  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.
