A Multi-objective approach for position alignment of track geometry measurements

This study aimed to develop a multi-objective approach for reducing the positional errors in geometry measurements of track as linear asset. Accordingly, we evaluated and compared two alignment methods – recursive segment-wise peak (RSPA) modified correlation optimised warping (MCOW). Furthermore, novel rule-based was introduced avoid data loss while aligning datasets assets. A case conducted implement assess performance these measurements. The results revealed that method preserves all single defects present datasets. RSPA outperforms MCOW when peaks, whereas is more efficient points have equal priority.