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New RIDE dataset standardizes train delay prediction benchmark

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced RIDE, a new open dataset and benchmark designed to standardize train delay prediction. This nationwide dataset, covering the Belgian railway network from 2023 to 2025, includes 94.5 million train events and 35.7 million weather records. The RIDE framework provides a unified evaluation protocol to compare various predictive models, revealing that graph neural networks achieved the best performance among learning-based methods. AI

IMPACT Standardizes evaluation for train delay prediction models, enabling better comparison and development of AI solutions.

RANK_REASON The cluster describes a new academic paper introducing an open dataset and benchmark for a specific machine learning task.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New RIDE dataset standardizes train delay prediction benchmark

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Cl\'ement Elliker, Mathis Le Bail, Cl\'ement Mantoux, Jesse Read, Sonia Vanier ·

    RIDE: An Open Dataset and Benchmark for Train Delay Prediction

    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. …

  2. arXiv cs.LG TIER_1 English(EN) · Sonia Vanier ·

    RIDE: An Open Dataset and Benchmark for Train Delay Prediction

    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 …

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