Скачать или смотреть 05-26-2023 ITS Weekly Seminar: E-Bikes’ Effect on Mode and Route Choice: A Case Study of Richmond...

Full Title:
E-Bikes’ Effect on Mode and Route Choice: A Case Study of Richmond, VA Bike Share

Speaker(s);
Celeste Chavis, Associate Professor, jointly appointed in the Departments of Transportation & Urban Infrastructure Studies and Civil Engineering at Morgan State University

Abstract;
Bikesharing systems across the United States are embracing electric-assist bikes (e-bikes) in their fleet of docked and dockless bikes to address physical and mobility barriers to biking. E-bikes allow riders to traverse upgrades and accelerate with less exertion. Despite this known benefit, there is a lack of understanding in how travel behavior changes after the introduction of e-bikes. Utilizing GPS data for the RVA Bikeshare system, this study analyzed differences in bike use among e-bike and regular bike users. A total of 4,075 trips were collected during the first week of each month from April 2019 to December 2019. Using Mapbox’s Map Matching API, GPS data was mapped to road segments in Open Street Maps (OSM). This study did a comprehensive descriptive analysis, origin-destination trip analysis, and user cluster analysis with the retrieved data. The results have shown that e-bikes are generally associated with longer trip distances, shorter trip times, higher speeds and lower elevations. In April, about 25% of the fleet were e-bikes and by December, approximately 65%. The t-tests results showed that the mean number of trips made per bike available was significantly more (~3.2x) for e-bikes compared to bikes (p-value=0.004). Longer-term memberships (annual, monthly) were found to be associated with significantly higher use of e-bikes than shorter-term memberships, potentially pointing to a lack of knowledge on the part of those who use the system less frequently or to a preference for normal bicycles. Finally, the user cluster analysis identified six diverse types of behaviors that varied by geographical region (e.g., central Richmond vs. recreational areas), as well as by trip distance, trip duration, and bike type.