Artificial Intelligence Across Rail Networks
THE RESOURCE LIBRARY
A curated guide to the ideas, projects and tools shaping intelligent railways. Find your starting point — and see why each resource is worth exploring.
Understand the opportunities and priorities for AI in rail.
An overview of analytical and generative AI across railway operations, maintenance and passenger services.
Useful for identifying business priorities and understanding what it takes to scale AI across an organisation.
mckinsey.com
UIC introduces its joint report with McKinsey on AI adoption and railway use cases.
Adds the railway association’s perspective to the same report, with context for sector-wide adoption.
uic.org
An introduction to EU-supported AI research, from predictive maintenance to network operations.
A helpful starting point for connecting AI concepts with named railway research projects.
rail-research.europa.eu
Explore research roadmaps and practical approaches to network management.
Roadmaps for AI integration in railway signalling, operational intelligence and network management.
Provides a structured research foundation for assessing where AI could contribute to railway systems.
rails-project.eu
The Capacity & Traffic Management System explores AI-assisted planning and control of railway traffic.
Shows how an infrastructure operator approaches capacity planning and disruption management in practice.
digitale-schiene-deutschland.de
Research into AI-supported operation of critical networks, including railway use cases.
Useful for exploring human–AI collaboration and comparing railway challenges with other network industries.
ai4realnet.eu
Find foundations for interoperable data and reproducible AI experiments.
An XML-based exchange format for railway infrastructure, timetables, rolling stock and signalling data.
Standardised data exchange supports integration between railway systems and the data pipelines used by AI.
railml.org
An open-source environment for studying railway rescheduling and multi-agent reinforcement learning.
Offers a practical entry point for testing algorithms on simulated railway coordination problems.
flatland-association.github.io
The original research paper introduces a dataset for semantic understanding of railway scenes.
Helps computer-vision researchers assess the dataset, its annotations and its suitability before building experiments.
openaccess.thecvf.com