Fine-grained crowd distribution forecasting with multi-order spatial interactions using mobile phone data

Li, Mingxiao; Gao, Song; Qiu, Peiyuan; Tu, Wei; Lü, Feng; Zhao, Tianhong; Li, Qingquan

doi:10.1016/j.trc.2022.103908

Cited by 5 publications

(1 citation statement)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such trajectory data can be collected in various ways such as mobile phones, GPS devices, social media geo-tags, and public transportation records (Dodge et al, 2020;Liu et al, 2015;Yue et al, 2014). The large-scale individual trajectory data has been used to portray human mobility across space and time, which brings new opportunities for understanding mobility patterns and laying the basis for many research topics in GIScience and beyond such as modeling human dynamics (Huang et al, 2018;Li et al, 2022;Shaw and Sui, 2020;Sun et al, 2016), traffic analysis and public transport planning (Caceres et al, 2012;Liu and Cheng, 2020), understanding the COVID-19 pandemic impacts on human mobility changes (Chow et al, 2021;Huang et al, 2020;Long and Ren, 2022;Noi et al, 2022), spatiotemporal analysis of individual air pollution exposure 4)Differential Privacy (DP), proposed by Dwork (2006) as a rigorous mathematical definition of privacy, has been widely used in privacy-preserving data publishing. By adding noise (e.g., Laplace noise) to the query result related to a database, others cannot determine whether a record exists in the database or not.…”

Section: Introductionmentioning

confidence: 99%

CATS: Conditional Adversarial Trajectory Synthesis for privacy-preserving trajectory data publication using deep learning approaches

Rao,

Gao,

Zhu

2023

International Journal of Geographical Information Science

Self Cite

View full text Add to dashboard Cite

The prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users. Such trajectory big data bring new opportunities to human mobility research but also raise public concerns with regard to location privacy. In this work, we present the Conditional Adversarial Trajectory Synthesis (CATS), a deep-learning-based GeoAI methodological framework for privacy-preserving trajectory data generation and publication. CATS applies K-anonymity to the underlying spatiotemporal distributions of human movements, which provides a distributional-level strong privacy guarantee. By leveraging conditional adversarial training on K-anonymized human mobility matrices, trajectory global context learning using the attention-based mechanism, and recurrent bipartite graph matching of adjacent trajectory points, CATS is able to reconstruct trajectory topology from conditionally sampled locations and generate high-quality individual-level synthetic trajectory data, which can serve as supplements or alternatives to raw data for privacy-preserving trajectory data publication. The experiment results on over 90k GPS trajectories show that our method has a better performance in privacy preservation, spatiotemporal characteristic preservation, and downstream utility compared with baseline methods, which brings new insights into privacy-preserving human mobility research using generative AI techniques and explores data ethics issues in GIScience.

show abstract