Université Gustave Eiffel was founded in 2020. It is an institution that has brought together a university (UPEM), a research institute (IFSTTAR), a school of architecture (Éav&t) and three engineering schools (EIVP, ENSG and ESIEE Paris). Université Gustave Eiffel is active in many research areas. In particular, it accounts for a quarter of French research on the cities of tomorrow and brings together multidisciplinary capabilities to conduct quality research for the benefit of society, offer education tailored to the social and economic world and support public policies. The railway traffic management team of the COSYS-ESTAS and COSYS-LEOST laboratories of Université Gustave Eiffel is issued from IFSTTAR (French institute of science and technology for transport, development and networks). It has many years of experience in railway traffic management optimization. The team has worked in many regional, national and European projects collaborating with several railway undertakings and infrastructure managers on the automation and optimization of the European railway system.

Within SORTEDMOBILITY, Université Gustave Eiffel will be in charge of project coordination. As such it will play a predominant role in WP6 and WP7. Moreover, it will lead WP5 and have a major responsibility in the development of the algorithms for self-organizing railway operations in WP3. Université Gustave Eiffel will also contribute to the definition of KPIs and operational principles in WP1, the identification of demand modeling requirements in WP2  and the integration of software modules in WP4.

SNCF is the parent company of the SNCF Group, which includes SNCF Réseau (infrastructure manager), SNCF Voyageurs (mobility solutions), Gares & Connexions (stations and their services), Keolis (international operator for bus, metro transport systems), Fret SNCF (rail freight operator) and Geodis (international logistics). Among the services offered by the SNCF parent company, the SNCF Innovation & Research department leads innovation projects on its own initiative or others demanded by the group companies. It has a long experience of innovation and research projects in collaboration with industrial and academics partners in France and abroad, on various topics (AI, autonomous train, robotics, optimization for railway planning and real-time traffic management, new mobility systems, new materials, engineering of complex systems, etc.).

Within SORTEDMOBILTY, SNCF will be in charge of WP1. As railway operator, it will lead the definition of the KPIs as well as the operational principles to be set in WP2-5. Moreover, it will supply the data concerning a case study and participate to the definition of guidelines and recommendations in WP5.

Technical University of Denmark (DTU) is one of the leading engineering universities in Europe and an international elite technical universit. DTU is at the academic and multidisciplinary forefront of the technical and the natural sciences. The Division of Transport is part of DTU’s Department of Technology, Management and Economics and consists of researchers with academic backgrounds in mathematical modelling, computer science, economics, psychology, engineering and other specializations. The transportation research has a social purpose - and helps create tangible results. Within the Division, the Machine Learning for Smart Mobility group started in 2016, and builds on earlier long experience of the same team at MIT (as members of the ITSLab, its.mit.edu), in Boston and in Singapore, during about 10 prior years of intensive research in machine learning, transport engineering, behavior modeling, simulation, optimization, and data collection.

Within SORTEDMOBILITY, DTU will lead WP2, being the main contributor to the design and development of demand forecast models. It will actively support their integration into the self-organization algorithms and the simulator in WP3 and WP4. Finally, DTU will participate to the experimental analysis of WP5. This work will be carried out both directly by DTU and through the direction of a PhD thesis in collaboration with BDK.

BaneDanmark (BDK) is the Danish national rail Infrastructure Manager, branched off the Danish National Railways in 1997. It is responsible for operations, maintenance and development of the national rail infrastructure (F-bane, 3.100 km) and the Copenhagen suburban network (S-bane). BDK is carrying on several development programs to improve rail operations, including the electrification of the main lines, the upgrade of the signaling system, the speed-upgrade of several lines for international traffic and the construction of new connections. BDK is active in research and has a consolidated collaboration with universities concerning AI research.

Within SORTEDMOBILITY, BDK will participate in the definition and integration of operational principles and KPIs for self-organizing operations (WP1, WP3), and in the validation and impact assessment of the overall study. BDK will provide data for the Copenhagen suburban case study in WP5. Moreover, it will host a PhD study led by DTU within WP2 for  demand modelling and will lead the exploitation of the project results in WP6.

The National Research Council (CNR) is the greatest Italian research public body, founded on November 18 of 1923. In 2003, the CNR became a “national public organization committed to carry out, promote, spread, transfer and improve research activities in the main sectors of knowledge growth and of its applications for the scientific, technological, economic and social development of the Country”. The Institute of Cognitive Sciences and Technologies (ISTC-CNR) includes scientists involved in interdisciplinary research ranging from cognitive science and linguistics to AI and robotics. Within the institute, the Laboratory of Autonomous Robotics and Artificial Life (LARAL) is one of the founders of the Evolutionary Robotics field and carried out pioneering research in the area of evolution of communication and language, and in the study of collective behavior.

Within SORTEDMOBILITY, ISTC-CNR will design, develop and test the self-organization decentralized strategy to allow intelligent trains to reach a consensus on the services, routes and schedules that maximize efficiency and better respond to the current demand (WP3, WP4, WP5). The strategy will be able to take into account operational principles such as moving block and virtual coupling.

Rete Ferroviaria Italiana SpA (RFI) is the company of the Ferrovie dello Stato Italiane Group with the public role of railway infrastructure manager, responsible for management and safety of the rail traffic on the whole Italian network, tracks, stations and installations. In its role of infrastructure manager, RFI allows the access to the railway network to railway undertakings, performs the maintenance and ensures the safe circulation on the whole network, manages the investments for the upgrading of railway lines (High Speed and Conventional) and the technological development.

Within SORTEDMOBILITY, RFI will contribute to the definition phase (principles, KPIs and case studies in WP1 and WP5) as owner of the operative context and potential final user. RFI will also contribute to the impact assessment phase, making available relevant data for one of the case studies tackled and participating to the analysis of the results achieved and to their interpretation for the development of guidelines and recommendations (WP5).

Delft University of Technology (TU Delft) is the oldest and largest university of technology in the Netherlands, having been funded in 1842. In the field of transportation science & technology TU Delft ranks 1st in the Shanghai Global Ranking 2017. Within TU Delft, the Digital Rail Traffic Lab (DRTLab) features a group of researchers internationally renowned for cutting-edge scientific and technological innovations in the field of railway traffic management and signaling. The DRTLab has a strong track record in moving block and virtual coupling next-generation signaling systems, train simulation, algorithms for optimal rail traffic planning, management and control.

Within SORTEDMOBILITY, TU Delft will lead WP4 and expand their simulator EGTRAIN so to enable innovative self-organizing train operations. TU Delft will contribute to developing traffic management algorithms and demand models in WP3 and WP2, ensuring smooth integration with the simulation platform. Moreover, it will contribute to reviewing the state of the art and defining self-organizing railway operations in WP1, and it will play a major role in WP5 to set up a framework for a comprehensive evaluation of self-organizing railway operations and generate scenarios.