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In microprocessor automatic block signalling (ABS) systems all the basic functionality is implemented in software. Current technique used by Russian educational organisations for training of engineering staff is solely based on functionality exploration of the microprocessor ABS equipment. We suggest that this approach is not sufficiently effective and could be improved. In our view, the improvement could be achieved by addition of extensive ABS algorithm development practice to the Railway Signalling Engineering curriculum. In this article, we present a prototype of a special microprocessor learning board, which supports development and debugging of various ABS algorithms. Along with the functional diagram, we give some technical details and present preliminary results of implementing and using this prototype.

The quality of a track circuit monitoring system is defined to the great extent by its ability to automatically analyse the acquired data, i. e. identify the state of a track circuit. Machine learning techniques can be applied to implement this functionality. Designing a machine learning algorithm requires a learning data set from the subject domain. In this article we discuss a basic principle of track circuit mathematical model design that could be used to generate such data set. We also apply this principle and a combination of some existing methods to design and demonstrate a mathematical model of the 25 Hz AC track circuit.