Abstract

The influx of people attending sport events involves creating wealth in the environment where they are held. To understand the impacts of these events on the host community, it is necessary to analyze the main explanatory variables in relation to models of buying behavior in tourism, so as to know which variables are the ones that affect most the expenditure done by attendees at a sport event. Therefore, the objective of our research is to present a model to evaluate the influence that certain variables related to the model of consumer behavior have on the expenditure. For that, the variables have been grouped into those related to the personal characteristics of the attendee, and into those others of subjective nature related to the perceptions of those attending a sport event. The research has been based on the use of Structural Equation Models using the technique of Partial Least Square (PLS). The sport event that has been analyzed is the Padel tournament “Cáceres International Open”. The proposed model shows significant results to support the relationships contained in the hypotheses, and provides positive data regarding the predictive relevance of the model.

Abstract

Resumen de la comunicación [EN] The work presented here demonstrates the potential of new generation data arising from innovative policies (based on persuasive technologies) in the transport sector. Improved understanding of the spatial distribution of health impacts arising from the introduction of new travel initiatives will support more targeted and efficient policy development across both the transport and health sectors. Typical health impacts include those arising from changes in levels of personal activity with alternative mode choices. With a sectoral approach to policy development, positive impacts for one sector (i.e. improved transport services) may be negated by dis-benefits in another (e.g. low levels of active travel choice and increased obesity related disease burden). The horizontal notion of equity (Thomopoulos, Grant-Muller and Tight, 2009) is applied using a range of transport-related health outcomes including cancer, heart disease and depression. The research methodology interfaces new generation ‘Track and Trace’ information on individuals location and mode choices (detected as mobile phone app-based sensor data) with a new integrated transport and health model (IHITM), finally calculating an equity indicator based on distributional impacts. Grant-Muller, S.; Hodgson, F.; Harrison, G.; Malleson, N.; Redfern, T.; Snowball, R. (2018). Transport-Health Equity Outcomes from mobile phone location data – a case study. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 256-256. https://doi.org/10.4995/CARMA2018.2018.8349 OCS 256 256

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. 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 OCS 153 162