  We describe customized synthetic datasets for publishing mobility data.
Private companies are providing new transportation modalities, and their data
is of high value for integrative transportation research, policy enforcement,
and public accountability. However, these companies are disincentivized from
sharing data not only to protect the privacy of individuals (drivers and/or
passengers), but also to protect their own competitive advantage. Moreover,
demographic biases arising from how the services are delivered may be amplified
if released data is used in other contexts.
  We describe a model and algorithm for releasing origin-destination histograms
that removes selected biases in the data using causality-based methods. We
compute the origin-destination histogram of the original dataset then adjust
the counts to remove undesirable causal relationships that can lead to
discrimination or violate contractual obligations with data owners. We evaluate
the utility of the algorithm on real data from a dockless bike share program in
Seattle and taxi data in New York, and show that these adjusted transportation
datasets can retain utility while removing bias in the underlying data.
