The deterioration of ballast and the consequences of weak subsoil increase the need for track maintenance. These problems reduce the service life of tracks, and a higher maintenance budget is required. This paper introduces an innovative method for evaluating the condition of the ballast and the subsoil. Fractal analysis of the vertical track geometry enables infrastructure managers to determine the root cause of track irregularities by quantifying the wavelength characteristics of the common track geometry data. This methodology determines whether the ballast or the substructure condition is the cause of poor track quality. Fractal analysis has been carried out to date on the main network of the Austrian Federal Railways, which comprises some 4000 km of track, as well as within the network of Suisse Federal Railways (∼5000 km). Additionally, several lines in the United States (Amtrak), Belgium (InfraBel), and Denmark (BaneDanmark) have been evaluated. A detailed validation process has been carried out to investigate different characteristics and their correlation to the in situ behavior of the track. This research is now showing promising results after being cross-checked with historical data and compared to the implemented maintenance measures. On-site inspections have confirmed the evaluated component-specific condition.

Adequate railway track condition is a prerequisite for safe and reliable railway operation. Many track quality indices (TQIs) have been developed with the aim of assessing the track condition holistically. These indices combine measurement signals of some or all relevant geometry parameters with different mathematical models. In this paper, a selection of important TQIs is evaluated. Using measurement data of a five kilometer track section, the indices are calculated and their properties are discussed. This study reveals that all indices exhibit drawbacks to varying degrees. As a consequence, a new index has been developed—the track quality index of Graz University of Technology (TUG_TQI). Its favorable characteristics are presented by means of the above-mentioned test section. The TUG_TQI combines all relevant track geometry parameters, which are normalized beforehand to eliminate over or underrepresentation of different parameters. Thus, the index reliably describes the overall geometrical track quality.

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Track engineers face increasing cost pressure and budget restrictions in their work today. This leads to growing difficulty in legitimizing crucial maintenance and renewal measures. As a result, infrastructure managers must ensure they invest all available financial resources as sustainably and efficiently as possible. These boundary conditions require an objective tool enabling both a component-specific condition evaluation and preventive maintenance with renewal planning. The present research introduces such a tool for railway tracks based on innovative track data analyses. This tool includes time-series analyses for predicting future quality behavior. Consequently, the technical necessity of maintenance actions can be derived for every specific track section. In addition, these technical evaluations are combined with economic and operational considerations to plan reasonable maintenance lengths for different track components in the next few years. In a further step, business evaluations by means of annuity monitoring are executed to determine whether ongoing track maintenance or complete track renewal is the most economical solution. This methodology also allows calculating the economic damage caused by neglecting the ideal point in time for reinvestment. On the basis of this economic damage, it is possible to rank projects by priority in the case of insufficient budgets and to ensure that all available resources are invested in the most reasonable manner possible. Furthermore, such analyses clearly show that when a specific degradation level of railway track is reached track renewal is more economic in relation to life-cycle costs than ongoing maintenance.

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