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Location data collected from mobile devices represent mobility behaviors atindividual and societal levels. These data have important applications rangingfrom transportation planning to epidemic modeling. However, issues must beovercome to best serve these use cases: The data often represent a limitedsample of the population and use of the data jeopardizes privacy.  To address these issues, we present and evaluate a system for generatingsynthetic mobility data using a deep recurrent neural network (RNN) which istrained on real location data. The system takes a population distribution asinput and generates mobility traces for a corresponding synthetic population.  Related generative approaches have not solved the challenges of capturingboth the patterns and variability in individuals' mobility behaviors overlonger time periods, while also balancing the generation of realistic data withprivacy. Our system leverages RNNs' ability to generate complex and novelsequences while retaining patterns from training data. Also, the modelintroduces randomness used to calibrate the variation between the synthetic andreal data at the individual level. This is to both capture variability in humanmobility, and protect user privacy.  Location based services (LBS) data from more than 22,700 mobile devices wereused in an experimental evaluation across utility and privacy metrics. We showthe generated mobility data retain the characteristics of the real data, whilevarying from the real data at the individual level, and where this amount ofvariation matches the variation within the real data.

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