| dc.description.abstract | This paper aims to create accurate predictive models for the bike-sharing system
operated by Oslo City Bikes. The three different machine learning methods are
used to predict user demand within a specified area of Oslo. Furthermore, the
paper intends to discover which factors that most influence bike-sharing usage preto
mid-pandemic. Different factors of bike-sharing systems will be evaluated to
create a reliable model. Recommendations for further research topics, as well as
possible business implementations for the model will be explained. The machine
learning method with the best performance was GRU with an MAE score of 14.30,
RMSE of 20.80 and R����� of 0.77. Multiple COVID-19 features indicating varying
intensities of lockdown were tested, however they did not have as much of an
effect as expected. | en_US |
