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Today's railway transport is built upon high-performance infrastructure. Cost-effective yet sustainable infrastructure presumes tracks with a precise and durable geometry. At ballasted tracks, the geometry is created and restored through tamping machines, which position the track panel and compact the ballast beneath the sleepers. It is commonly agreed that the ballast compaction plays an important role in the long-term stability of the track. Yet, there is no method available which allows a direct correlation between the compactness of the ballast and the stability of the track geometry. Available studies either model track behaviour without considering the bedding, or analyse ballast compactness locally while disregarding its influence on the track geometry. This paper presents a new methodology which establishes a relation between these two topics—ballast compaction during tamping and subsequent track behaviour. A state-of-the-art tamping machine has been equipped with an experimental measurement setup, constantly recording relevant data during every tamping process. These data can be used to derive an indication for the achieved compaction under every sleeper. Utilising the tamping machine's internal measuring system for track geometry documentation, every tamping process (every sleeper) is assigned to the precise position along the track. The data set is merged and synchronised with regular track geometry measurements of the infrastructure manager. The result is a comprehensive data set which allows precise analyses between tamping machine measurements and track behaviour. This data set provides the foundation for future research, aiming towards a better understanding of the tamping process and its influence on the quality and durability of the established track geometry. [ABSTRACT FROM AUTHOR]

The positive effects of under sleeper pads have already been proven by track and laboratory tests worldwide. In Austria, padded concrete sleepers reduce track deterioration by 50 percent and have therefore been used as standard components since 2010. As the pads increase material costs, many infrastructure managers have discussed the idea of covering costs by reducing the ballast bed thickness. Technically, this approach (less ballast) is feasible, as the pads increase the track elasticity and protect the ballast. Further, pads lead to higher rail deflection and distribution of the load to more sleepers, and the stresses in sleepers are therefore relieved. Here, we compared Austrian test sections with padded and unpadded concrete sleepers and with a difference in the ballast bed thickness: standard thickness of 30 cm against 20 cm below the sleeper. Fractal analyses and standard deviations of the track's longitudinal level provided information about the sections' long-term track behaviour over 20 years. We found that the standard solution with 30 cm ballast performed better in the long term compared to 20 cm. Additionally, the test section with padded sleepers on a reduced ballast bed thickness showed a lower maintenance demand than the unpadded concrete sleeper track on a 30 cm ballast bed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF. [ABSTRACT FROM AUTHOR]