Data Science and Google Mobility Reporting for Fuel Demand Modeling
DOI:
https://doi.org/10.24215/15146774e012Keywords:
Fuel demand, Google Mobility, Time series, Transportation and Logistics, Machine LearningAbstract
The recent global pandemic situation prompted the generation of open mobility reports, an initiative created by Google in support of health policies associated with COVID-19. The significant change in the flow of vehicles during the pandemic situation and the variation in fuel consumption associated with transportation and various productive activities required the creation of new predictive models related to an unusual data set (e.g., geolocation of rivers). Proper manipulation and analysis of these data provide a forecast that improves the traditional forecast of fuel demand associated with actual consumption. In this paper we analyze the databases of fuel sales (gasoline and diesel), available and open in official websites and information of the company YPF. The results show a positive correlation between the variables related to the demand for these fuels and the Google mobility records, with some particularities. The programming language used for the development of the visualization code, geostatistics, predictive calculation and research reports is "R".
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Copyright (c) 2023 Irma No, Julián Tornillo, Guadalupe Pascal, Aixa Maldonado
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