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This book offers a comprehensive presentation of methods for analysing reliability, safety, and risk in railway control systems. It introduces a model-based approach to hybrid reliability and safety assessment, supported by a structured ontology that brings together core concepts from reliability engineering, functional safety, and cybersecurity. It covers structural and functional reliability, the prediction of rare dangerous events, and the mathematical models and metrics used to evaluate critical railway components. Approaches for normalising reliability indicators, analysing switching algorithms, and understanding systematic failures complement the discussion, together with expert-based techniques for estimating failure distributions where statistical data are limited. Modern challenges, such as the safety of AI-supported systems, information security risks, and the role of digital twins in preventing hazardous failures, are explored in depth. Practical examples include derailment-risk reduction, assessment of train movements under prohibited signals, and the use of automated signalling to improve safety at railway stations. Graph-based methods for evaluating operational safety and tools for modelling fire risks on locomotives are also included. It further presents methods for analysing incomplete expert preferences and constructing risk matrices within the ALARP framework, applicable not only to railway systems but also to other high-reliability industries.

Providing both theoretical foundations and practical applications, this book is a valuable resource for railway engineers, safety specialists, risk analysts, and students in railway and transport engineering.