Location data collected from mobile devices represent mobility behaviors at individual and societal levels. These data have important applications ranging from transportation planning to epidemic modeling. However, issues must be overcome to best serve these use cases: The data often represent a limited sample of the population and use of the data jeopardizes privacy.
To address these issues, we present and evaluate a system for generating synthetic mobility data using a deep recurrent neural network (RNN) which is trained on real location data. The system takes a population distribution as input and generates mobility traces for a corresponding synthetic population.
Related generative approaches have not solved the challenges of capturing both the patterns and variability in individuals' mobility behaviors over longer time periods, while also balancing the generation of realistic data with privacy. Our system leverages RNNs' ability to generate complex and novel sequences while retaining patterns from training data. Also, the model introduces randomness used to calibrate the variation between the synthetic and real data at the individual level. This is to both capture variability in human mobility, and protect user privacy.
Location based services (LBS) data from more than 22,700 mobile devices were used in an experimental evaluation across utility and privacy metrics. We show the generated mobility data retain the characteristics of the real data, while varying from the real data at the individual level, and where this amount of variation matches the variation within the real data.
BERT4Traj: Transformer Based Trajectory Reconstruction for Sparse Mobility Data
University of Georgia（佐治亚大学）；Department of Geography University of Georgia（佐治亚大学地理系）；College of Public Health University of Georgia（佐治亚大学公共卫生学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
REPLAY: Modeling Time-Varying Temporal Regularities of Human Mobility for Location Prediction over Sparse Trajectories
State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau（物联网智能城市国家重点实验室和计算机与信息科学系，澳门大学）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；Bern University of Applied Sciences（伯尔尼应用科学大学）；University of Fribourg（弗里堡大学）
FloGAN: Scenario-Based Urban Mobility Flow Generation via Conditional GANs and Dynamic Region Decoupling
Future Cities Laboratory Global（未来城市实验室全球）；Singapore-ETH Centre（新加坡-ETH中心）；College of Computing and Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）；Department of Civil Engineering and Engineering Mechanics（土木工程与工程力学系）；Columbia University（哥伦比亚大学）
Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion
Department of Civil and Environmental Engineering, University of California, Los Angeles（加州大学洛杉矶分校土木与环境工程系）；Novateur Research Solutions（Novateur研究解决方案）；Department of Civil and Environmental Engineering, University of California, Berkeley（加州大学伯克利分校土木与环境工程系）
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）
Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release
McGill University（麦吉尔大学）；CentraleSupélec（巴黎中央理工-高等电力学院）；CNRS（法国国家科学研究中心）；Université Paris Sud（巴黎第十一大学）；Montreal Institute for Learning Algorithms (Mila)（蒙特利尔学习算法研究所）；Université de Montréal（蒙特利尔大学）
