In every business, the production of knowledge, coming from the process of effective information, is recognized as a strategic asset and source of competitive advantage. In the field of railways, a vast amount of data are produced, which is necessary to be assessed, deployed in an optimum way, and used as a mechanism, which will lead to making the right decisions, aiming at saving resources and maintain the fundamental principle of the railways which is the passengers’ safety. This paper uses stored-inactive data from a Greek railway company, and uses the method of data mining and applies machine learning techniques to create strategic decision support and draw up a risk and control plan for trains. We make an effort to apply Machine Learning open source software (Weka) to the obsolete procedures of maintenance of the rolling stock of the company (hand-written work orders from the supervisors to the technicians, dealing with the dysfunctions of a train unit by experience, the lack of planning and coding of the malfunctions and the maintenance schedule). Using the J48 and M5P algorithms from the Weka software, data are recorded, processed, and analyzed that can help monitor or discover, with great accuracy, the prevention of possible damage or stresses, without the addition of new recording devices—monitoring on trains, with the aim of predicting the diagnosis of the train fleet. The innovative method is capable of being used as a tool for the optimization of the management’s performance of the trains to provide the appropriate information for the implementation of planning and the technical ability of the trains in order to achieve the greatest target of importance for the railways, which is the passengers’ safety.

The successful implementation of Business Intelligence (BI) technologies in public transport relies primarily on the Internet for connectivity to facilitate real-time data transfer and communication within the vehicle or system. This contributes to service quality, and due to a positive influence on the environment, it is a sustainable solution. Beyond chatbots and digital assistants, Business Intelligence (BI) technologies can change the face of urban transportation. Equipped with BI and advanced analytics, transport networks will be able to offer better, timely, more personalized services that will enable better decision-making, reduce operational costs, and enhance sustainability. The aim of the paper is to describe the application of Business Intelligence tools in process enhancement at public transport companies with a focus on urban transportation. Integration of such technologies allows new decision-support strategies to be created that will add to sustainable solutions with a positive environmental footprint. More specifically, it investigates how data mining and machine learning, supported by low-cost, open-source tools like Weka and KNIME, can upgrade the processes of transport service providers. The study also investigates the incentives and benefits for companies in charge of providing safe urban transportation through the adoption of these technologies. These tools will help the companies increase their efficiency by reducing operational costs and hence improve the quality of the services. Finally, the results and incentives of transport organizations are presented in order to create applications that can be the main tool for improving decision-making by company management and developing supportive strategies that will lead to sustainable development and efficient urban transport systems.