2026-03-08T13:58:15Z https://kansai-u.repo.nii.ac.jp/oai

oai:kansai-u.repo.nii.ac.jp:00024279 2024-10-30T04:50:37Z 528:1538:1587

Workplace Assignment to Workers in Synthetic Populations in Japan 30296082 Murata, Tadahiko 村田, 忠彦 村田 Murata 忠彦 Tadahiko Iwase, Daiki 岩瀬, 大樹 岩瀬 Iwase 大樹 Daiki 70847201 Harada, Takuya 原田, 拓弥 原田 Harada 拓弥 Takuya Agent-based modeling daytime population distribution microsimulations social simulations synthetic population workplace assignment 合成人口データ 社会シミュレーション 昼間人口 関西大学 Kansai University In this article, we assign workplace attributes to each worker in each household in a synthetic population using multiple censuses conducted in Japan. The synthetic population is a set of artificial individual attributes for each resident that is synthesized according to census data. We have synthesized a set of the synthetic populations of Japan. We assign a workplace attribute to each worker to estimate daytime population distribution and develop activity-based models in agent-based or microsimulations. Although statistical information in a residential area or a working place is released by the government and some individual moving data are released by cellphone companies, it is hard to collect the information with home and workplace location of a worker with their family and working information. We employ origin–destination–industry (ODI) statistics to estimate workplaces for workers. Since some attributes in ODI statistics are not available for privacy reasons, we propose a workplace assignment method for all cities, towns, and villages using restricted ODI and OD statistics in Japan. We show how much difference there are between the number of workers using the complete ODI statistics and the number of workers by the proposed workplace assignment method. We show that 88.2% of workers in a city in Japan are assigned to correct cities as workplaces by our proposed method. We also show several maps of daytime population distributions by our proposed method. Synthetic populations with workplace attributes enable real-scale social simulations to design transport or business systems in times of peace or to estimate victims and plan recoveries in times of emergency, such as disasters or pandemics. Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant 20K10362 Kansai University Fund for Supporting Outlay Research Centers, 2020–2021 IEEE 2022-11-07 eng journal article VoR http://hdl.handle.net/10112/00028288 https://kansai-u.repo.nii.ac.jp/records/24279 10.1109/TCSS.2022.3217614 2329924X IEEE Transactions on Computational Social Systems 1 10 https://kansai-u.repo.nii.ac.jp/record/24279/files/KU-1100-20221107-01.pdf application/pdf 5.3 MB 2023-04-24