Discovering human mobility patterns with geo-location data collected from smartphone users has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns based on GPS data. We view this problem from a probabilistic perspective in order to explore more information from the original GPS data compared to other conventional methods. A non-parameter Bayesian modeling method, Infinite Gaussian Mixture Model, is used to estimate the probability density for the daily mobility. Then, we use Kullback-Leibler divergence as the metrics to measure the similarity of different probability distributions. And combining Infinite Gaussian Mixture Model and Kullback-Leibler divergence, we derived an automatic clustering algorithm to discover mobility patterns for each individual user without setting the number of clusters in advance. In the experiments, the effectiveness of our method is validated on the real user data collected from different users. The results show that the IGMM-based algorithm outperforms the GMM-based algorithm. We also test our methods on the dataset with different lengths to discover the minimum data length for discovering mobility patterns.
On Inferring User Socioeconomic Status with Mobility Records
Nanyang Technological University（南洋理工大学）；Huawei Singapore Research Center（华为新加坡研究中心）；The University of Hong Kong（香港大学）；China-Singapore International Joint Research Institute（中新国际联合研究院）
Leveraging graph neural networks and mobility data for COVID-19 forecasting
Postgraduate Program in Computer Science, Federal University of Ouro Preto（奥鲁普托联邦大学计算机科学研究生院）；Department of Computing, Federal University of Ouro Preto（奥鲁普托联邦大学计算机系）；National Center for Monitoring and Early Warning of Natural Disasters（国家自然灾害监测与预警中心）
Network-Level Measures of Mobility from Aggregated Origin-Destination Data
Department of Applied Mathematics, University of Washington（应用数学系，华盛顿大学）；Department of Mathematics, University of California, San Diego（数学系，加州大学圣地亚哥分校）
Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Tencent AI Lab（腾讯AI实验室）
MotiveMob: Motivation as Semantic Action for Closed-Loop Human Mobility Generation
The University of Osaka（大阪大学）；The University of Tokyo（东京大学）；The Hong Kong University of Science and Technology（香港科技大学）；Institute of Science Tokyo（东京科学大学）
