Periodic Reporting for period 1 - NEXUS (Next-gen technologies for enhanced metro operations)
Período documentado: 2024-10-01 hasta 2025-09-30
Urbanisation, sustainability, and digitalisation are reshaping public transport worldwide. Metro networks, as the backbone of urban mobility, face pressure to meet rising demand, remain resilient to disruptions, and transition towards greener, more efficient operations. Operators must integrate technologies (automation, AI, advanced control systems) into existing infrastructures while maintaining safety and service quality.
The NEXUS project addresses these challenges by developing a holistic framework for next-generation metro systems that are adaptive, resilient, inclusive, and sustainable. It supports operators and manufacturers in adopting data-driven, human-centred solutions that enhance performance and deliver social, environmental, and economic value. Strategically, NEXUS contributes to EU-RAIL goals by promoting digital transformation and decarbonisation of urban rail mobility.
Key objectives:
- Enhance adaptability and resilience through simulation-based optimisation.
- Improve inclusivity and accessibility of metro vehicles and stations.
- Strengthen safety and cybersecurity using AI-assisted approaches.
- Advance automation and control systems ensuring interoperability and scalability.
- Leverage AI to optimise efficiency, maintenance, and service quality.
- Support European industrial competitiveness through validated frameworks and guidelines.
- Foster stakeholder engagement and social inclusion to align technology with societal needs.
Impact pathway:
- Scientific: new methodologies in simulation, AI, and system modelling, reinforcing Europe’s research base in intelligent transport.
- Technological: validated tools and demonstrators for AI-driven control and optimisation, accelerating deployment.
- Industrial: open, interoperable solutions strengthening European competitiveness.
- Societal: inclusivity, accessibility, and safety to enhance user experience and mobility quality.
- Environmental: energy-efficient designs and operations supporting climate-neutral transport goals.
The NEXUS project addresses these challenges by developing a holistic framework for next-generation metro systems that are adaptive, resilient, inclusive, and sustainable. It supports operators and manufacturers in adopting data-driven, human-centred solutions that enhance performance and deliver social, environmental, and economic value. Strategically, NEXUS contributes to EU-RAIL goals by promoting digital transformation and decarbonisation of urban rail mobility.
Key objectives:
- Enhance adaptability and resilience through simulation-based optimisation.
- Improve inclusivity and accessibility of metro vehicles and stations.
- Strengthen safety and cybersecurity using AI-assisted approaches.
- Advance automation and control systems ensuring interoperability and scalability.
- Leverage AI to optimise efficiency, maintenance, and service quality.
- Support European industrial competitiveness through validated frameworks and guidelines.
- Foster stakeholder engagement and social inclusion to align technology with societal needs.
Impact pathway:
- Scientific: new methodologies in simulation, AI, and system modelling, reinforcing Europe’s research base in intelligent transport.
- Technological: validated tools and demonstrators for AI-driven control and optimisation, accelerating deployment.
- Industrial: open, interoperable solutions strengthening European competitiveness.
- Societal: inclusivity, accessibility, and safety to enhance user experience and mobility quality.
- Environmental: energy-efficient designs and operations supporting climate-neutral transport goals.
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.
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.
NEXUS has delivered significant results advancing the state of the art in metro adaptability, AI integration, and control system design. It introduces a novel framework combining modelling, simulation, and AI for metro system optimisation. The outcomes so far lay the groundwork for measurable impacts on performance, sustainability, and passenger experience.
1. Integrated Framework for Metro Adaptability Analysis
Current metro planning relies on isolated models for infrastructure, vehicles, and passenger flows. NEXUS overcomes this by developing an integrated, multi-layer simulation environment (WP4) unifying:
. Passenger behaviour and flow dynamics.
- Vehicle operation and capacity management.
- Station layout and crowd movement.
- Operational scenarios for fluctuating demand and disruptions.
2. AI-Driven Decision Support in Metro Operations
- AI use in urban rail remains limited. NEXUS defines and demonstrates key AI use cases (WP6), including:
- Crowding prediction using external data.
- Timetable creation via GTFS feeds.
- Passenger demand forecasting.
- Anomaly detection in operations.
3. Next-Generation Control System Architectures
In WP5, NEXUS proposes innovative control architectures aligned with EU-RAIL’s Digital and Automated Train Operations (DATO), defining:
- Requirements for adaptable, resilient, interoperable systems.
- Scalable automation concepts for future TMS.
- Interfaces linking control systems and AI modules.
4. Human-Centred and Inclusive Design
NEXUS integrates accessibility and inclusivity into technical modelling (WP3, WP4), linking human factors with system adaptability and surpassing static approaches to accessibility.
5. Standardisation, Open Science and Reproducibility
Reproducibility is ensured through:
- Public deliverables (D4.2 D5.1 D6.1 D6.3).
- Open-access publications and dissemination at conferences and workshops.
1. Integrated Framework for Metro Adaptability Analysis
Current metro planning relies on isolated models for infrastructure, vehicles, and passenger flows. NEXUS overcomes this by developing an integrated, multi-layer simulation environment (WP4) unifying:
. Passenger behaviour and flow dynamics.
- Vehicle operation and capacity management.
- Station layout and crowd movement.
- Operational scenarios for fluctuating demand and disruptions.
2. AI-Driven Decision Support in Metro Operations
- AI use in urban rail remains limited. NEXUS defines and demonstrates key AI use cases (WP6), including:
- Crowding prediction using external data.
- Timetable creation via GTFS feeds.
- Passenger demand forecasting.
- Anomaly detection in operations.
3. Next-Generation Control System Architectures
In WP5, NEXUS proposes innovative control architectures aligned with EU-RAIL’s Digital and Automated Train Operations (DATO), defining:
- Requirements for adaptable, resilient, interoperable systems.
- Scalable automation concepts for future TMS.
- Interfaces linking control systems and AI modules.
4. Human-Centred and Inclusive Design
NEXUS integrates accessibility and inclusivity into technical modelling (WP3, WP4), linking human factors with system adaptability and surpassing static approaches to accessibility.
5. Standardisation, Open Science and Reproducibility
Reproducibility is ensured through:
- Public deliverables (D4.2 D5.1 D6.1 D6.3).
- Open-access publications and dissemination at conferences and workshops.