This paper presents a comprehensive investigation of machine learning-based intrusion detection methods to reveal cyber attacks in railway axle counting networks. In contrast to the state-of-the-art works, our experimental results are validated with testbed-based real-world axle counting components. Furthermore, we aimed to detect targeted attacks on axle counting systems, which have higher impacts than conventional network attacks. We present a comprehensive investigation of machine learning-based intrusion detection methods to reveal cyber attacks in railway axle counting networks. According to our findings, the proposed machine learning-based models were able to categorize six different network states (normal and under attack). The overall accuracy of the initial models was ca. 70-100% for the test data set in laboratory conditions. In operational conditions, the accuracy decreased to under 50%. To increase the accuracy, we introduce a novel input data-preprocessing method with the denoted gamma parameter. This increased the accuracy of the deep neural network model to 69.52% for six labels, 85.11% for five labels, and 92.02% for two labels. The gamma parameter also removed the dependence on the time series, enabled relevant classification of data in the real network, and increased the accuracy of the model in real operations. This parameter is influenced by simulated attacks and, thus, allows the classification of traffic into specified classes.

European trends in children’s travel patterns show decreasing rates of trips walked or cycled. Against this background, a concept for a smartphone app was developed to promote active travel modes for children and adolescents. The app collects players’ travel data as input for a game: a high level of real-life environmentally friendly and active travel modes leads to a higher score. Competing players redeem the points they have collected to reach specific virtual locations on a map and win real-life rewards (e.g., shopping vouchers). The game was developed based on the user-centered design approach—an iterative process between design, prototyping, and evaluation. This paper presents the game concept alongside the results of a prototype field test at three schools in the province of Lower Austria, comprising 57 students aged 12 to 18. Results suggest that the game was easy to use and entertaining. However, younger players lost interest faster than older ones. Players emphasized improvements in relation to mode detection and tracking of individual trips, since fair playing conditions were requested by all age groups. Although the level of knowledge about sustainable mobility is high among young people, the game was rated as being a good tool for raising awareness regarding the environmental and health effects of mode choice. The promising results of this research project need to be transferred into a business model that can provide ongoing game updates, keep the players interested, and achieve long-term effects.

Railway operators are in continuous pressure to minimize maintenance and rehabilitation costs of infrastructures; at the same time they are expected to provide a reliable service by optimum allocation of natural and economical resources. Railway tracks and level crossings are long-lived assets where their service life stretch 30 to 100 years. This paper aims to show an approach that serve as a decision support whereby expert's knowledge can directly be integrated by using a delphiround. A proposed methodology is illustrated by application examples using level crossings commonly used in Austria by Wiener Lokalbahnen. Lifecycle based user – product performance – expectations and economic imperatives could be incorporated through the development of key performance metrics. Assessment criteria are set by allocation percentages for value criteria under these criteria are the sub value – contingencies where Monte Carlo simulation is used for probabilistic scenario analysis. The approach facilitates the first step for a detailed lifecycle cost analysis of infrastructures by supporting experts and non-expert decision makers to get a quicker overview on system benefits as well as serve as a bridge to the practical application of more robust data-driven lifecycle cost analysis.