Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Abstract

Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people’s resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020.

Research summary

Combines pandemic scenarios, mobility data and machine learning to project US motor-gasoline demand during COVID-19.

Cite this work

@article{mobilitydemand2020,
  title = {{Machine learning model to project the impact of COVID-19 on US motor gasoline demand}},
  author = {Shiqi Ou and Xin He and Weiqi Ji and Wei Chen and Lang Sui and Yu Gan and Zifeng Lu and Zhenhong Lin and Sili Deng and Steven Przesmitzki and Jessey Bouchard},
  year = {2020},
  journal = {Nature Energy},
  volume = {5},
  pages = {666-673},
  doi = {10.1038/s41560-020-0662-1},
  number = {9},
  url = {https://jiweiqi.github.io/papers/mobility-demand/}
}