arXiv:2606.05070v1 Announce Type: new Abstract: Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols. To address this gap, we introduce RIDE, an open dataset and benchmark for train delay prediction built at nationwide scale over the Belgian railway network. RIDE covers 94.5M train events, 3.6M journeys, and 35.7M weather records from 2023 to 2025. It is organized as a layered data pipeline from raw railway and weather
The proliferation of AI and big data analytics necessitates robust real-world datasets for practical application and benchmarking in critical infrastructure sectors.
A standardized, large-scale dataset for train delay prediction can significantly advance research and operational efficiency in intelligent transportation systems, impacting logistics and public services.
The availability of RIDE enables more consistent and comparable research in train delay prediction, potentially leading to more accurate models and improved railway operations globally.
- · AI researchers
- · Railway operators
- · Logistics companies
- · Commuters
- · Inefficient predictive modeling techniques
Improved train schedule adherence and reduced operational costs for railway networks.
Increased adoption of AI and machine learning in transportation management beyond rail.
Enhanced overall public trust in complex AI-driven infrastructure systems.
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Read at arXiv cs.LG