Predicting Delayed Trajectories Using Network Features: A Study on the Dutch Railway Network

• URL: http://arxiv.org/abs/2507.11776v1
• Date: Tue, 15 Jul 2025 22:30:36 GMT
• Title: Predicting Delayed Trajectories Using Network Features: A Study on the Dutch Railway Network
• Authors: Merel Kampere, Ali Mohammed Mansoor Alsahag,
• Abstract summary: This research addresses a gap in delay prediction studies within the Dutch railway network by employing an XGBoost with a focus on topological features.<n>Current research predominantly emphasizes short-term predictions and neglects the broader network-wide patterns essential for mitigating ripple effects.<n>This research implements and improves an existing methodology, originally designed to forecast the evolution of the fast-changing US air network, to predict delays in the Dutch Railways.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Dutch railway network is one of the busiest in the world, with delays being a prominent concern for the principal passenger railway operator NS. This research addresses a gap in delay prediction studies within the Dutch railway network by employing an XGBoost Classifier with a focus on topological features. Current research predominantly emphasizes short-term predictions and neglects the broader network-wide patterns essential for mitigating ripple effects. This research implements and improves an existing methodology, originally designed to forecast the evolution of the fast-changing US air network, to predict delays in the Dutch Railways. By integrating Node Centrality Measures and comparing multiple classifiers like RandomForest, DecisionTree, GradientBoosting, AdaBoost, and LogisticRegression, the goal is to predict delayed trajectories. However, the results reveal limited performance, especially in non-simultaneous testing scenarios, suggesting the necessity for more context-specific adaptations. Regardless, this research contributes to the understanding of transportation network evaluation and proposes future directions for developing more robust predictive models for delays.

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