IDEAS home Printed from https://ideas.repec.org/a/zbw/wistat/230954.html
   My bibliography  Save this article

Die Verwendung von Straßensensoren und Capture-recapture-Techniken zur Messfehlerkorrektur in Surveys

Author

Listed:
  • Klingwort, Jonas

Abstract

Dieser Artikel stellt eine Methode vor, welche Survey-, Sensor- und administrative Daten verknüpft und unter Anwendung von Capture-recapture-Techniken (CRC) die Korrektur von Messfehlern in Surveys ermöglicht. Dazu werden die Antworten des niederländischen Surveys zu Güterverkehr mit externen Sensormessungen verknüpft. Mittels CRC-Techniken wird die Unterberichterstattung geschätzt, wobei administrative Daten Informationen über Fahrzeuge und Halter liefern, die zur Modellierung der Heterogenität in den Beobachtungen verwendet werden. Die Ergebnisse zeigen, dass die Surveyschätzungen aufgrund von Unterberichterstattung negativ verzerrt sind.

Suggested Citation

  • Klingwort, Jonas, 2021. "Die Verwendung von Straßensensoren und Capture-recapture-Techniken zur Messfehlerkorrektur in Surveys," WISTA – Wirtschaft und Statistik, Statistisches Bundesamt (Destatis), Wiesbaden, vol. 73(1), pages 49-58.
    • Handle: RePEc:zbw:wistat:230954
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/230954/1/wista-2021-1-049-058.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Natalie Shlomo & Harvey Goldstein, 2015. "Editorial: Big data in social research," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 787-790, October.
    2. 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.
    3. Stopher, Peter R. & Greaves, Stephen P., 2007. "Household travel surveys: Where are we going?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(5), pages 367-381, June.
    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. 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.
    2. Jariyasunant, Jerald & Carrel, Andre & Ekambaram, Venkatesan & Gaker, DJ & Kote, Thejovardhana & Sengupta, Raja & Walker, Joan L., 2011. "The Quantified Traveler: Using personal travel data to promote sustainable transport behavior," University of California Transportation Center, Working Papers qt9jg0p1rj, University of California Transportation Center.
    3. 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).
    4. Gingerich, Kevin & Maoh, Hanna & Anderson, William, 2016. "Expansion of a GPS Truck Trip Sample to Remove Bias and Obtain Representative Flows for Ontario," 57th Transportation Research Forum (51st CTRF) Joint Conference, Toronto, Ontario, May 1-4, 2016 319310, Transportation Research Forum.
    5. Aihua Fan & Xumei Chen, 2020. "Exploring the Relationship between Transport Interventions, Mode Choice, and Travel Perception: An Empirical Study in Beijing, China," IJERPH, MDPI, vol. 17(12), pages 1-19, June.
    6. Li, Linchao & Zhu, Jiasong & Zhang, Hailong & Tan, Huachun & Du, Bowen & Ran, Bin, 2020. "Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 282-292.
    7. Chen, Cynthia & Gong, Hongmian & Lawson, Catherine & Bialostozky, Evan, 2010. "Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 830-840, December.
    8. 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).
    9. 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).
    10. Winters, Meghan & Voss, Christine & Ashe, Maureen C. & Gutteridge, Kaitlyn & McKay, Heather & Sims-Gould, Joanie, 2015. "Where do they go and how do they get there? Older adults' travel behaviour in a highly walkable environment," Social Science & Medicine, Elsevier, vol. 133(C), pages 304-312.
    11. Henning Silber & Johannes Breuer & Christoph Beuthner & Tobias Gummer & Florian Keusch & Pascal Siegers & Sebastian Stier & Bernd Weiß, 2022. "Linking surveys and digital trace data: Insights from two studies on determinants of data sharing behaviour," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 387-407, December.
    12. 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.
    13. Jariyasunant, Jerald & Carrel, Andre & Ekambaram, Venkatesan & Gaker, DJ & Kote, Thejovardhana & Sengupta, Raja & Walker, Joan L., 2011. "The Quantified Traveler: Using personal travel data to promote sustainable transport behavior," University of California Transportation Center, Working Papers qt678537sx, University of California Transportation Center.
    14. 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.
    15. 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.
    16. Patrick Bonnel & Etienne Hombourger & Ana-Maria Olteanu-Raimond & Zbigniew Smoreda, 2015. "Passive Mobile Phone Dataset to Construct Origin-destination Matrix: Potentials and Limitations," Post-Print halshs-01664219, HAL.
    17. Lukas Hartwig & Reinhard Hössinger & Yusak Octavius Susilo & Astrid Gühnemann, 2022. "The Impacts of a COVID-19 Related Lockdown (and Reopening Phases) on Time Use and Mobility for Activities in Austria—Results from a Multi-Wave Combined Survey," Sustainability, MDPI, vol. 14(12), pages 1-24, June.
    18. Michael Adjemian & Jeffrey Williams, 2009. "Using census aggregates to proxy for household characteristics: an application to vehicle ownership," Transportation, Springer, vol. 36(2), pages 223-241, March.
    19. Georgios Magkonis & Karen Jackson, 2019. "Identifying Networks in Social Media: The case of #Grexit," Networks and Spatial Economics, Springer, vol. 19(1), pages 319-330, March.
    20. Nina Verzosa & Stephen Greaves & Chinh Ho & Mark Davis, 2021. "Stated willingness to participate in travel surveys: a cross-country and cross-methods comparison," Transportation, Springer, vol. 48(3), pages 1311-1327, June.

    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:zbw:wistat:230954. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/stagvde.html .

    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.