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Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods
Jia Long Bao, Ali Mohammed Mansoor Alsahag, Seyed Sahand Mohammadi Ziabari
The reliable prediction of passenger train delays is a critical component of railway management. While contemporary research frequently attempts to maximize absolute accuracy by de…
Integrating Fairness and Explainability in a Multiple Instance Reinforcement Learning System
Bente Hinkenhuis, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor Alsahag
Predicting student performance from educational interaction data requires models that are both accurate and sufficiently transparent to support meaningful intervention, while demog…
Predicting Delayed Trajectories Using Network Features: A Study on the Dutch Railway Network
Merel Kampere, Ali Mohammed Mansoor Alsahag
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 i…
Time series classification of satellite data using LSTM networks: an approach for predicting leaf-fall to minimize railroad traffic disruption
Hein de Wilde, Ali Mohammed Mansoor Alsahag, Pierre Blanchet
Railroad traffic disruption as a result of leaf-fall cost the UK rail industry over 300 million per year and measures to mitigate such disruptions are employed on a large scale, wi…