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

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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
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    References listed on IDEAS

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    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.
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    Cited by:

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    2. 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).

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