Cited by 71Traffic Prediction and Management TechniquesTransportation Planning and OptimizationHuman Mobility and Location-Based Analysis

Real time transit demand prediction capturing station interactions and impact of special events

Noursalehi, Peyman, Koutsopoulos, Haris N., Zhao, Jinhua·2018·Transportation Research Part C: Emerging Technologies

Integrity signals

Registry

Crossref · 10.1016/j.trc.2018.10.023

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

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We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Short-term forecasting of public transit ridership can improve with ML.

    “Also, short-term forecasting of public transit ridership can improve with ML; see for example [158,586].”

    From Tackling climate change with machine learning (2023) · cites this paper as [586]

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2 of the 58 references couldn’t be matched to a known paper.

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Authors
Noursalehi, P., Koutsopoulos, H. N., & Zhao, J.
Affiliations
  • Northeastern University (US)
  • Massachusetts Institute of Technology (US)
Corresponding author
Noursalehi, Peyman
Venue
Transportation Research Part C: Emerging Technologies
Publisher
Elsevier BV
Published
2018-12
Volume
97
Pages
277-300
ISSN
0968-090X (ISSN-L)
OpenAlex
W2899828597
Cited by
71
References
58
Citations by year
2026: 9 · 2025: 10 · 2024: 7 · 2023: 7 · 2022: 13 · 2021: 10 · 2020: 11 · 2019: 3 · 2017: 1
FWCI
4.81
Type
journal article
Language
en
Open access
closed
Locations
Transportation Research Part C Emerging Technologies (publishedVersion)
Topics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Social Sciences › Transportation › Transportation Planning and Optimization
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
Keywords
state-space model, dynamic factor models, hierarchical clustering, ensemble forecasting, special events, football match, London Underground, public transport demand, service quality

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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