Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction
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There is a rich literature exploring ML methods to predict bus arrival times and their uncertainty.
“For example, there is a rich literature exploring ML methods to predict bus arrival times and their uncertainty [18,526].”
From Tackling climate change with machine learning (2023) · cites this paper as [526]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Mazloumi, E., Rose, G., Currie, G., & Moridpour, S.
- Affiliations
- Monash University (AU)
- RMIT University (AU)
- Corresponding author
- Mazloumi, Ehsan
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2011-04
- Volume
- 24
- Issue
- 3
- Pages
- 534-542
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W2050053010
- Cited by
- 133
- References
- 49
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 7 · 2023: 7 · 2022: 13 · 2021: 10 · 2020: 4 · 2019: 15 · 2018: 8 · 2017: 9 · 2016: 9 · 2015: 10 · 2014: 11 · 2013: 8 · 2012: 5
- FWCI
- 16.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Engineering Applications of Artificial Intelligence (publishedVersion)
- RMIT Research Repository (RMIT University Library) (submittedVersion)
- Figshare (submittedVersion)
- Topics
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- prediction intervals, artificial neural networks, uncertainty quantification, bus travel time prediction, confidence intervals, model structure uncertainty, uncertainty decomposition
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