Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorAgriesti, Serioen_US
dc.contributor.authorRoncoli, Claudioen_US
dc.contributor.authorNahmias-Biran, Bat Henen_US
dc.contributor.departmentDepartment of Built Environmenten
dc.contributor.groupauthorPlanning and Transportationen
dc.contributor.organizationAriel Universityen_US
dc.date.accessioned2022-03-15T12:38:33Z
dc.date.available2022-03-15T12:38:33Z
dc.date.issued2022-02en_US
dc.description| openaire: EC/H2020/856602/EU//FINEST TWINS Funding Information: Funding: This research was funded by the FINEST Twins Center of Excellence, H2020 European Union funding for Research and Innovation grant number 856602. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.description.abstractAgent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers.en
dc.description.versionPeer revieweden
dc.format.extent26
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationAgriesti, S, Roncoli, C & Nahmias-Biran, B H 2022, ' Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data ', ISPRS International Journal of Geo-Information, vol. 11, no. 2, 148 . https://doi.org/10.3390/ijgi11020148en
dc.identifier.doi10.3390/ijgi11020148en_US
dc.identifier.issn2220-9964
dc.identifier.otherPURE UUID: fad35c9f-6744-40ce-a8cc-6049013eb44aen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/fad35c9f-6744-40ce-a8cc-6049013eb44aen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85125045027&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/80328101/ijgi_11_00148_v2.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/113374
dc.identifier.urnURN:NBN:fi:aalto-202203152253
dc.language.isoenen
dc.publisherMDPI AG
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/856602/EU//FINEST TWINS Funding Information: Funding: This research was funded by the FINEST Twins Center of Excellence, H2020 European Union funding for Research and Innovation grant number 856602. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.relation.ispartofseriesISPRS International Journal of Geo-Informationen
dc.relation.ispartofseriesVolume 11, issue 2en
dc.rightsopenAccessen
dc.subject.keywordActivity-based demand generationen_US
dc.subject.keywordSpatial assignmenten_US
dc.subject.keywordSynthetic populationen_US
dc.subject.keywordWorkplaces assignmenten_US
dc.titleAssignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Dataen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion

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