The success of the European rail system to foster the modal shift towards rail requires cost-efficient and reliable long-lasting trains. GEARBODIES contributes to this effort by improving the efficiency of rolling stock maintenance in close collaboration with the ongoing CFM-IP1-01-2019 (PIVOT2). To achieve the above, GEARBODIES follows a twofold approach: extending overhaul periods and improving maintenance processes. The extension of overhaul periods will be facilitated by developing high-performance and long-lifetime components for running gear. The improvement of maintenance processes will be boosted by developing innovative NDT technologies to optimise inspection processes for lightweight carbody shells. GEARBODIES will design and prototype several elastomer-metal running gear components, suitable for serial production, based on high-performance new elastomer formulations and existing elastomers not yet applied in rolling stock elements. In addition, the project will also explore innovative technologies for the development of low LCC bearings. New lubrication solutions, new materials for races and rollers, novel polymers for cages and the effects of new bearing geometries will be researched, among which the most feasible ones will be integrated in a new bearing design and prototyped. GEARBODIES will develop an innovative modular platform to reduce the inspection time of lightweight carbody shells. The platform will incorporate tailored thermography and ultrasonic inspection systems and will facilitate the automated detection and assessment of defects throughout the thickness of the shell by using a customed software module. GEARBODIES will benefit form a strong multidisciplinary consortium, made of 13 partners from 8 countries, committed to the mentioned actions towards maximisation of the project's impact.

The overall objective of MobiDataLab is to propose to the mobility stakeholders (transport organising authorities, operators, industry, innovators) a replicable methodology and sustainable tools that foster the development of a data sharing culture in Europe and beyond. MobiDataLab is based on a continuous co-development of knowledge and of technical solutions for data sharing with high involvement of all data producers and consumers in the transport and mobility landscape. This will be put in action through problem-solving oriented Labs, the collection and analysis of advice and recommendations of experts and supporting cities/regions/clusters/associations aided by the incremental construction of a cross-thematic knowledge base and of a cloud-based service platform, which will federate access and usage of data sharing resources. MobiDataLab leverages on the legal, technological and economic opportunities to: - present mobility data providers with recommendations on how to improve the quality, accessibility and usability of their data - how to describe them, how to store them safely and securely, how to make them available, easy to find, and understandable; - encourage the reuse of these data and foster users’ trust in the data, contributing to the development and promotion of open tools to the community of innovators; - bring together mobility stakeholders (both data providers and data consumers) to find innovative solutions to concrete problems, using open data as a tool. The MobiDataLab consortium is formed by 10 partners from Industry, Research, Academia, Consultancy and Governance sectors, located in 7 countries. The consortium shares a common view on the values and benefits of open data and open source principles for fostering independence and uptake by communities. The MobiDataLab yet young-and-emerging group of supporting parties allows to consider replicability of approach and results in many more countries, including beyond EU.

The abundance of tracking sensors in recent years has led to the generation of high-frequency and high-volume streams of data, including vessels, vehicles' tracking data, smartwatches, cameras, and earth observation sensors. However, there are cases where the trajectory of a moving object has gaps, errors, or is unavailable. However, a vast pool of tracking data is available but remains unexplored or underutilized and has the potential to reveal important information. The MUlti-Sensor Inferred Trajectories (MUSIT) project aims at exploring and fusing data from all heterogeneous sources to provide detailed information about a moving object’s whereabouts and behavior, reduce gaps, and produce a refined and inferred trajectory with minimal errors. The fusion of multi-sensor data is required to fill in the trajectory gaps of moving objects and attach useful semantics to the trajectory and its components. AI algorithms and spatio-temporal methodologies that can fuse information and infer the “missing knowledge” are crucial to the implementation of MUSIT. Furthermore, different representation models from multiple domains within the ICT sector will also be explored. Datasets will be made available in cases where it was previously thought impossible, and infer knowledge thus improving the overall surveillance. Therefore, the MUSIT project will tackle the aforementioned issues in a process that can be categorized into three parts: i) data collection and creation, ii) exploitation and utilization of cross-domain representation models within the ICT sector for trajectories, and iii) analysis and processing of outcomes to produce information-rich results related to vessel monitoring and urban mobility.

Lack of orchestration, structured and standardized integration protocols and metadata descriptors, incorporation of real-world traffic complexities and nuances, underutilization of valuable resources, model uncertainties and integration of micro-mobility services and VRUs result in suboptimal performance in addressing complex issues related to the management of mobility services and infrastructure and a divergence from EU’s sustainable mobility targets. FEDORA aims to pave the way towards advanced traffic and network management through the development of a federated spaces platform offering a holistic framework of innovative solutions and services that enable precise and pro-acting sensing of supply and demand, facilitate optimal operation of transport services, and advances learning and evolution in complex environments. At the operational level, FEDORA offers a collaborative space of data that can realize advanced data alchemy processes using interconnected services and tools, a space of advanced traffic management optimisation services and a multi-modal simulation space to create and assess future mobility scenarios. The approach is validated in six thematic demonstrations in Vienna, the Basque country,Reggio Emilia, Nicosia, Budapest and Denmark, covering varying EU urban and rural contexts, infrastructure maturity levels, multimodal mobility services availability, organisation/operational structures and social conditions. Interaction with existing programmes on roadmapping and recommendations at national, EU and global level will be promoted, allowing a multiplication effect of project’s results.

Considering Artificial Intelligence (AI) capabilities and potential risks, and taking into account its limitations, AI4CCAM will develop an open environment for integrating trustworthy-by-design AI models of vulnerable road user behaviour anticipation in urban traffic conditions, and accounting for improved road safety and user acceptance. Leveraging the Trustworthy AI guidelines for general intelligent software systems and the ethics recommendations for connected automated vehicles, AI4CCAM will support AI-based scenarios management in which pedestrian/cyclist behaviour anticipation models will integrate visual gaze estimation and where explainable ego car trajectory prediction models are simulated with ethical dilemmas and multiplied with generative adversarial networks and metamorphic testing techniques. The AI4CCAM open environment will include an interoperable digital framework for managing and generating AI-based urban-traffic scenarios in which trustworthy-by-design AI models can be tested and an online participatory space to foster acceptance of AI in automated driving, determine AI risks and identify biases in datasets and cyber-threats. Simulation scenarios of road users interacting with automated vehicles will be developed and evaluated in three complementary use cases covering the whole sense-plan-act paradigm and user acceptance. As such, the project will advance knowledge in building trustworthy-by-design AI-based solutions for CCAM applications.