To meet the increasing demands for availability at reasonable cost, operators and maintainers of railway point machines are constantly looking for innovative techniques for switch condition monitoring and prediction. This includes automated fault root cause diagnosis based on measurement data (such as motor current curves) and other information. However, large, comprehensive sets of labeled data suitable for standard machine learning are not yet available. Existing data-driven approaches focus only on the differentiation of a few major fault categories at the level of the measurement data (i.e., the “fault symptoms”). There is great potential in hybrid models that use expert knowledge in combination with multiple sources of information to automatically identify failure causes at a much more detailed level. This paper discusses a Bayesian network diagnostic model for determining the root causes of faults in point machines, based on expert knowledge and few labeled data examples from the Netherlands. Human-interpretable current curve features and other information sources (e.g., past maintenance actions) are used as evidence. The result of the model is a ranking of the most likely failure causes with associated probabilities in terms of fuzzy multi-label classification, which is directly aimed at providing decision support to maintenance engineers. The validity and limitations of the model are demonstrated by a scenario-based evaluation and a brief analysis using information theoretic measures. We present the information sources used, the detailed development process and the analysis methodology. This article is intended to be a guide to developing similar models for various complex technical assets.

Continuous wheel condition monitoring is indispensable for the early detection of wheel defects. In this paper, we provide an approach based on cepstral analysis of axle-box accelerations (ABA). It is applied to the data in the spatial domain, which is why we introduce a new data representation called navewumber domain. In this domain, the wheel circumference and hence the wear of the wheel can be monitored. Furthermore, the amplitudes of peaks in the navewumber domain indicate the severity of possible wheel defects. We demonstrate our approach on simple synthetic data and real data gathered with an on-board multi-sensor system. The speed information obtained from fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU) data is used to transform the data from time to space. The data acquisition was performed with a measurement train under normal operating conditions in the mainline railway network of Austria. We can show that our approach provides robust features that can be used for on-board wheel condition monitoring. Therefore, it enables further advances in the field of condition based and predictive maintenance of railway wheels.

Area-wide measurements of traffic flow are usually not possible with today's common sensor technologies. However, such information is essential for (urban) traffic planning and control. Hence, in order to support traffic managers, this paper analyses an approach for deriving traffic flows from probe vehicle speeds, which are potentially available with a wide spatial coverage. The idea is to apply the speed-flow function as known from macroscopic traffic flow theory. In this context, a stochastic representation of the fundamental diagram via Bayesian networks is proposed which also considers the temporal dependencies and transitions between the appearing traffic states. The paper describes the relevant theoretical concepts in comparison to the traditional approach of fitting deterministic curves to empirical speed-flow relations. Moreover, it analyses the findings of an extensive validation in context of traffic flow estimation via probe vehicle data using real traffic measurements provided by about 600 local detectors and about 4,300 taxi probes in Berlin, Germany.

In their recent paper [Phys. Rev. E 79, 031115 (2009)], Ning Jia and Shoufeng Ma used some Markov chain arguments for the analytical description of inflow in the deterministic Nagel-Schreckenberg model with open boundaries. In this context, they considered two different mechanisms of injecting vehicles: the standard injection rule and a popular expanded injection rule. While the results for the first one seem to be correct, simulations show that the inflow formula in case of the expanded injection rule yields only approximate results. Therefore, this comment provides the exact formula also in this case and explains the shortcoming in the derivation of Jia and Ma

Traffic data fusion has much to do with combining available or considered data sources in the best possible way. In this, it is very similar to optimizing a portfolio of financial assets in regard of return and risk. This article draws the analogy between these two mostly different scientific worlds, i.e. finance and engineering. Similarities and differences in context of weighted-mean data fusion based on numerical traffic flow measurements such as travel times or speeds are discussed. This, in particular, includes guessing the potential benefit of negative weights. Optimal weights are derived following a strict mathematical theory based on assumptions (parameters) about systematic bias and correlations of the considered data sources. Moreover, a specific way of reducing the systematic bias of the fusion results is proposed and compared to common methods. The whole approach is demonstrated based on position data from two independent vehicle fleets in Athens, Greece. In this context, the problem of parameter calibration is solved by applying an advanced tool for such floating car data systems, called “self-evaluation”. The experiments show that the proposed methods reliably reduce the systematic bias and variance of the fusion results with regard to the original data as well as in comparison to the naïve fusion approach that uses equal weights for all data sources. Copyright © 2015 John Wiley & Sons, Ltd.