Quantifying the dynamic predictability of train delay with uncertainty-aware neural networks-Reference-Cited by-同舟云学术

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Quantifying the dynamic predictability of train delay with uncertainty-aware neural networks

Published:2024-05 Issue: Volume:162 Page:104563
ISSN:0968-090X
Container-title:Transportation Research Part C: Emerging Technologies
language:en
Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Spanninger Thomas^ORCID,Wiedemann Nina^ORCID,Corman Francesco^ORCID

Funder

SNF

ETH Zürich Foundation

Publisher

Elsevier BV

Reference69 articles.

1. Deep learning using rectified linear units (ReLU);Agarap,2018

2. A gentle introduction to conformal prediction and distribution-free uncertainty quantification;Angelopoulos,2021

3. Exploring patterns of train delay evolution and timetable robustness;Artan;IEEE Trans. Intell. Transp. Syst.,2022

4. A stochastic model for reliability analysis of periodic train timetables;Artan;Transportmetrica B,2023

5. Information Theory;Ash,1965

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