The objective of SORTEDMOBILITY is to develop and assess concepts, models and algorithms to enable self-organizing railway operations, whereby intelligent trains autonomously participate in the traffic management. The aim is to improve flexibility, capacity and resilience of the railway system as a mobility backbone, to accomplish an efficient and demand-aware urban and interurban rail mobility growth. The assessment will be performed thanks to a holistic integrated approach. Traffic simulation based on real data will provide the experimental proof-of-concept validating the achievement of TRL3. This objective addresses three major evolution challenges emerging in the urban and interurban public transport system: i) guaranteeing a high level of service (e.g., frequent, reliable, demand-responsive, resilient) in larger and larger networks, ii) ensuring overall accessibility gains for the heterogeneous population of users in their daily travel patterns within a multi-modal environment; iii) achieving efficiency and fairness in a system involving multiple actors operating in a competitive market. These challenges are becoming more and more critical in the current context of urban development. Indeed, the on-going mobility revolution encompasses the appearance of new operational settings, with personal and flexible new transport modes bringing higher connectivity and evolving dynamics to travellers' decisions. This has so far mostly resulted in a non-ecological and non-efficient increase of car traffic flows in the city. Today, public transport networks, and railways in particular, are managed in a centralized way. The traditional decision-making process can hardly cope with the three pointed-out evolution challenges. Intuitively, instead, a self-organizing approach could be able to do so. First, it could efficiently scale up to large networks. Second, it could satisfy the need for transport customization: it could leave aside the very concept of rigid timetabling and exploit the flexibility of self-organization to respond to multi-modality needs in terms of synchronization, accessibility discrepancies across heterogeneous travelers, or modal substitution in case of service performance changes due, e.g., to disruptions. Third, it could simplify and encourage cooperation and local competition in a dynamic context. Inspired from natural systems, as bird flocks or ant colonies, the innovation that SORTEDMOBILITY will propose is a self-organization approach that relies on the ability of multiple intelligent agents—i.e., trains—to decide their route and schedule based on local knowledge of demand and network conditions, and to interact with neighbor agents to negotiate and find a consensus on the best shared solution. The expected results of SORTEDMOBILITY will be the proposal and assessment of a holistic integrated approach. It will join novel algorithms for self-organizing operations based on new operational principles, innovative models to capture passenger demand evolution and enhanced microscopic mobility simulation. These algorithms and models will exploit state-of-the-art Artificial Intelligence techniques, in particular in the fields of swarm intelligence and machine learning. SORTEDMOBILITY will also produce ad hoc KPIs suitable to assess such a holistic approach. The analysis of the simulated traffic evolution and its impact on passengers will result in a set of guidelines and recommendations for infrastructure managers, system manufacturers and regulatory bodies to support future system specifications. The analysis and assessment will be carried out on three case studies selected and supplied by European railway infrastructure managers to cover a large spectrum of urban public transport configurations, in Denmark, Italy and France.