ICONET will significantly extend state of the art research and development around the PI concept in pursuit of a new networked architecture for interconnected logistics hubs that combine with IoT capabilities and aiming towards commercial exploitation of results. ICONET strives to achieve the end commercial goal of allowing shipments to be routed towards final destinations automatically, by using collaborative decisions inspired by the information centric networking paradigm, and optimizing efficiency and customer service levels across the whole network. According to this vision, cargo regarded as smart physical packets will flow between hubs based on ‘content’ of the cargo influencing key commercial imperatives such as cost, optimisation, routing, efficiency and advancing EU's Green agenda. Consequently, the consortium are discernibly aimed at three (3) avenues of commercialisation and exploitation from the ICONET innovation, specifically targeted in the areas of (a) Warehousing as a service, (b) E-commerce fulfillment as a service, and (c) Synchromodality as a service. PI based logistic configurations will be simulated, prototyped and validated in the project . Modelling and analysis techniques will be combined with serious game type simulation, physical and digital prototyping, using living lab (LL) requirements scenarios and data. With analyses and simulations, optimal topologies and distribution policies for PI will be determined. The project implementation will be based on a succession of phases of modelling and design/prototyping, learning and experimentation and feedback and interaction with the wider business community, including the ALICE logistics platform as well as members of the partner Associations ESC, UIRR and ELUPEG. Through its Living Labs, the project will address under the PI paradigm both Supply Network Collaboration and Supply Network Coordination.

The vision of MODALSHIFT lies in the creation of a transport network and traffic management optimisation framework, trusted and valuable for local stakeholders, that bridges the data from infrastructures, logistics and mobility operators. New IoT devices - a smart box enabling Capacity-as-a-Service, and a e-subscription device for public transport access for vulnerable people, will increase the sources for data collection. A mobility data space, associated to novel geolocation data anonymisation, will be set up in the 3 Case Studies (Bulgaria, Italy, Spain) to ensure trusted and secure data exchange between data providers and users. This multisource data will enhance traffic state forecasting and increase the detection rate of events by 15%. On this basis, predictive and prescriptive analytics and synchromodality-based scenarios, tested in digital twins and early pilots, will identify optimal actions of transport stakeholders for adjusting their operations, towards a reduction of 25% of the interconnection or transshipment delays. Agent-based modelling will identify how a modal shift towards low-carbon, active and shared mobility services can be acceptable by end-users and support a reshape of the public transport services and the use of urban space. A multimodal traffic management platform will orchestrate, upon the data space, the cooperation of stakeholders at network and multimodal hub scales. It enables the connection of dynamic optimisation algorithms to operational drivers’ tool for mobility operators, and of static models to visual interface for transport planners. The determination of governance models, values for each stakeholder, dynamic pricing and business models, will steer the participation of 8 stakeholders for each Case Study in the multimodal traffic management system. With this approach, MODALSHIFT stimulates new uses of the transport network to reduce traffic congestion for low-carbon and inclusive mobility, avoiding pernicious rebound effects.

Gathering 31 partners from 10 different countries, DECARBOMILE aims to trigger an unprecedented improvement of the green last mile logistics in Europe. To reach that goal, DECARBOMILE relies on a strong experience of decarbonating urban logistics through European initiatives such as CIVITAS. Partners will build upon all previous results to develop improved delivery methods, tools and methodologies, and implement them across Europe. The solutions developed in DECARBOMILE will demonstrate the full potential of decarbonised last mile logistics in four living labs (in Logrono - Spain, Nantes -France, Hamburg - Germany and Istanbul - Turkey) and 4 satellites (Tallinn - Estonia, Getafe - Spain, Ghent - Belgium and Sarajevo - Bosnia and Herzegovina) will be involved at a smaller scale to test and study the solution in their own local contexts. To be successful in its implementation, DECARBOMILE will rely on developed methodologies to implement the new solutions and delivery methods in collaboration with all relevant local stakeholders, based on their needs and behaviours. The relation with and between stakeholders will be facilitated by the creation of a collaborative urban consolidation logistics framework that will include a digital platform, methodologies for collaboration, and ICT and IoT tools. This common framework, along with tailored innovative business models and recommendations on local policies, will allow for a strong collaboration during the project, allow to learn more about the end-users’ needs and behaviours. The delivery methods will be strongly improved with urban consolidation centres, micro urban consolidation centres including smart lockers, innovations on cargo bikes and how they can be used with load pooling for instance, electric barge and more. The goal is to use and improve existing solutions and allow their interoperability and modularity to improve their efficiency and use their complementarity.

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 conditi

Electric motors are one of the main components in industries, thus knowing their health state exploiting predictive maintenance programs is nowadays more than necessary in the vision of the Factory of the Future. The correct application of these programs will reduce the repairing costs and the unplanned downtime, will generate savings in employees’ time and will optimise the energy consumption. So far, only expensive and bulky monitoring equipments, targeted to costly electric motors (e.g., carbon mills on power plants), are available on the market. These systems provide raw data that can be only manipulated by specialists. For these reasons, large firms currently may only implement predictive maintenance programs on costly electric motors and in that case they must employ experts capable of understanding the data provided by these systems. However, the large part of active industrial motors is small-medium sized and, for these reasons, are unmonitored. These motors are usually a key asset in SMEs’ production process. In this proposal, we aim at the “predictive maintenance democratisation”, i.e. a shift of its benefits also to smaller size motors. This can be done with a low-cost and easy-to-use system based on Internet of Things technologies. Particularly, we aim at improving and evaluating the market potential of a system capable to process, automatically understand motors’ health states and warn the end user in a simple manner, thus providing its benefits to both large firms and SMEs.