EMERALDSs vision is to design, develop and create an urban data-oriented Mobility Analytics as a Service (MAaaS) toolset, consisting of the so-called emeralds services, compiled in a proof-of-concept prototype, capable of exploiting the untapped potential of extreme urban mobility data. The toolset will enable the stakeholders of the urban mobility ecosystem to collect and manage ubiquitous spatio-temporal data of high-volume, high-velocity and of high-variety, analyse them both in online and offline settings, import them to real-time responsive AI/ML algorithms and visualize results in interactive dashboards, whilst implementing privacy preservation techniques at all data modalities and at all levels of its architecture. The toolset will offer advanced capabilities in data mining (searching and processing) of large amounts and varieties of urban mobility data and its efficiency will be assessed, validated and demonstrated in three TRL5 pilot use cases (by following a co-development approach with mobility and city stakeholders to improve decision making in urban smart city environments), and deployed/showcased in two early adopters data-driven TRL6 applications (by integrating the new services to existing systems to improve commercial offerings).

IW-NET will deliver a multimodal optimisation process across the EU Transport System, increasing the modal share of IWT and supporting the EC’s ambitions to reduce transport GHG emissions by two thirds by 2050. Enablers for sustainable infrastructure management and innovative vessels will support an efficient and competitive IWT sector addressing infrastructure bottlenecks, insufficient IT integration along the chain and slow adoption of technologies such as new vessel types, alternative fuels, automation, IoT, machine learning. The Living Lab will apply user-centered application scenarios in important TEN-T corridors demonstrating and evaluating the impacts in simulations and tests covering technological, organisational, legal, economical, ecological, and safety/security issues: 1) Digitalisation: optimised planning of barge operations serving dense urban areas with predictive demand routing (Brussels-Antwerp-Courtrai-Lille-Valenciennes); data driven optimisation on navigability in uncertain water conditions (Danube). 2) Sustainable Infrastructure and Intelligent Traffic Management: lock forecasting reducing uncertainty in voyage planning; lock planning; management of fairway sections where encounters are prohibited; berth planning with mandatory shore power supply and other services (hinterland of Bremerhaven via Weser/Mittelland Canal). 3) Innovative vessels: new barge designs fitting corridor conditions and target markets: barges with a high degree of automation for urban distribution (East Flanders-Ghent); new barge for push boats capable with low/high water levels optimising capacities (Danube from Austria to Romania); use of GALILEO services for advanced driver assistance like guidance, bridge height warning and automatic lock entering (Spree-Oder waterway close to Berlin). Accompanying activities are stakeholder engagement, capacity building, and the delivery of a European IWT development roadmap with policy recommendations for increasing the IWT share.

PLANET addresses the challenges of assessing the impact of emerging global trade corridors on the TEN-T network and ensuring effective integration of the European to the Global Network by focusing in two key R&D pillars: • A Geo-economics approach, modelling and specifying the dynamics of new trade routes and its impacts on logistics infrastructure & operations, with specific reference to TEN-T, including peripheral regions and landlocked developing countries; • An EU-Global network enablement through disruptive concepts and technologies (IoT, Blockchain and PI, 5G, 3D printing, autonomous vehicles /automation, hyperloop) which can shape its future and address its shortcomings, aligned to the DTLF concept of a federated network of T&L platforms. PLANET goes beyond strategic transport studies, and ICT for transport research, by rigorously modelling, analysing, demonstrating & assessing their interactions and dynamics thus, providing a more realistic view of the emerging T&L environment. The project employs 3 EU-global real-world corridor Living Labs including sea and rail for intercontinental connection and provides the experimentation environment for designing and exploiting future PI-oriented Integrated Green EU-Global T&L Networks [EGTN]. To facilitate this process, PLANET delivers a Symbiotic Digital Clone for EGTNs, as an open collaborative planning tool for TEN-T Corridor participants, infrastructure planners, and industry/technology strategists. PLANET also delivers an Active Blueprint and Road Map, providing guidance and building public & private actor capacity towards the realisation of EGTNs, and facilitating the development of disadvantaged regions. The project engages major T&L stakeholders, contributing to both strategy and technology and (importantly) has the industry weight and influence to create industry momentum in Federated Logistics and TEN-T’s integration into the Global Network.

GREEN.DAT.AI aims to channel the potential of AI towards the goals of the European Green Deal, by developing novel Energy-Efficient Large-Scale Data Analytics Services, ready-to-use in industrial AI-based systems, while reducing the environmental impact of data management processes. GREEN.DAT.AI will demonstrate the efficiencies of the new analytics services in four industries (Smart Energy, Smart Agriculture/Agri-food, Smart Mobility, Smart Banking) and six different application scenarios, leveraging the use of European Data Spaces. The ambition is to exploit mature (TRL5 or higher) solutions already developed in recent H2020 projects and deliver an efficient, massively distributed, open-source, green, AI/FL - ready platform, and a validated go-to-market TRL7/8 Toolbox for AI-ready Data Spaces. The services will cover AI-enabled data enrichment, Incentive mechanisms for Data Sharing, Synthetic Data Generation, Large-scale learning at the Edge/Fog, Federated & Auto ML at the edge/fog, Explainable AI/Feature Learning with Privacy Preservation, Federated & Automatic Transfer Learning, Adaptive FL for Digital Twin Applications, Automated IoT event-based change detection/forecasting. The GREEN.DAT.AI Consortium consists of a multidisciplinary group of 17 partners from 10 different countries (and one associated party), well balanced in terms of expertise. The vast majority of partners already have key roles in a number of projects funded under the Big Data PPP (ICT-16-2017) topic, namely BigDataStack, CLASS, Track & Know, and I-BiDaaS and are serving as active members of the BDVA/DAIRO Association, FIWARE, AIOTI, and ETSI. In addition, partners come from a variety of sectors, such as banking, mobility, energy, and agriculture, constituting a representative workforce of their respective domains, which will contribute to industry adoption and stimulate uptake in other sectors as well.

DT4GS is aimed at delivering an “Open Digital Twin Framework” for both shipping companies and the broader waterborne industry actors to tap into new opportunities made available through the use of Digital Twins(DTs). The project will enable shipping stakeholders to embrace the full spectrum of DT innovations to support smart green shipping in the upgrade of existing ships and new vessels. DT4GS will cover the full ship lifecycle by embracing federation of DT applications as well as utilising DTLF policies and related shared-dataspace developments for the sector. DT4GS applications will focus on shipping companies but will also provide decarbonisation decision-support system for shipyards, equipment manufacturers, port authorities and operators, river commissions, classification societies, energy companies and transport/corridor infrastructure companies. DT4GS’s objectives are to: 1. Support shipping companies in achieving up to 20% reduction in CO2e with a 2026 horizon, by developing and deploying real-time configurable DTs for ship and fleet operational performance optimisation in 4 Living Labs involving shipping companies, with different vessel types, and establishing fully validated industry services for Green Shipping Operational Optimisation DTs expected to be adopted by 1000+ ships by 2030. 2. Establish a comprehensive zero-emission shipping methodology and support Virtual Testbed and Decision Support Systems that address both new builds and retrofits comprising: a. A DT4GS (Green Shipping) Dataspace for the broader shipping sector contributing to GAIA-X by establishing a core European industry resource that accelerates the green and digital transition of waterborne shipping and transport value chains. b. Simulation based solutions to retrofit ships, targeting 55% reduced CO2e reduction by 2030. c. A smart green “new-build” reference design per vessel type. d. Virtual Testbed services for reducing the cost of physical testing of GS solutions by 20%.