Improving the generation of synthetic travel demand using origin–destination matrices from mobile phone data.
Saved in:
| Title: | Improving the generation of synthetic travel demand using origin–destination matrices from mobile phone data. |
|---|---|
| Authors: | Matet, Benoît1,2 (AUTHOR) benoit.matet@outlook.com, Côme, Etienne1 (AUTHOR) etienne.come@univ-eiffel.fr, Furno, Angelo2 (AUTHOR) angelo.furno@univ-eiffel.fr, Hörl, Sebastian3 (AUTHOR) sebastian.horl@irt-systemx.fr, Oukhellou, Latifa1 (AUTHOR) latifa.oukhellou@univ-eiffel.fr, El Faouzi, Nour-Eddin2 (AUTHOR) nour-eddin.elfaouzi@univ-eiffel.fr |
| Source: | Transportation. Jun2026, Vol. 53 Issue 3, p1107-1139. 33p. |
| Subjects: | Origin & destination traffic surveys, Location data, Statistical models, Transportation demand management, Spatiotemporal processes, Urban transportation |
| Geographic Terms: | France, Lyon (France) |
| Abstract: | The dynamics of urban transportation can be captured using activity-based models, which rely on travel demand data to get a comprehensive understanding of urban mobility. This data is usually derived from population samples and Household Travel Surveys (HTSs), which can be expensive and as a result, are conducted only every 5 to 10 years. Moreover, due to their limited reach, they are not adapted to represent the spatio-temporal structure of the flows of the total population. This calls for complementary data sources that could be used to update old surveys to cut costs and to estimate the global spatial mobility behavior of the population. In this paper, we propose steps in the state-of-the-art pipeline for travel demand synthesis with an approach for the temporal calibration and the location attribution based on time-dependent origin–destination (OD) matrices. These matrices describe the flows between zones of a city. This methodology is illustrated on the city of Lyon, France, with OD matrices estimated from the mobile phone activity of the subscribers of French telecom operator Orange. We explore how the spatialization can be performed using various probabilistic graph models whose parameters are evaluated via the OD matrices. The structure of the models enforces the consistency of the locations with the chains of activities, such as the fact that two "home" activities must have the same location. Multiple models are proposed, corresponding to different compromises between the two potentially incompatible sources that are HTS and mobile data. We show that while a very naive spatialization approach allows the generation of synthetic travel demand that perfectly fits the flows described by the OD matrices without respecting the consistency of the locations, the other proposed approaches offer much more realistic agendas at the expense of only small discrepancies with the mobile data. [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.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 193167756 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Improving the generation of synthetic travel demand using origin–destination matrices from mobile phone data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Matet%2C+Benoît%22">Matet, Benoît</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> benoit.matet@outlook.com</i><br /><searchLink fieldCode="AR" term="%22Côme%2C+Etienne%22">Côme, Etienne</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> etienne.come@univ-eiffel.fr</i><br /><searchLink fieldCode="AR" term="%22Furno%2C+Angelo%22">Furno, Angelo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> angelo.furno@univ-eiffel.fr</i><br /><searchLink fieldCode="AR" term="%22Hörl%2C+Sebastian%22">Hörl, Sebastian</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> sebastian.horl@irt-systemx.fr</i><br /><searchLink fieldCode="AR" term="%22Oukhellou%2C+Latifa%22">Oukhellou, Latifa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> latifa.oukhellou@univ-eiffel.fr</i><br /><searchLink fieldCode="AR" term="%22El+Faouzi%2C+Nour-Eddin%22">El Faouzi, Nour-Eddin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> nour-eddin.elfaouzi@univ-eiffel.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Transportation%22">Transportation</searchLink>. Jun2026, Vol. 53 Issue 3, p1107-1139. 33p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Origin+%26+destination+traffic+surveys%22">Origin & destination traffic surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Location+data%22">Location data</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Transportation+demand+management%22">Transportation demand management</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+transportation%22">Urban transportation</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22France%22">France</searchLink><br /><searchLink fieldCode="DE" term="%22Lyon+%28France%29%22">Lyon (France)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The dynamics of urban transportation can be captured using activity-based models, which rely on travel demand data to get a comprehensive understanding of urban mobility. This data is usually derived from population samples and Household Travel Surveys (HTSs), which can be expensive and as a result, are conducted only every 5 to 10 years. Moreover, due to their limited reach, they are not adapted to represent the spatio-temporal structure of the flows of the total population. This calls for complementary data sources that could be used to update old surveys to cut costs and to estimate the global spatial mobility behavior of the population. In this paper, we propose steps in the state-of-the-art pipeline for travel demand synthesis with an approach for the temporal calibration and the location attribution based on time-dependent origin–destination (OD) matrices. These matrices describe the flows between zones of a city. This methodology is illustrated on the city of Lyon, France, with OD matrices estimated from the mobile phone activity of the subscribers of French telecom operator Orange. We explore how the spatialization can be performed using various probabilistic graph models whose parameters are evaluated via the OD matrices. The structure of the models enforces the consistency of the locations with the chains of activities, such as the fact that two "home" activities must have the same location. Multiple models are proposed, corresponding to different compromises between the two potentially incompatible sources that are HTS and mobile data. We show that while a very naive spatialization approach allows the generation of synthetic travel demand that perfectly fits the flows described by the OD matrices without respecting the consistency of the locations, the other proposed approaches offer much more realistic agendas at the expense of only small discrepancies with the mobile data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193167756 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11116-024-10524-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 1107 Subjects: – SubjectFull: Origin & destination traffic surveys Type: general – SubjectFull: Location data Type: general – SubjectFull: Statistical models Type: general – SubjectFull: Transportation demand management Type: general – SubjectFull: Spatiotemporal processes Type: general – SubjectFull: Urban transportation Type: general – SubjectFull: France Type: general – SubjectFull: Lyon (France) Type: general Titles: – TitleFull: Improving the generation of synthetic travel demand using origin–destination matrices from mobile phone data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Matet, Benoît – PersonEntity: Name: NameFull: Côme, Etienne – PersonEntity: Name: NameFull: Furno, Angelo – PersonEntity: Name: NameFull: Hörl, Sebastian – PersonEntity: Name: NameFull: Oukhellou, Latifa – PersonEntity: Name: NameFull: El Faouzi, Nour-Eddin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00494488 Numbering: – Type: volume Value: 53 – Type: issue Value: 3 Titles: – TitleFull: Transportation Type: main |
| ResultId | 1 |
