Author: Vorona-Slivinskaya, Lubov; Koryakov, Alexey; Semenycheva, Irina; Druzyanova, Varvara
Title: Demand dynamics for hydrocarbon fuels during the COVIDâ€19 pandemic Cord-id: spjrwngn Document date: 2021_6_7
ID: spjrwngn
Snippet: This work is aimed at analyzing demand dynamics for hydrocarbon fuels from March 2020 and developing a forecast model for the near future. Based on the method of artificial neural network with feedforward and backpropagation learning, a model is proposed for forecasting oil demand and passenger mobility during a pandemic for the United States, Russia, and India. The results of the calculations showed that road and air transport dynamics strongly depend on the application of measures to limit and
Document: This work is aimed at analyzing demand dynamics for hydrocarbon fuels from March 2020 and developing a forecast model for the near future. Based on the method of artificial neural network with feedforward and backpropagation learning, a model is proposed for forecasting oil demand and passenger mobility during a pandemic for the United States, Russia, and India. The results of the calculations showed that road and air transport dynamics strongly depend on the application of measures to limit and ban and the level of COVIDâ€19 incidence in a country. The proposed method can be used to make forecasts during pandemics and unforeseen situations to regulate the price policy for hydrocarbon fuels and the safety of passenger traffic in the future.
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