IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v52y2025i4d10.1007_s11116-024-10472-x.html
   My bibliography  Save this article

A deep semi-supervised machine learning algorithm for detecting transportation modes based on GPS tracking data

Author

Listed:
  • Paria Sadeghian

    (Dalarna University)

  • Arman Golshan

    (Dalarna University)

  • Mia Xiaoyun Zhao

    (Dalarna University)

  • Johan Håkansson

    (Dalarna University)

Abstract

Transportation research has benefited from GPS tracking devices since a higher volume of data can be acquired. Trip information such as travel speed, time, and most visited locations can be easily extracted from raw GPS tracking data. However, transportation modes cannot be extracted directly and require more complex analytical processes. Common approaches for detecting travel modes heavily depend on manual labelling of trajectories with accurate trip information, which is inefficient in many aspects. This paper proposes a method of semi-supervised machine learning by using minimal labelled data. The method can accept GPS trajectory with adjustable length and extract latent information with long short-term memory (LSTM) Autoencoder. The method adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. The proposed method is assessed by applying it to the case study where an accuracy of 93.94% can be achieved, which significantly outperforms similar studies.

Suggested Citation

  • Paria Sadeghian & Arman Golshan & Mia Xiaoyun Zhao & Johan Håkansson, 2025. "A deep semi-supervised machine learning algorithm for detecting transportation modes based on GPS tracking data," Transportation, Springer, vol. 52(4), pages 1745-1765, August.
    • Handle: RePEc:kap:transp:v:52:y:2025:i:4:d:10.1007_s11116-024-10472-x
    DOI: 10.1007/s11116-024-10472-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-024-10472-x
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-024-10472-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Marra, Alessio D. & Sun, Linghang & Corman, Francesco, 2022. "The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area," Transport Policy, Elsevier, vol. 116(C), pages 258-268.
    2. Roy, Avipsa & Fuller, Daniel & Nelson, Trisalyn & Kedron, Peter, 2022. "Assessing the role of geographic context in transportation mode detection from GPS data," Journal of Transport Geography, Elsevier, vol. 100(C).
    3. Li Shen & Peter R. Stopher, 2014. "Review of GPS Travel Survey and GPS Data-Processing Methods," Transport Reviews, Taylor & Francis Journals, vol. 34(3), pages 316-334, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hong, Ye & Stüdeli, Emanuel & Raubal, Martin, 2023. "Evaluating geospatial context information for travel mode detection," Journal of Transport Geography, Elsevier, vol. 113(C).
    2. Yaxuan Zhang & Chunjiang Li & Ying Song & Yanwei Chai & Yingling Fan, 2024. "Personalizing the dichotomy of fixed and flexible activities in everyday life: deriving prism anchors from GPS-enabled survey data," Transportation, Springer, vol. 51(3), pages 1063-1088, June.
    3. Liang, Zhiyuan & Tang, Yili & Yu, Jianing & Wang, Yacan, 2024. "A collective incentive strategy to manage ridership rebound and consumer surplus in mass transit systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    4. Liu, Jianmiao & Li, Junyi & Chen, Yong & Lian, Song & Zeng, Jiaqi & Geng, Maosi & Zheng, Sijing & Dong, Yinan & He, Yan & Huang, Pei & Zhao, Zhijian & Yan, Xiaoyu & Hu, Qinru & Wang, Lei & Yang, Di & , 2023. "Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management," Applied Energy, Elsevier, vol. 331(C).
    5. Roy, Avipsa & Fuller, Daniel & Nelson, Trisalyn & Kedron, Peter, 2022. "Assessing the role of geographic context in transportation mode detection from GPS data," Journal of Transport Geography, Elsevier, vol. 100(C).
    6. Adam, Arnaud & Finance, Olivier & Thomas, Isabelle, 2021. "Monitoring trucks to reveal Belgian geographical structures and dynamics: From GPS traces to spatial interactions," Journal of Transport Geography, Elsevier, vol. 91(C).
    7. Konečný Vladimír & Zuzaniak Martin & Brídziková Mária & Jaśkiewicz Marek, 2023. "Regional Differences in the Impact of the COVID-19 Pandemic on the Demand for Bus Transport in the Slovak Republic," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 146-157, January.
    8. Viktorija Bobinaite & Inga Konstantinaviciute & Arvydas Galinis & Ausra Pazeraite & Vaclovas Miskinis & Mindaugas Cesnavicius, 2023. "Energy Sufficiency in the Passenger Transport of Lithuania," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    9. Hsueh, Chieh & Lin, Jen-Jia, 2023. "Influential factors of the route choices of scooter riders: A GPS-based data study," Journal of Transport Geography, Elsevier, vol. 113(C).
    10. McArthur, David Philip & Hong, Jinhyun, 2019. "Visualising where commuting cyclists travel using crowdsourced data," Journal of Transport Geography, Elsevier, vol. 74(C), pages 233-241.
    11. Katie Crist & Tarik Benmarhnia & Steven Zamora & Jiue-An Yang & Dorothy D. Sears & Loki Natarajan & Lindsay Dillon & James F. Sallis & Marta M. Jankowska, 2021. "Device-Measured and Self-Reported Active Travel Associations with Cardiovascular Disease Risk Factors in an Ethnically Diverse Sample of Adults," IJERPH, MDPI, vol. 18(8), pages 1-16, April.
    12. Florian Aschauer & Inka Rösel & Reinhard Hössinger & Heinz Brian Kreis & Regine Gerike, 2019. "Time use, mobility and expenditure: an innovative survey design for understanding individuals’ trade-off processes," Transportation, Springer, vol. 46(2), pages 307-339, April.
    13. Xu, Xinyue & Liu, Jing & Zhang, Anzhong & XieLan, Shiyu & Li, Zinuo & Liu, Jun & Ran, Bin, 2024. "The impacts of COVID-19 on route choice with guidance information in urban rail transit of megacities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    14. Silm, Siiri & Tominga, Ago & Saidla, Karl & Poom, Age & Tammaru, Tiit, 2024. "Socio-economic and residential differences in urban modality styles based on a long-term smartphone experiment," Journal of Transport Geography, Elsevier, vol. 115(C).
    15. Kwang-Sub Lee & Jin Ki Eom, 2024. "Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility," Transportation, Springer, vol. 51(5), pages 1907-1961, October.
    16. Yukun Gao & Pengjun Zhao, 2024. "Tracing long-term commute mode choice shifts in Beijing: four years after the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    17. Ishibashi, Sumiko & Kobayashi, Taiki & Taniguchi, Mamoru, 2024. "Does emphasis change in transportation mode choice affect workers’ actual mode choice? Implications from Japan in the COVID-19 era," Transport Policy, Elsevier, vol. 146(C), pages 343-355.
    18. Tinessa, Fiore & Román Garcia, Concepción & Simonelli, Fulvio & Papola, Andrea & Pagliara, Francesca, 2024. "How public transport users would react to different pandemic alert scenarios in the post-vaccine era? An analysis of preferences and attitudes of the users in the metropolitan area of Naples (Italy)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).
    19. Zhu, Siying & Cai, Yutong & Wang, Mengtong & Wang, Hua & Meng, Qiang, 2023. "How will China–Singapore International Land–Sea Trade Corridor affect route choice behaviour? A discrete choice model," Transport Policy, Elsevier, vol. 144(C), pages 11-22.
    20. Mariem Fekih & Tom Bellemans & Zbigniew Smoreda & Patrick Bonnel & Angelo Furno & Stéphane Galland, 2021. "A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France)," Transportation, Springer, vol. 48(4), pages 1671-1702, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:transp:v:52:y:2025:i:4:d:10.1007_s11116-024-10472-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.