## 4 Results and Discussion  
In this section, the track quality of Graz University of Technology (TUG_TQI) is applied on the introduced 5-km test section (Figure 7), thus providing a visual representation of the methodology. By means of this graph, the qualities of the new index are then discussed. The plot includes  
Figure 6: Splitting and normalizing the gauge signal. measurement signals of the track section, the individual indices of the geometry parameters, and the holistic TUG_TQI. The TUG_TQI is well-suited to describe the overall geometrical track quality, as it incorporates all characteristics of the individual indices. The differences between better and worse track quality can be observed in the example of the longitudinal level. The index delivers low values from km 1.150 to km 1.850, where the raw signal shows good track quality. By contrast, at around km 3.500, the input signal scatters in a high amplitude range. Consequently, the TQI_Longitudinal_level features higher values in this area, which represents worse quality. Similar characteristics can be observed in the alignment index, which additionally reveals another aspect of the quality index. Not only does it accurately depict alignment issues over longer track sections, but it also reacts to isolated defects, as the spike at km 0.100 proves. The severity of this defect is suspicious and potentially a result of a data measurement error: the input signal exceeds even the intermediate action level (28 mm) by far and inexplicably no other signal shows any abnormality at this position [38]. This example confirms that the methodology also correctly represents isolated defects. The track gauge index underlines the necessity to consider  
Figure 7: Measurement signals (primary y-axis) with their respective indices (secondary y-axis) and TUG_TQI, calculated for the 5-km test section. positive and negative gauge values separately, as described in [18]. Although the absolute amplitudes of the input signal reach considerably higher values in the positive range (e.g., between km 3.500 and km 4.000), the corresponding index delivers relatively low values. In contrast, the index takes on significantly higher values in case of track narrowing (e.g., around km 3.000) because of very different threshold levels. The cross level index is continuously low since the input signal scatters in an uncritical value range. In contrast, the twist index locally delivers much higher values. This is also reasonable considering the strict threshold values for this parameter and the partially strong irregularities of the signal. The overall TUG_TQI combines all effects-the isolated defect of the alignment signal at km 0.100, the good quality of all signals between km 1.150 and km 1.850 as well as the geometry issues around km 3.500. For visualization reasons, the TQI is cut off at 1.0; otherwise, the isolated defect at km 0.100 would distort the plot and make the remaining curve uninterpretable. Arguably the most widely used indices to describe the geometrical track condition are standard deviations (SD). The standard deviation is thus consulted as the benchmark for TUG_TQI. Using track data from another European railway network, SD and TUG_TQI are calculated and directly compared. Figure 8 visualizes the results of the longitudinal level, for which SD and TUG_TQI deliver similar results, and the twist, where the two methodologies partly differ. The resulting curves are z-transformed and shifted along the y-axis until their lowest values equal zero (see Section 2.3). The uppermost signal in Figure 8 represents the curvature in mm, which depicts the line routing. The section consists of a straight track from the beginning to km 131.055, a curve between km 131.160-131.710 as well as transition curves in between. As for the longitudinal level, standard deviation and individual TUG_TQI are almost equal (neglecting the absolute level). This can be interpreted as validation of the TUG_TQI, as the standard deviation is a well-established parameter.