Research Article
Synthetic People Flow: Privacy-Preserving Mobility Modeling from Large-Scale Location Data in Urban Areas
@INPROCEEDINGS{10.1007/978-3-030-94822-1_36, author={Naoki Tamura and Kenta Urano and Shunsuke Aoki and Takuro Yonezawa and Nobuo Kawaguchi}, title={Synthetic People Flow: Privacy-Preserving Mobility Modeling from Large-Scale Location Data in Urban Areas}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 18th EAI International Conference, MobiQuitous 2021, Virtual Event, November 8-11, 2021, Proceedings}, proceedings_a={MOBIQUITOUS}, year={2022}, month={2}, keywords={Spatio-temporal data analysis Privacy preserving data mining Unsupervised Learning}, doi={10.1007/978-3-030-94822-1_36} }- Naoki Tamura
Kenta Urano
Shunsuke Aoki
Takuro Yonezawa
Nobuo Kawaguchi
Year: 2022
Synthetic People Flow: Privacy-Preserving Mobility Modeling from Large-Scale Location Data in Urban Areas
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-030-94822-1_36
Abstract
Recently, there has been an increasing demand for traffic simulation and congestion prediction for urban planning, especially for infection simulation due to the Covid-19 epidemic. On the other hand, the widespread use of wearable devices has made it possible to collect a large amount of user location history with high accuracy, and it is expected that this data will be used for simulation. However, it is difficult to collect location histories for the entire population of a city, and detailed data that can reproduce trajectories is expensive. In addition, such personal location histories contain private information such as addresses and workplaces, which restricts the use of raw data. This paper proposes Agent2Vec, a mobility modeling model based on unsupervised learning. Using this method, we generate synthetic human flow data without personal information.
