Cited by 405Human Mobility and Location-Based AnalysisTraffic Prediction and Management TechniquesData Management and Algorithms

Inferring transportation modes from GPS trajectories using a convolutional neural network

Open access
Dabiri, Sina, Heaslip, Kevin·2018·Transportation Research Part C: Emerging Technologies

Integrity signals

Registry

Crossref · 10.1016/j.trc.2017.11.021

Cites

31 of 31 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

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

  • There is work on inferring people's travel modes and destinations from social media or mobile phone sensors such as GPS.

    “There is also work on inferring people's travel modes and destinations from social media or various mobile phone sensors such as GPS (transportation mode detection), e.g., [156,798].”

    From Tackling climate change with machine learning (2023) · cites this paper as [156]

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Authors
Dabiri, S., & Heaslip, K.
Affiliations
Virginia Tech (US)
Corresponding author
Dabiri, Sina
Venue
Transportation Research Part C: Emerging Technologies
Publisher
Elsevier BV
Published
2018-01
Volume
86
Pages
360-371
ISSN
0968-090X (ISSN-L)
OpenAlex
W2771098373
Cited by
405
References
31
Citations by year
2026: 25 · 2025: 33 · 2024: 53 · 2023: 50 · 2022: 57 · 2021: 61 · 2020: 62 · 2019: 44 · 2018: 20
FWCI
51.65
Type
journal article
Language
en
Open access
Locations
  • Transportation Research Part C Emerging Technologies (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (public-domain)
Topics
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
Keywords
GPS tracks, convolutional neural networks, travel demand analysis, smartphone mobility data, motion features, CNN architecture design, CNN ensemble, travel behavior

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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