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

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2022-02
Major/Subject
Mcode
Degree programme
Language
en
Pages
26
Series
ISPRS International Journal of Geo-Information, Volume 11, issue 2
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.
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.
Keywords
Activity-based demand generation, Spatial assignment, Synthetic population, Workplaces assignment
Other note
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