Cited by 133Traffic Prediction and Management TechniquesTransportation Planning and OptimizationForecasting Techniques and Applications

Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction

Mazloumi, Ehsan, Rose, Geoff, Currie, Graham, Moridpour, Sara·2011·Engineering Applications of Artificial Intelligence

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Crossref · 10.1016/j.engappai.2010.11.004

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49 of 49 references verified0 citation errors · 0 not checkable

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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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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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