London Heathrow Airport Uses Real-Time Analytics for Improving Operations
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DOI: 10.1287/inte.2020.1044
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References listed on IDEAS
- Victor Richmond R. Jose & Robert L. Winkler, 2009. "Evaluating Quantile Assessments," Operations Research, INFORMS, vol. 57(5), pages 1287-1297, October.
- Yael Grushka-Cockayne & Kenneth C. Lichtendahl Jr. & Victor Richmond R. Jose & Robert L. Winkler, 2017. "Quantile Evaluation, Sensitivity to Bracketing, and Sharing Business Payoffs," Operations Research, INFORMS, vol. 65(3), pages 712-728, June.
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Cited by:
- Sobrie, Léon & Verschelde, Marijn & Hennebel, Veerle & Roets, Bart, 2023. "Capturing complexity over space and time via deep learning: An application to real-time delay prediction in railways," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1201-1217.
- Jiang, Yirui & Tran, Trung Hieu & Williams, Leon, 2023. "Machine learning and mixed reality for smart aviation: Applications and challenges," Journal of Air Transport Management, Elsevier, vol. 111(C).
- Okwir, Simon & Amouzgar, Kaveh & Ng, Amos HC., 2025. "Exploring prediction accuracy for optimal taxi times in airport operations using various machine learning models," Journal of Air Transport Management, Elsevier, vol. 122(C).
- Sobrie, Léon & Verschelde, Marijn & Roets, Bart, 2024. "Explainable real-time predictive analytics on employee workload in digital railway control rooms," European Journal of Operational Research, Elsevier, vol. 317(2), pages 437-448.
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Keywords
data-driven prediction; collaborative decision making; machine learning; airport performance;All these keywords.
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