Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic

Songhua Hua, Chenfeng Xiong, Zhanqin Liu, Lei Zhang

The COVID-19 pandemic has led to a globally unprecedented change in human mobility. Leveraging two-year bike-sharing trips from the largest bike-sharing program in Chicago, this study examines the spatiotemporal evolution of bike-sharing usage across the pandemic and compares it with other modes of transport. A set of generalized additive (mixed) models are fitted to identify relationships and delineate nonlinear temporal interactions between station-level daily bike-sharing usage and various independent variables including socio-demographics, land use, transportation features, station characteristics, and COVID-19 infections.

Data

• All_final_Divvy_R2019_1015.7z: features to build GAM

Code

• [N]1-Trips_Data_Prepare.py: Finish the data preprocessing.
• [N]2.5-Mode compare.py: Compare three different modes: Driving; Transit; Bikesharing, and Total mobility
• [N]2-Feature_Engineering.py: Build features for GAMs.
• [N]3-Response_Engineering.py: Compute Y variables for GAMs
• [N]4-Model_Build.R: Finish the GAM model fit.
• [N]4.5-Single-Plot-GAM.R: Nonlinear interaction plot.
• [N]3.5-GeoPlot.py: Spatial plot.

Results

Nonlinear interactions between time index and different independent variables of interest regarding the cumulative relative changes.

Spatial patterns of Cumulative relative changes by July 31st, 2020.