The MOMIT project will develop innovative products and solutions supporting the maintenance process of railway infrastructures. MOMIT concept is based on the exploitation of unmanned technologies as Earth Observation satellites and RPAS-borne sensors. Starting from collected data analysis, MOMIT will bring at cutting edge level the remote sensing technology: developing advanced post processing chains, data fusion, automation, defining new indicators from estimated parameters, MOMIT will design new operational workflows able to support intelligent asset management. MOMIT will adopt a multi scale approach: Satellite and RPAS data will be combined in order to maximize their benefits and characteristics. A first overall analysis (with satellite and over long sections) will guide the detailed analysis and trigger specific preventive actions. Thus, maintenance activities are guided by this combined analysis with a general optimization of resources. Effectiveness and efficiency of proposed solutions will be demonstrated by six main application cases, validated in a real operational environment: - Ground movements: interferometry derived by SAR satellite data analysis will adopt to define tools and indicators supporting the user for detailed analysis and preventive actions planning - Hydraulic activities: a combination of optical and radar satellite data will be used to monitor soil moisture and water bodies close to the track - Natural hazards: anomalies along the track related to natural phenomena (as vegetation growth) will be monitored by the use of satellite data. - Electrical system: RPASs will be equipped with innovative sensors to monitor electrical effects impacting on the infrastructure efficiency - Civil engineering structures: a combination of satellite and RPAS data will be used to identify possible criticalities to the infrastructure - Safety: anomalies and illicit activities along the track will be monitor by the use of optical a radar satellite data

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.

VICE4RAIL aims to accelerate the adoption of EGNSS for deploying efficient, resilient, and competitive ERTMS solutions. Focus is on the development of a certification and standardization process for the use of EGNSS, closing the gaps for rail safety critical applications and converging towards a pan-European EGNSS-based solution for ERTMS and its global roll-out. The starting point is the use of EGNOS V2 and the Balise virtualisation – as indicated by the European Parliament directive on July 2021 – opening the way for further applications based on the absolute train positioning and EGNOS V3. Two major developments will be undertaken: identification of suitable certification procedures compliant with the CENELEC norms and realization of a Hybrid Virtualized Testing Certification Environment based on the zero-on-site testing paradigm with the construction of a dedicated testing facility on a RFI’s railway lines where GNSS-based multisensor positioning solutions can be evaluated and certified in operational scenarios. This unique testing environment will be connected with the ERTMS accredited laboratory of CEDEX to assess the end-to-end performance of the ERTMS chain with GNSS-based positioning devices in operational scenarios. Behaviour of the system under GNSS signals that include faults very rare and difficult to experience field but with a potential high impact on safety, can be evaluated avoiding extensive and not exhaustive field tests. VICE4RAIL will exploit the synergies with the Pilot Line Novara – Rho funded by RFI to integrate GNSS positioning into the ERTMS and the Shift2Rail GATE4Rail project that introduced the zero-on-field virtual testing for GNSS. Furthermore, thanks to SNCF, CEDEX, RFI and SOGEI a liaison will be established with the Europe’s Rail R2DATO project to complement their on-going activities and sharing results and assets and with the RTCM SC 134 Special Committee that is completing the standardization process for GNSS receivers for rail.

The overall DAYDREAMS objective is to move forward the integration and use of data and artificial/human trustworthy intelligence together with context-driven HMI for prescriptive Intelligent Asset Management Systems (IAMS) in railway by (i) advancing in maintenance approach towards prescriptive asset management, (ii) improving the decision-making process by developing multi-objective decision optimisation approaches taking into account all implications of IAMS decisions in the railway environment, and (iii) reinforcing the role of the person-in-the-loop by designing and developing advanced context-driven HMIs to allow context- and risk-aware multiple-options decision-making processes. The HMI will allow the person-in-the-loop to: (i) properly access and visualise predictions/metrics and models, (ii) assess why and how the model predicts something, (iii) Steer models by setting parameters, and (iv) evaluate alternatives using parameter steering and extending this process through speculative execution. The DAYDREAMS objective will be assessed by validating the proposed solution using the following two-step approach: (i) A first validation at TRL 4 of the approaches (developed prescriptive asset management) using several internal scenarios and one scenario provided by the CFM IN2SMART2 project; (ii) A further validation of the DAYDREAMS methodologies integrated in a TRL 5 prototype using at least two scenarios: at least one internal scenario and the IN2SMART2 scenario. The validation will cover both the performances of the prototypes and its trust for future adoption in multi-actors environments. The validation will be carried out by defining evaluation and validation metrics and KPIs: (i) linked to asset management problems to be solved by the involved Infrastructure Managers (IMs) and related baselines, (ii) quantified and measurable; (iii) referred to high-level KPIs defined in the S2R IMPACT2 CFM project, and (iv) useful to address multi-objective optimisation.

Railway stations hold a unique position in the urban landscape: they not only act as complex nodes of mobility and transport, but also as public places that can be seen as integral elements of the city. Consequently, stations have a decisive impact on their urban surroundings as places of everyday life, affecting all stakeholders including citizens and the environment. The central ambition of RAIL4CITIES is to develop a new operational, readily available and highly applicable model of stations (SCP model), combined with a common European methodology and tool for its effective implementation. The project takes inter-dependent impediments (profit-orientated business model, complex web of agents and stakeholders, policy gaps) into account and provides decision makers with the tools to transform stations into promoters of sustainable cities. The model will be applied to 5 living labs addressing the stations’ transformation into hubs of green and active mobility (FR), energy hubs (IT), towards Transit Oriented Development (DE), into a socially-inclusive services hub with using Nature Based Solutions (PL) and services hubs enabling the 15-minute city and circular economy (BE). This will be enhanced by 3 case-studies from the high-speed rail line from Lisbon to Oporto (PT) to study the resilience of infrastructures for both adaptation of spaces to new future uses, and adaptation to climate-change issues and health crises. With 14 partners from 7 European countries, further supported by 9 institutions through letters of support, the RAIL4CITIES consortium and ecosystem integrates the relevant stakeholders from universities, industry, government and the public, and is therefore in the position to design, evaluate and publish a EU-wide model for transforming existing stations or designing new ones into socio-technical systems operating as city’s greening engines for the surrounding environment, and new urban hubs aggregating multiple services for the users and its citizens