A Review of Literature and Public Datasets for the Application of Artificial Intelligence in the Railway Industry

Abstract: This Thesis project aims for a holistic overview of Artificial Intelligence (AI) applications in the railway industry. Our research covers diverse subdomains of railway systems such as traffic planning and scheduling, logistics and optimization, maintenance, safety and security, passenger experience, communication, and autonomous trains. The first part of this work presents a taxonomy of different terms related to AI and the railway industry. Then, we have analyzed the state of the art of AI applied to the railway industry by conducting an extensive literature review, summarizing different tasks and problems belonging to specific domains and subdomains of the railway industry and common AI-based models implemented for their solution. The existing literature reviews typically cover a limited scope either regarding specific railway subdomains or some certain aspects of AI. Within this study we present an integrated overview with special emphasis on the data used to create AI models. To achieve this, we have also conducted an extensive review on publicly available AI oriented datasets that can be used in the different railway domains. Finally, we present a blueprint for the implementation of AI within the railway industry based on our findings.The results of our research show that the possible applications of AI in the railway sector are vast and there are many problems and tasks that can greatly benefit from it. Moreover, very different types of data are implemented to feed AI models: including not only numerical, label and image data but a wide variety of data types ranging from sound, GPS coordinates, track geometry, speed and acceleration data, data from rolling stock vibrations, knowledge from experts, log data, temperature and geological data and more. Data can also be harvested using different technologies such as IoT devices, wireless networks, smart sensors, computer-based simulations and digital twins. These and more insights are discussed in detail within this project.With this study, we want to stress that the existence of available data is one of the critical aspects of AI applications in the railway industry, and we hope to benefit researchers in the fields of computer science and the transport industry alike by providing an insight into these valuable data and information on how it can be accessed and utilized.