Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City
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.
An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City
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.
An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City
Uddin, Majbah (Autor:in) / Hwang, Ho-Ling (Autor:in) / Hasnine, Md Sami (Autor:in)
Transportation Planning and Technology ; 46 ; 482-498
19.05.2023
17 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Business and Bikeshare User Perceptions of the Economic Benefits of Capital Bikeshare
| British Library Online Contents | 2015
Powering bikeshare in New York City: does the usage of e-bikes differ from regular bikes?
| Taylor & Francis Verlag | 2024
Exploring the Impact of Dockless Bikeshare on Docked Bikeshare—A Case Study in London
| DOAJ | 2020
Bikeshare: A Review of Recent Literature
| Online Contents | 2016