Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data
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 disagg...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/11/2/148 |
| _version_ | 1797479460104044544 |
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| author | Serio Agriesti Claudio Roncoli Bat-hen Nahmias-Biran |
| author_facet | Serio Agriesti Claudio Roncoli Bat-hen Nahmias-Biran |
| author_sort | Serio Agriesti |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:46:11Z |
| format | Article |
| id | doaj.art-972a5048902d42439e375f6a97c78325 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T21:46:11Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-972a5048902d42439e375f6a97c783252023-11-23T20:16:37ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-02-0111214810.3390/ijgi11020148Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available DataSerio Agriesti0Claudio Roncoli1Bat-hen Nahmias-Biran2Department of Built Environment, Aalto University, Otakaari 4, 02150 Espoo, FinlandDepartment of Built Environment, Aalto University, Otakaari 4, 02150 Espoo, FinlandDepartment of Civil Engineering, Ariel University, Ramat HaGolan St 65, Ariel 40700, IsraelAgent-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.https://www.mdpi.com/2220-9964/11/2/148synthetic populationspatial assignmentactivity-based demand generationworkplaces assignment |
| spellingShingle | Serio Agriesti Claudio Roncoli Bat-hen Nahmias-Biran Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data ISPRS International Journal of Geo-Information synthetic population spatial assignment activity-based demand generation workplaces assignment |
| title | Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data |
| title_full | Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data |
| title_fullStr | Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data |
| title_full_unstemmed | Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data |
| title_short | Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data |
| title_sort | assignment of a synthetic population for activity based modeling employing publicly available data |
| topic | synthetic population spatial assignment activity-based demand generation workplaces assignment |
| url | https://www.mdpi.com/2220-9964/11/2/148 |
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