Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy

@article{Berke2022GeneratingSM,
  title={Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy},
  author={Alex Berke and Ronan Doorley and Kent Larson and Esteban Moro Egido},
  journal={Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing},
  year={2022}
}
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… 

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