  Zero Emission Vehicles (ZEV) play an important role in the decarbonization of
the transportation sector. For a wider adoption of ZEVs, providing a reliable
infrastructure is critical. We present a machine learning approach that uses
unsupervised temporal clustering algorithm along with survey analysis to
determine infrastructure performance and reliability of alternative fuels. We
illustrate this approach for the hydrogen fueling stations in California, but
this can be generalized for other regions and fuels.
