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Deriving weeklong activity-travel dairy from Google Location History: survey tool development and a field test in Toronto.

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Title: Deriving weeklong activity-travel dairy from Google Location History: survey tool development and a field test in Toronto.
Authors: Li, Melvyn1 (AUTHOR) melvyn.li@utoronto.ca, Wang, Kaili1 (AUTHOR) jackkaili.wang@mail.utoronto.ca, Liu, Yicong1 (AUTHOR) nora.liu@mail.utoronto.ca, Nurul Habib, Khandker1 (AUTHOR) khandker.nurulhabib@utoronto.ca
Source: Transportation. Jun2026, Vol. 53 Issue 3, p1085-1106. 22p.
Subjects: Location data, Transportation demand management
Geographic Terms: Toronto (Ont.), Canada
Abstract: This paper introduces an innovative travel survey methodology that utilizes Google Location History (GLH) data to generate travel diaries for transportation demand analysis. By leveraging the accuracy and omnipresence among smartphone users of GLH, the proposed methodology avoids the need for proprietary GPS tracking applications to collect smartphone-based GPS data. This research utilizes the existing travel survey software, TRavel Activity Internet Survey Interface (TRAISI), which allows for the design and implementation of surveys through highly modular and customizable components. A new module was developed within this software to serve as a repository for GLH, enabling the derivation of activity-travel diaries from each respondent's GLH. The feasibility of this data collection approach is showcased through the Google Timeline Travel Survey (GTTS) conducted in the Greater Toronto Area, Canada. The resultant dataset from the GTTS is demographically representative and offers detailed and accurate travel behavioural insights. [ABSTRACT FROM AUTHOR]
Copyright of Transportation is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Toronto+%28Ont%2E%29%22">Toronto (Ont.)</searchLink><br /><searchLink fieldCode="DE" term="%22Canada%22">Canada</searchLink>
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  Data: This paper introduces an innovative travel survey methodology that utilizes Google Location History (GLH) data to generate travel diaries for transportation demand analysis. By leveraging the accuracy and omnipresence among smartphone users of GLH, the proposed methodology avoids the need for proprietary GPS tracking applications to collect smartphone-based GPS data. This research utilizes the existing travel survey software, TRavel Activity Internet Survey Interface (TRAISI), which allows for the design and implementation of surveys through highly modular and customizable components. A new module was developed within this software to serve as a repository for GLH, enabling the derivation of activity-travel diaries from each respondent's GLH. The feasibility of this data collection approach is showcased through the Google Timeline Travel Survey (GTTS) conducted in the Greater Toronto Area, Canada. The resultant dataset from the GTTS is demographically representative and offers detailed and accurate travel behavioural insights. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Transportation is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1007/s11116-024-10523-3
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      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 1085
    Subjects:
      – SubjectFull: Location data
        Type: general
      – SubjectFull: Transportation demand management
        Type: general
      – SubjectFull: Toronto (Ont.)
        Type: general
      – SubjectFull: Canada
        Type: general
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      – TitleFull: Deriving weeklong activity-travel dairy from Google Location History: survey tool development and a field test in Toronto.
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            NameFull: Li, Melvyn
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            NameFull: Wang, Kaili
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            NameFull: Liu, Yicong
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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