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

Unsupervised Temporal Clustering to Monitor the Performance of Alternative Fueling Infrastructure