ABOUT
24 months
13 partners
2 Metro networks in European cities
The NEXUS project aims to transform metro systems by developing flexible, demand-responsive capacity, optimizing train operations, and enhancing sustainability. This multi-method simulation model addresses three key aspects: integrating dynamic passenger profiles at stations, implementing metro vehicle concepts that optimize passenger flow while prioritizing accessibility and inclusiveness, and enhancing network efficiency through dynamically optimized train movements. Additionally, NEXUS will improve train control systems to reduce costs and support more responsive operations, applying sustainable technologies to manage the total cost of ownership and reduce the energy footprint. Cybersecurity considerations will also be prioritized to address potential risks associated with new data sources and analytical methods.
The Challenge & the Approach
Metro systems are essential to urban mobility, but they face critical challenges such as aging infrastructure, outdated control systems, and difficulty adapting to changing passenger demand.
The NEXUS project seeks to transform European metro transportation by addressing these real-world challenges. With the goal of extending beyond the state-of-the-art and pioneering transformative innovations in metro systems, we are introducing the following innovative approaches across various dimensions:
- Paradigm Shift in Adaptability: NEXUS redefines metro operations by introducing a paradigm shift in adaptability. The project introduces comprehensive solutions to manage fluctuations in demand, moving away from traditionally planned frameworks and enabling dynamic responses to real-time passenger needs.
- Advancing Full Automation in Train Control Systems: NEXUS is developing fully automated systems to control metro trains, utilizing technologies like CBTC (Communication-Based Train Control) and ATO (Automatic Train Operation). CBTC allows real-time communication between trains and control centres, ensuring precise tracking and management. ATO enables trains to operate automatically, handling tasks like speed regulation and station stops without human intervention. These advancements aim to enhance safety, improve operational efficiency, and provide a smoother experience for passengers in urban rail systems
- Integrating AI and Data Science: NEXUS integrates AI and data science at the core of metro operations, exploring applications such as predictive maintenance, energy optimization, and cybersecurity. The goal is to develop cost-effective strategies that enhance efficiency while maintaining financial sustainability.
- User-Centric Optimization: Going beyond traditional operational metrics, NEXUS places a strong emphasis on the user experience within metro stations. By analysing passenger behaviour, preferences, and journey conditions, the project aims to optimize services to meet the evolving needs of diverse demographics.
- Comprehensive Technology Impact Analysis: NEXUS takes a holistic approach, conducting thorough analyses of the social, environmental, and technological impacts of new implementations. This responsible and sustainable approach sets a new benchmark for technology integration in metro systems.
- Alignment with EU-Rail System Pillar: NEXUS aligns with the System Pillar of the EU-Rail Joint Undertaking, contributing to the development of a common architecture for the railway system. This focus on interoperability and open interfaces supports a more unified and efficient European rail network.
- Energy Optimization and Predictive Maintenance: Through collaborative workstreams, NEXUS emphasizes predictive maintenance and dynamic energy optimization. The ambition is to establish protocols that maximize operational efficiency while minimizing environmental impact.
Objectives
- Enhance metro adaptability and explore the potential of full automation employing a detailed methodical approach
- Redefine the metro experience by improving efficiency while prioritising accessibility and inclusivity within train cars
- Develop dynamic passenger profiles that reflect the diverse demographics and travel purposes within the metro system
- Produce comprehensive guidelines tailored to support transport operators in optimising their metro systems
- Contribute valuable insights that will inform the definition of future needs and requirements across diverse metro systems
- Adopt AI solutions to support operational processes within metro systems
- Strengthen metro system security and resilience against threats
- Craft a sustainable transport system that transcends conventional paradigms
- Deploy a sound strategy towards the exploitation and uptake of NEXUS results
Proof of concept and key technologies
The following key technologies are central to NEXUS’s technical pursuits:
Metro network simulation model
This includes assessing the adaptability of dynamic passenger profiles in various operational scenarios, including peak and off-peak periods, emergency conditions, and unforeseen disruptions, as well as analysing the intricate movement of metro trains within the network. By accurately simulating real-world conditions, the project will gain valuable insights into the strengths and weaknesses of the existing metro system, providing a detailed assessment of service levels, passenger flow, accessibility, and overall user experience.
DataContextHub & ML factory
A central data management platform that integrates diverse data sources, ensuring consistent data quality, and seamless data sharing. This tool is crucial for addressing the challenges of machine learning, where high-quality, varied, and representative data is essential for model performance The DataContextHub supports essential machine learning needs, including data accessibility, integration, and interpretability, while also ensuring security and ethical integrity. This integrated approach helps NEXUS develop machine learning models that are not only technically robust but also aligned with real-world applications.
Metro network optimization model
A simulation tool will focus on optimizing metro services across various operational scenarios, from standard conditions to disruptions. The tool will support network-level optimization by providing cost-saving and service-improving recommendations for operators and enhancing the travel experience for passengers.
Public Transport Analyzer
Using data science and machine learning, an advanced and interactive web dashboard skeleton will explore and analyse shared public transport data to be used in future control systems, to optimize public transport performance.
AITravel plug-in that offers eXplainable AI, surrogate models and active learning models for analysis of correlations between variables
A simulation tool will focus on optimizing metro services across various operational scenarios, from standard conditions to disruptions. The tool will support network-level optimization by providing cost-saving and service-improving recommendations for operators and enhancing the travel experience for passengers.
Sustainability TCO Model
Using data science and machine learning, an advanced and interactive web dashboard skeleton will explore and analyse shared public transport data to be used in future control systems, to optimize public transport performance.
Consortium
