Print Email Facebook Twitter Assessment of architectures for Automatic Train Operation driving functions Title Assessment of architectures for Automatic Train Operation driving functions Author Wang, Z. (TU Delft Transport and Planning) Quaglietta, E. (TU Delft Transport and Planning) Bartholomeus, Maarten G.P. (ProRail) Goverde, R.M.P. (TU Delft Transport and Planning) Date 2022 Abstract Automatic Train Operation (ATO) is well-known in urban railways and gets increasing interest from mainline railways at present to improve capacity and punctuality. A main function of ATO is the train trajectory generation that specifies the speed profile over the given running route considering the timetable and the characteristics of the train and infrastructure. This paper proposes and assesses different possible ATO architecture configurations through allocating the intelligent components on the trackside or onboard. The set of analyzed ATO architecture configurations is based on state-of-the-art architectures proposed in the literature for the related Connected Driver Advisory System (C-DAS). Results of the SWOT analysis highlight that different ATO configurations have diverse advantages or limitations, depending on the type of railway governance and the technological development of the existing railway signaling and communication equipment. In addition, we also use the results to spotlight operational, technological, and business advantages/limitations of the proposed ATO-over-ETCS architecture that is being developed by the European Union Agency for Railways (ERA) and provide a scientific argumentation for it. Subject ATO-over-ETCSAutomatic Train OperationConnected Driver Advisory SystemSWOTTrain Path EnvelopeTrain trajectory generation To reference this document use: http://resolver.tudelft.nl/uuid:ae3a739d-fca4-4d2a-bc2c-1ca576fe5faa DOI https://doi.org/10.1016/j.jrtpm.2022.100352 ISSN 2210-9706 Source Journal of Rail Transport Planning & Management, 24 Part of collection Institutional Repository Document type journal article Rights © 2022 Z. Wang, E. Quaglietta, Maarten G.P. Bartholomeus, R.M.P. Goverde Files PDF 1_s2.0_S221097062200052X_main.pdf 1.13 MB Close viewer /islandora/object/uuid:ae3a739d-fca4-4d2a-bc2c-1ca576fe5faa/datastream/OBJ/view