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A Hawkes-Granger causal discovery model for macro and micro-level train delay propagation path identification in complex railway networks

https://doi.org/10.2139/ssrn.5898784
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Modeling train operation as sequences: A study of delay prediction with operation and weather data
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Discerning Primary and Secondary Delays in Railway Networks using Explainable AI
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Near-term train delay prediction in the Dutch railways network
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An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree
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Spatio-Temporal Point Processes With Attention for Traffic Congestion Event Modeling
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Train Time Delay Prediction for High-Speed Train Dispatching Based on Spatio-Temporal Graph Convolutional Network
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Enhancing the Understanding of Train Delays With Delay Evolution Pattern Discovery: A Clustering and Bayesian Network Approach
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Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review
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A2DJP: A Two Graph-Based Component Fused Learning Framework for Urban Anomaly Distribution and Duration Joint-Prediction
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Detecting and quantifying causal associations in large nonlinear time series datasets
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Causal Discovery from Temporal Data: An Overview and New Perspectives
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An Algorithm for Fast Recovery of Sparse Causal Graphs
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Causal Discovery in Hawkes Processes by Minimum Description Length
inferredcites →https://doi.org/10.1609/aaai.v37i12.26669openalex-snapshot
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction
inferredcites →https://doi.org/10.3390/make1010019openalex-snapshot
Causal Discovery with Attention-Based Convolutional Neural Networks
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