IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v154y2021icp413-429.html
   My bibliography  Save this article

Insights on data quality from a large-scale application of smartphone-based travel survey technology in the Phoenix metropolitan area, Arizona, USA

Author

Listed:
  • Hong, Shuyao
  • Zhao, Fang
  • Livshits, Vladimir
  • Gershenfeld, Shari
  • Santos, Jorge
  • Ben-Akiva, Moshe

Abstract

Collecting accurate travel data is vital for transportation planning purposes. Regional travel demand forecasts as well as transportation system analyses depend on datasets that provide origins and destinations of travel for various modes, purposes of travel, socio-economic characteristics of the system users, and other attributes critical for understanding travel demand. GPS-based household travel surveys emerged as a state-of-the-practice method to collect travel data with increased accuracy and detail. The Maricopa Association of Governments conducted a survey utilizing Future Mobility Sensing (FMS) technology. One hundred percent of the sample was collected with the FMS technology platform that combines mobile sensing through a smartphone app with machine learning and a user interface. The technology enables detailed, multi-day, multimodal, user-verified travel and activity behavior data to be obtained with a reduced burden on participants. The data collected through the survey was analyzed together with a comparable dataset obtained through traditional recall-based collection methods during the same time period. The broad conclusions are that the 100% GPS-based surveys with the FMS technology platform provide greater accuracy, detail and completeness of data, as well as greater flexibility than traditional data collection approaches that rely on participant recall. Emphasis was made on comparative analyses between traditionally collected data and the GPS survey with the FMS technology. The paper systematically identifies and explains differences and provides original analyses that can inform future decision making relevant to similar data collection exercises. The method is particularly applicable for monitoring mobility in the ongoing conditions of rapidly changing travel behavior, especially due to the COVID-19 pandemic.

Suggested Citation

  • Hong, Shuyao & Zhao, Fang & Livshits, Vladimir & Gershenfeld, Shari & Santos, Jorge & Ben-Akiva, Moshe, 2021. "Insights on data quality from a large-scale application of smartphone-based travel survey technology in the Phoenix metropolitan area, Arizona, USA," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 413-429.
    • Handle: RePEc:eee:transa:v:154:y:2021:i:c:p:413-429
    DOI: 10.1016/j.tra.2021.10.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856421002482
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2021.10.002?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. Seo, Toru & Kusakabe, Takahiko & Gotoh, Hiroto & Asakura, Yasuo, 2019. "Interactive online machine learning approach for activity-travel survey," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 362-373.
    2. Peter Stopher & Camden FitzGerald & Min Xu, 2007. "Assessing the accuracy of the Sydney Household Travel Survey with GPS," Transportation, Springer, vol. 34(6), pages 723-741, November.
    3. Paul Kelly & Patricia Krenn & Sylvia Titze & Peter Stopher & Charlie Foster, 2013. "Quantifying the Difference Between Self-Reported and Global Positioning Systems-Measured Journey Durations: A Systematic Review," Transport Reviews, Taylor & Francis Journals, vol. 33(4), pages 443-459, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Li, Shaojie & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2023. "Building-centric investigation into electric vehicle behavior: A survey-based simulation method for charging system design," Energy, Elsevier, vol. 271(C).

    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. Chiara Calastri & Romain Crastes dit Sourd & Stephane Hess, 2020. "We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning," Transportation, Springer, vol. 47(1), pages 175-201, February.
    2. Satomi Kimijima & Masahiko Nagai, 2017. "Human Mobility Analysis for Extracting Local Interactions under Rapid Socio-Economic Transformation in Dawei, Myanmar," Sustainability, MDPI, vol. 9(9), pages 1-14, September.
    3. Molloy, Joseph & Schatzmann, Thomas & Schoeman, Beaumont & Tchervenkov, Christopher & Hintermann, Beat & Axhausen, Kay W., 2021. "Observed impacts of the Covid-19 first wave on travel behaviour in Switzerland based on a large GPS panel," Transport Policy, Elsevier, vol. 104(C), pages 43-51.
    4. Peter Schantz & Lina Wahlgren & Jane Salier Eriksson & Johan Nilsson Sommar & Hans Rosdahl, 2018. "Estimating duration-distance relations in cycle commuting in the general population," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-20, November.
    5. 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.
    6. Stopher, Peter & Clifford, Eoin & Swann, Natalie & Zhang, Yun, 2009. "Evaluating voluntary travel behaviour change: Suggested guidelines and case studies," Transport Policy, Elsevier, vol. 16(6), pages 315-324, November.
    7. 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.
    8. Georges Sfeir & Filipe Rodrigues & Maya Abou Zeid & Francisco Camara Pereira, 2023. "Analyzing the Reporting Error of Public Transport Trips in the Danish National Travel Survey Using Smart Card Data," Papers 2308.01198, arXiv.org, revised Sep 2023.
    9. Peter R. Stopher & Asif Ahmed & Wen Liu, 2017. "Travel time budgets: new evidence from multi-year, multi-day data," Transportation, Springer, vol. 44(5), pages 1069-1082, September.
    10. Aschauer, Florian & Hössinger, Reinhard & Jara-Diaz, Sergio & Schmid, Basil & Axhausen, Kay & Gerike, Regine, 2021. "Comprehensive data validation of a combined weekly time use and travel survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 66-82.
    11. Mars, Lidón & Arroyo, Rosa & Ruiz, Tomás, 2022. "Mobility and wellbeing during the covid-19 lockdown. Evidence from Spain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 107-129.
    12. Thomas, T. & Tutert, S.I.A., 2013. "An empirical model for trip distribution of commuters in The Netherlands: transferability in time and space reconsidered," Journal of Transport Geography, Elsevier, vol. 26(C), pages 158-165.
    13. Fillekes, Michelle Pasquale & Röcke, Christina & Katana, Marko & Weibel, Robert, 2019. "Self-reported versus GPS-derived indicators of daily mobility in a sample of healthy older adults," Social Science & Medicine, Elsevier, vol. 220(C), pages 193-202.
    14. Delclòs-Alió, Xavier & Miralles-Guasch, Carme, 2017. "Suburban travelers pressed for time: Exploring the temporal implications of metropolitan commuting in Barcelona," Journal of Transport Geography, Elsevier, vol. 65(C), pages 165-174.
    15. Tsoleridis, Panagiotis & Choudhury, Charisma F. & Hess, Stephane, 2022. "Deriving transport appraisal values from emerging revealed preference data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 225-245.
    16. Egu, Oscar & Bonnel, Patrick, 2020. "How comparable are origin-destination matrices estimated from automatic fare collection, origin-destination surveys and household travel survey? An empirical investigation in Lyon," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 267-282.
    17. Andrew Bwambale & Charisma F. Choudhury & Stephane Hess & Md. Shahadat Iqbal, 2021. "Getting the best of both worlds: a framework for combining disaggregate travel survey data and aggregate mobile phone data for trip generation modelling," Transportation, Springer, vol. 48(5), pages 2287-2314, October.
    18. Mofeng Yang & Yixuan Pan & Aref Darzi & Sepehr Ghader & Chenfeng Xiong & Lei Zhang, 2022. "A data-driven travel mode share estimation framework based on mobile device location data," Transportation, Springer, vol. 49(5), pages 1339-1383, October.
    19. Andreas Dypvik Landmark & Petter Arnesen & Carl-Johan Södersten & Odd André Hjelkrem, 2021. "Mobile phone data in transportation research: methods for benchmarking against other data sources," Transportation, Springer, vol. 48(5), pages 2883-2905, October.
    20. Guangnian Xiao & Qin Cheng & Chunqin Zhang, 2019. "Detecting travel modes from smartphone-based travel surveys with continuous hidden Markov models," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.

    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:eee:transa:v:154:y:2021:i:c:p:413-429. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.