Determinants of autonomous train operation adoption in rail freight: knowledge-based assessment with Delphi-ANP approach

Abstract

AbstractRailways are the backbone of complex and multimodal transport systems in Europe. To secure a larger share of the transport market and attract existing and future freight customers, further improvement in services is required. To achieve this goal, the use of new technologies in the era of railway automation and digitalization is required. Automatic train operation (ATO) in rail transport is considered a promising solution for providing cost-effective rail products. In this study, we define ATO as autonomous train operation and the feasibility of ATO deployment in rail freight is investigated. For this purpose, a knowledge-based approach is introduced to identify opportunities, problems, and the most appropriate grade of automation in rail freight. In a multi-stage process, Delphi questionnaires were combined with the analytic network process (ANP) method to investigate, define, and weight the determinants for ATO deployment. The final phase of the survey estimated the potential costs and drivers for different grades of automation. The results show that, in addition to the positive impacts of ATO, there are numerous challenges and risks that need to be analysed before ATO is implemented. In addition, the Delphi-ANP approach was used to identify the key determinants for decision-making prior to ATO implementation and the most viable alternative based on them. Investment cost, level of safety, energy saving, and reliability of management system are the most important determinants for the decision to implement ATO. The results of this study can effectively support rail infrastructure managers and operators in strategic planning and decision-making for ATO implementation in rail freight.