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In-stream mobility and speed estimation of mobile devices from mobile network data.

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Title: In-stream mobility and speed estimation of mobile devices from mobile network data.
Authors: Scholler, Rémy1,2 (AUTHOR) remy.scholler@orange.com, Alaoui-Ismaïli, Oumaïma1 (AUTHOR), Renaud, Denis1 (AUTHOR), Couchot, Jean-François2 (AUTHOR), Ballot, Eric3 (AUTHOR)
Source: Transportation. Feb2026, Vol. 53 Issue 1, p193-228. 36p.
Subjects: Privacy, General Data Protection Regulation, 2016, Data analysis, European Union, Speed measurements, Velocity measurements, Cell phone systems, Data protection laws
Abstract: The cellular network is now nearly an almost ubiquitous and real-time sensor with coverage anywhere and anytime for any device. Mobile network data is a rich source for official statistics, such as human mobility. However, unlike GPS tracks, each mobile device in this data is described without precise knowledge of its spatial characteristics. Furthermore, there is no information about the device's mobility status (i.e., whether it is moving or not) or speed which are important for behavioral analysis. Common mobility and speed estimations rely on precise location and do not consider privacy leakage risk. In this work, we propose two probabilistic approaches that estimate respectively devices' mobility and devices' speed from cellular data and connection likelihood maps for each network cell. Every estimation is computed in a short time and with a short history of data (for speed and for mobility). This constraint may be helpful with the most stringent legal frameworks for mobile operators including the combination of ePrivacy Directive and General Data Protection Regulation (GDPR) in Europe. The proposed approaches are the first we are aware of that allows for both mobility and speed estimation in this context. We experimented on two datasets, obtained from a mobile network operator's signaling data and the associated GPS tracks of many consenting users. Our speed estimations are over 20% more accurate than common ones based on mobile sites and we provide confidence intervals for each estimation. Mainly due to mobile network uncertainty, our approach for speed estimation are relatively inaccurate at low speeds and the movement detection could remain unclear. However our approach for mobility estimation fills this gap. [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: In-stream mobility and speed estimation of mobile devices from mobile network data.
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  Data: <searchLink fieldCode="AR" term="%22Scholler%2C+Rémy%22">Scholler, Rémy</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> remy.scholler@orange.com</i><br /><searchLink fieldCode="AR" term="%22Alaoui-Ismaïli%2C+Oumaïma%22">Alaoui-Ismaïli, Oumaïma</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Renaud%2C+Denis%22">Renaud, Denis</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Couchot%2C+Jean-François%22">Couchot, Jean-François</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ballot%2C+Eric%22">Ballot, Eric</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Transportation%22">Transportation</searchLink>. Feb2026, Vol. 53 Issue 1, p193-228. 36p.
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  Data: <searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink><br /><searchLink fieldCode="DE" term="%22General+Data+Protection+Regulation%2C+2016%22">General Data Protection Regulation, 2016</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22European+Union%22">European Union</searchLink><br /><searchLink fieldCode="DE" term="%22Speed+measurements%22">Speed measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Velocity+measurements%22">Velocity measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+phone+systems%22">Cell phone systems</searchLink><br /><searchLink fieldCode="DE" term="%22Data+protection+laws%22">Data protection laws</searchLink>
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  Data: The cellular network is now nearly an almost ubiquitous and real-time sensor with coverage anywhere and anytime for any device. Mobile network data is a rich source for official statistics, such as human mobility. However, unlike GPS tracks, each mobile device in this data is described without precise knowledge of its spatial characteristics. Furthermore, there is no information about the device's mobility status (i.e., whether it is moving or not) or speed which are important for behavioral analysis. Common mobility and speed estimations rely on precise location and do not consider privacy leakage risk. In this work, we propose two probabilistic approaches that estimate respectively devices' mobility and devices' speed from cellular data and connection likelihood maps for each network cell. Every estimation is computed in a short time and with a short history of data (for speed and for mobility). This constraint may be helpful with the most stringent legal frameworks for mobile operators including the combination of ePrivacy Directive and General Data Protection Regulation (GDPR) in Europe. The proposed approaches are the first we are aware of that allows for both mobility and speed estimation in this context. We experimented on two datasets, obtained from a mobile network operator's signaling data and the associated GPS tracks of many consenting users. Our speed estimations are over 20% more accurate than common ones based on mobile sites and we provide confidence intervals for each estimation. Mainly due to mobile network uncertainty, our approach for speed estimation are relatively inaccurate at low speeds and the movement detection could remain unclear. However our approach for mobility estimation fills this gap. [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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        Value: 10.1007/s11116-024-10494-5
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      – Code: eng
        Text: English
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        PageCount: 36
        StartPage: 193
    Subjects:
      – SubjectFull: Privacy
        Type: general
      – SubjectFull: General Data Protection Regulation, 2016
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      – SubjectFull: Data analysis
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      – SubjectFull: European Union
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      – SubjectFull: Speed measurements
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      – SubjectFull: Velocity measurements
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      – SubjectFull: Cell phone systems
        Type: general
      – SubjectFull: Data protection laws
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      – TitleFull: In-stream mobility and speed estimation of mobile devices from mobile network data.
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            NameFull: Scholler, Rémy
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              M: 02
              Text: Feb2026
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              Y: 2026
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