Trip mode detection from massive smartphone data
Nowadays, some smartphone applications require the location of users to be able to provide circumstantial information. However, this data may not be fluid and continuously recorded in a way that can be easily analysed for transport planning purposes. This paper proposes a methodology to reconstruct trips and detect modes from a weather smartphone app data, combined with a validation survey. These results can be useful to create origin-destination matrices and other analyses based on trip data. The authors' study shows that the Artificial Neural Network (ANN), combined with a proposed data processing framework, provides the best travel mode detection.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
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Supplemental Notes:
- © 2023 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
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Authors:
- Viallard, Alexis
- Bourdeau, Jean-Simon
- Morency, Catherine
- Trépanier, Martin
- Vargas, Edwin
- Benzamane, Hicham
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Conference:
- 12th International Conference on Transport Survey Methods
- Location: Maceira , Portugal
- Date: 2022-3-20 to 2022-3-25
- Publication Date: 2024
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 37-47
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Serial:
- Transportation Research Procedia
- Volume: 76
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 2352-1465
- Serial URL: http://www.sciencedirect.com/science/journal/23521465/
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Global Positioning System; Location data; Machine learning; Mobile applications; Smartphones; Travel behavior
- Identifier Terms: Python (Programming Language)
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;
Filing Info
- Accession Number: 01916408
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 23 2024 10:49AM