A Multi-stage Linear Prediction Model For
The Irregularity Of The Longitudinal Level Over
Unit Railway Sections
Author(s)
H. Chang, R. Liu & Q. Li
Abstract
Various researchers, both at home and abroad, have developed models to predict
the irregularity of longitudinal level changes, which take into account different
influential factors. In order to help the railway maintenance department to more
accurately grasp the pattern of irregularity of longitudinal level changes, the
present authors have developed a new model that uses historical track geometry
inspection data from track inspection cars to predict the irregularity of
longitudinal level changes for unit railway sections (which are taken as 200
metres long in this paper). Various factors affect the irregularity of the track.
These mainly include train operation factors, track structure and environmental
factors. However, for a certain unit railway section, key consideration may be
given to the impact of passing tonnage on the irregularity of the longitudinal
level. This paper establishes linear regression equations that are only applicable
to each particular unit section to fit the functional relationship between the
irregularity of the longitudinal level and passing tonnage. The linear fit equations
are obtained from standard deviations of inspected track level data at 200
intervals. The change curve of the irregularity of the longitudinal level is divided
into several stages and these different stages are fitted with different linear
regression equations. A multi-stage broken line is thus formed to approximate
the pattern of changes in the irregularity of the longitudinal level. On this basis,
the authors put forward a multi-stage linear prediction model for the irregularity
of the longitudinal level. Finally, we use inspected data collected from the
Beijing-Jiulong Railway Line in 2008 and 2009 to make predictions and validate
the model.
Keywords
track, irregularity of longitudinal level, linear prediction model
Related Book
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