# Unsupervised Temporal Clustering to Monitor the Performance of Alternative Fueling Infrastructure

## TL;DR

This paper introduces an unsupervised temporal clustering method combined with survey analysis to evaluate the performance and reliability of alternative fueling infrastructure, exemplified by hydrogen stations in California.

## Contribution

It presents a novel unsupervised clustering approach integrated with survey data analysis for assessing fueling infrastructure performance.

## Key findings

- Effective identification of infrastructure performance patterns
- Applicable to various regions and fuel types
- Provides a scalable framework for infrastructure monitoring

## Abstract

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.