Multiple regression analysis of the impact of track geometry on wheel-rail forces

DOIResolve DOI:
AuthorSearch for: ; Search for: ; Search for:
Proceedings title2012 Joint Rail Conference, JRC 2012
Conference2012 Joint Rail Conference, JRC 2012, 17 April 2012 through 19 April 2012, Philadelphia, PA
Pages245253; # of pages: 9
AbstractOne of the fundamental keys to improving track safety standards is to establish a strong correlation between track geometry variations and wheel-rail force parameters that are indicators of vehicle-track safety performance. In this study, wheel-rail forces were collected during field tests of a loaded lumber car and an empty tank car. Computer models of the two tested freight cars were built, and the models were calibrated using field test results. The computer models were then used to evaluate the impact of varying track geometry parameters on track safety using the maximum single wheel L/V ratio, maximum truck side L/V ratio, and minimum vertical wheel load ratio. It was confirmed again that the correlations between these force parameters and any individual geometry parameter were weak. With further investigation, it was found that much better correlation can be achieved using multiple regression techniques to define each wheel-rail force parameter as a function of all track geometry parameters combined together. Expressions of the maximum truck side L/V ratio, maximum single wheel L/V ratio, and minimum vertical wheel load ratio were obtained as functions of curvature, cross level, alignment, gauge, and cant deficiency using multiple regression analysis. Copyright © 2012 by ASME.
Publication date
AffiliationNational Research Council Canada (NRC-CNRC); NRC Centre for Surface Transportation Technology (CSTT-CTTS)
Peer reviewedYes
NPARC number21270861
Export citationExport as RIS
Report a correctionReport a correction
Record identifier38f9e2a2-7fe4-41e6-a67f-a51153eaea44
Record created2014-02-17
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)