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Extracting User Behavior at Electric Vehicle Charging Stations with Transformer Deep Learning Models

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Extracting User Behavior at Electric Vehicle Charging Stations with Transformer Deep Learning Models

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dc.contributor.author Marchetto, Daniel es_ES
dc.contributor.author Ha, Sooji es_ES
dc.contributor.author Dharur, Sameer es_ES
dc.contributor.author Asensio, Omar es_ES
dc.date.accessioned 2020-07-30T10:56:11Z
dc.date.available 2020-07-30T10:56:11Z
dc.date.issued 2020-05-11
dc.identifier.isbn 9788490488324
dc.identifier.uri http://hdl.handle.net/10251/148979
dc.description.abstract [EN] Mobile 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. es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Web data es_ES
dc.subject Internet data es_ES
dc.subject Big data es_ES
dc.subject Qca es_ES
dc.subject Pls es_ES
dc.subject Sem es_ES
dc.subject Conference es_ES
dc.subject Electric Vehicles es_ES
dc.subject Mobility es_ES
dc.subject Mobile Data es_ES
dc.subject Natural Language Processing es_ES
dc.subject Transformer Models es_ES
dc.title Extracting User Behavior at Electric Vehicle Charging Stations with Transformer Deep Learning Models es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/CARMA2020.2020.11613
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Marchetto, D.; Ha, S.; Dharur, S.; Asensio, O. (2020). Extracting User Behavior at Electric Vehicle Charging Stations with Transformer Deep Learning Models. Editorial Universitat Politècnica de València. 153-162. https://doi.org/10.4995/CARMA2020.2020.11613 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics es_ES
dc.relation.conferencedate Julio 08-09,2020 es_ES
dc.relation.conferenceplace Valencia, Spain es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/CARMA/CARMA2020/paper/view/11613 es_ES
dc.description.upvformatpinicio 153 es_ES
dc.description.upvformatpfin 162 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\11613 es_ES


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