An Interpretable Machine Learning Framework to Understand Bikeshare
  Demand before and during the COVID-19 Pandemic in New York City
Majbah Uddin Ho-Ling Hwang Md Sami Hasnine
In recent years, bikesharing systems have become increasingly popular as
affordable and sustainable micromobility solutions. Advanced mathematical
models such as machine learning are required to generate good forecasts for
bikeshare demand. To this end, this study proposes a machine learning modeling
framework to estimate hourly demand in a large-scale bikesharing system. Two
Extreme Gradient Boosting models were developed: one using data from before the
COVID-19 pandemic (March 2019 to February 2020) and the other using data from
during the pandemic (March 2020 to February 2021). Furthermore, a model
interpretation framework based on SHapley Additive exPlanations was
implemented. Based on the relative importance of the explanatory variables
considered in this study, share of female users and hour of day were the two
most important explanatory variables in both models. However, the month
variable had higher importance in the pandemic model than in the pre-pandemic
model.