Georgia Tech Team Uses Machine Learning to Drive Electric Vehicle Policy Findings

A new study from the Georgia Institute of Technology School of Public Policy harnesses machine learning techniques to provide the best insight yet into the attitudes of electric vehicle (EV) drivers towards the existing charger network. The study findings could help policymakers focus their efforts. In the paper, which is featured on the cover of the June 2020 issue of Nature Sustainability, a team led by Assistant Professor Omar Isaac Asensio trained a machine learning algorithm to analyze unstructured consumer data from 12,270 electric vehicle charging stations across the United States. The study demonstrates how machine learning tools can be used to quickly analyze streaming data for policy evaluation in near real-time (see sidebar). Streaming data refers to data that comes in a feed, continuously, such as user reviews from an app.