# An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City

arXiv: https://arxiv.org/abs/2311.06110 · HTML: https://mlsanity.com/paper/2311.06110/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Modeling bike counts in a bike-sharing system considering the effect of weather conditions](https://mlsanity.com/paper/2006.07563/) (2020)

## Built on by (most cited first, 1 of 1)

- [Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques](https://mlsanity.com/paper/2402.00654/) (2024)

## Abstract (arXiv)

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.