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Extracting User Behavior at Electric Vehicle Charging Stations with Transformer Deep Learning ModelsMobile applications have become widely popular for their ability to access real-time information. In electric vehicle (EV) mobility, these applications are used by drivers to locate charging stations in public spaces, pay for charging transactions, and engage with other users. This activity generates a rich source of data about charging infrastructure and behavior. However, an increasing share of this data is stored as unstructured text—inhibiting our ability to interpret behavior in real-time. In this article, we implement recent transformer-based deep learning algorithms, BERT and XLnet, that have been tailored to automatically classify short user reviews about EV charging experiences. We achieve classification results with a mean accuracy of over 91% and a mean F1 score of over 0.81 allowing for more precise detection of topic categories, even in the presence of highly imbalanced data. Using these classification algorithms as a pre-processing step, we analyze a U.S. national dataset with econometric methods to discover the dominant topics of discourse in charging infrastructure. After adjusting for station characteristics and other factors, we find that the functionality of a charging station is the dominant topic among EV drivers and is more likely to be discussed at points-of-interest with negative user experiences.
Detecting Behavioral Failures in Emerging Electric Vehicle Infrastructure Using Supervised Text Classification AlgorithmsThere is a growing interest in applying computational tools to the automatic discovery of social and economic behavior. For example, with decisions involving resource allocation related to public infrastructure, the ability to predict failures can allow for more efficient policy responses. In this paper, we use social data from a popular electric vehicle (EV) driver app to characterize the emerging EV charging station infrastructure. We introduce a typology of EV charging experiences collected from user reviews and deploy text classification algorithms, including convolutional neural networks (CNN), to automatically learn about potential failures. We use machine learning techniques as a pre-processing tool for econometric analyses on the quality of service delivery. After classifying the reviews into 9 main user topics and 34 subtopics, we find that the dominant issues in EV charging relate to station functionality and availability, which drive negative consumer experience. Contrary to the public discourse about EVs, range anxiety was not of large concern to existing EV drivers. Based on our findings, we move towards automated identification of failures in public charging infrastructure that can significantly reduce research evaluation costs through relatively simple computational solutions.