During the 1st Reporting Period, the NEXUS project achieved the expected results, advancing the development of adaptable, inclusive, and sustainable metro systems. Activities focused on collecting user requirements (from passengers and operators), modelling and simulation, AI model development, and specifying future metro control systems, laying the groundwork for validation and demonstration in the next stages.
WP3 – Requirements, framework and state of the art
WP3 was completed successfully, resulting in a detailed mapping of user, operator, and system requirements, providing the functional, operational, and regulatory baseline for adaptable metro design. Surveys and workshops with stakeholders identified needs related to inclusivity, security, and digitalisation, ensuring a strong user-centred foundation for subsequent WPs.
WP4 – Models supporting metro adaptability analysis
WP4 achieved key milestones in creating a simulation-based analytical framework for metro adaptability. A suite of models was developed to represent passenger flows, vehicle dynamics, and station operations, enabling systematic testing of adaptability strategies and configurations. Validated through initial test cases, the framework supports the KPI on adaptability enhancement and provides a basis for integrating AI-driven and control functionalities (WP5 and WP6). The work reached TRL 4, establishing a solid foundation for further validation in WP7.
WP5 – Future train control feasibility study
WP5 defined the functional architecture and performance requirements for next-generation metro control systems. Key outcomes include a framework for scalable and interoperable control systems supporting automation and real-time adaptability, identification of several control configurations for different automation and resilience levels, and an initial assessment of benefits related to energy efficiency, capacity, and safety. These outputs contribute directly to EU-RAIL objectives for digital and automated train operations.
WP6 – AI and data science implementation in metro operation
WP6 advanced the application of AI and data analytics to enhance metro performance and efficiency. Activities included mapping AI use cases (≥10 identified) and implementing initial demonstrators (4 developed) addressing predictive maintenance, crowd management, and operational optimisation. The AI demonstrators (D6.1 and D6.3) reached TRL 4 and will be validated through pilot scenarios in WP7. These results mark a key step toward achieving the KPI on AI integration in operations, fostering data-driven decision-making in metro management.
