An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City
Author:
Affiliation:
1. Oak Ridge National Laboratory, National Transportation Research Center, Oak Ridge, TN, USA
2. Department of Civil and Environmental Engineering, Howard University, Washington, DC, USA
Publisher
Informa UK Limited
Subject
Transportation,Geography, Planning and Development
Link
https://www.tandfonline.com/doi/pdf/10.1080/03081060.2023.2201280
Reference18 articles.
1. Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review
2. Modeling bike counts in a bike-sharing system considering the effect of weather conditions
3. Exploring the health and spatial equity implications of the New York City Bike share system
4. Sustainable mobility in auto-dominated Metro Boston: Challenges and opportunities post-COVID-19
5. Bike-share rebalancing strategies, patterns, and purpose
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