Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data
dc.contributor | Aalto-yliopisto | fi |
dc.contributor | Aalto University | en |
dc.contributor.author | Agriesti, Serio | en_US |
dc.contributor.author | Roncoli, Claudio | en_US |
dc.contributor.author | Nahmias-Biran, Bat Hen | en_US |
dc.contributor.department | Department of Built Environment | en |
dc.contributor.groupauthor | Planning and Transportation | en |
dc.contributor.organization | Ariel University | en_US |
dc.date.accessioned | 2022-03-15T12:38:33Z | |
dc.date.available | 2022-03-15T12:38:33Z | |
dc.date.issued | 2022-02 | en_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.abstract | Agent-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.version | Peer reviewed | en |
dc.format.extent | 26 | |
dc.format.mimetype | application/pdf | en_US |
dc.identifier.citation | Agriesti, 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/ijgi11020148 | en |
dc.identifier.doi | 10.3390/ijgi11020148 | en_US |
dc.identifier.issn | 2220-9964 | |
dc.identifier.other | PURE UUID: fad35c9f-6744-40ce-a8cc-6049013eb44a | en_US |
dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/fad35c9f-6744-40ce-a8cc-6049013eb44a | en_US |
dc.identifier.other | PURE LINK: http://www.scopus.com/inward/record.url?scp=85125045027&partnerID=8YFLogxK | en_US |
dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/80328101/ijgi_11_00148_v2.pdf | en_US |
dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/113374 | |
dc.identifier.urn | URN:NBN:fi:aalto-202203152253 | |
dc.language.iso | en | en |
dc.publisher | MDPI AG | |
dc.relation | info: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.ispartofseries | ISPRS International Journal of Geo-Information | en |
dc.relation.ispartofseries | Volume 11, issue 2 | en |
dc.rights | openAccess | en |
dc.subject.keyword | Activity-based demand generation | en_US |
dc.subject.keyword | Spatial assignment | en_US |
dc.subject.keyword | Synthetic population | en_US |
dc.subject.keyword | Workplaces assignment | en_US |
dc.title | Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data | en |
dc.type | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä | fi |
dc.type.version | publishedVersion |