2008 .

[5]  Futian Wang,et al.  Research on a Short-Range Prediction Model for Track Irregularity over Small Track Lengths , 2010 .

[6]  Masashi Miwa,et al.  Actual Data Analysis of Alignment Irregularity Growth and its Prediction Model , 2005 .

[7]  Peng Xu,et al.  A short-range prediction model for track quality index , 2011 .

[8]  Yang Ai-hong.  Automatic Correct Milepost System of geometry inspection car based on RFID , 2009 .

[9]  Robert B. Noland,et al.  Current map-matching algorithms for transport applications: State-of-the art and future research directions , 2007 .

[10]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[11]  C. Esveld.  Modern railway track , 1989 .

[12]  Igoris Podagelis,et al.  Influence of gauge width on rail side wear on track curves , 2006 .

[13]  Gernot Hartmann.  Reports and bulletins , 2012 .

[14]  J. Greenfeld.  MATCHING GPS OBSERVATIONS TO LOCATIONS ON A DIGITAL MAP , 2002 .

[15]  Abigail L. Bristow,et al.  Developing an Enhanced Weight-Based Topological Map-Matching Algorithm for Intelligent Transport Systems , 2009 .

[16]  Javad Sadeghi,et al.  Field investigation on effects of railway track geometric parameters on rail wear , 2006 .

[17]  Dieter Pfoser,et al.  Capturing the Uncertainty of Moving-Object Representations , 1999, SSD.

[18]  Peter Veit,et al.  Sustainability in Track: A Precondition for High Speed Traffic , 2010 .

[19]  Washington Y. Ochieng,et al.  Integrity of map-matching algorithms , 2006 .

[20]  Stanislav Jovanovic,et al.  Railway track quality assessment and related decision making , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).