Charging stations seem to be the main deterrent for people not to choose to drive an electric vehicle. Despite the vehicle's eco-efforts, researchers believe that infrastructure fuels the challenge of installing enough charging stations. To address this issue, researchers have developed AI that can accurately identify places where there are insufficient and out-of-service stations via user reviews.
"We're spending billions of both public and private dollars on electric vehicle infrastructure," says Omar Asensio (@AsensioResearch), principal investigator and assistant professor in the School of Public Policy at the Georgia Institute of Technology. "But we really don't have a good understanding of how well these investments are serving the public and public interest."
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"When users are engaging and sharing information about charging experiences, they are often engaging in prosocial or pro-environmental behavior, which gives us rich behavioral information for machine learning," says Asensio. But compared to analyzing data tables, texts can be challenging for computers to process. "A review could be as short as three words. It could also be as long as 25 or 30 words with misspellings and multiple topics," says co-author Sameer Dharur of Georgia Institute of Technology. Users sometimes even throw smiley faces or emojis into the texts.
Findings were published in Patterns.
"That's a milestone in the transition for us to deploy these AI tools because it's no longer 'can the AI do as good as human?'" says Asensio. "In some cases, the AI exceeded the performance of human experts."
"This is a wake-up call for us because, given the massive investment in electric vehicle infrastructure, we're doing it in a way that is not necessarily attentive to the social equity and distributional issues of access to this enabling infrastructure," says Asensio. "That is a topic of discussion that's not going away and we're only beginning to understand."
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