Real-time data from mobile platforms to evaluate sustainable transportation infrastructure

Abstract

By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to adoption. Using large-scale social data and machine-learning based on 12,720 electric vehicle (EV) charging stations, we provide national evidence on how well the existing charging infrastructure is serving the needs of the rapidly expanding population of EV drivers in 651 core-based statistical areas in the United States. We deploy supervised machine-learning algorithms to automatically classify unstructured text reviews generated by EV users. Extracting behavioural insights at a population scale has been challenging given that streaming data can be costly to hand classify. Using computational approaches, we reduce processing times for research evaluation from weeks of human processing to just minutes of computation. Contrary to theoretical predictions, we find that stations at private charging locations do not outperform public charging locations provided by the government. Overall, nearly half of drivers who use mobility applications have faced negative experiences at EV charging stations in the early growth years of public charging infrastructure, a problem that needs to be fixed as the market for electrified and sustainable transportation expands. A reliable charging infrastructure is critical to wider adoption of electric cars. With large-scale social data and machine intelligence, this study shows the importance of the quality, not just the quantity, of charging stations to consumers, suggesting policy design should include consumer data.