Authors: Xueting Jin*, Arizona State University, Daoqin Tong, Arizona State University, Zhongju Zhang, Arizona State University
Topics: Spatial Analysis & Modeling
Keywords: Shared Mobility, Dockless bike Sharing, Demand Estimation, Beijing
Session Type: Paper
Start / End Time: 1:10 PM / 2:50 PM
Room: Roosevelt 4, Marriott, Exhibition Level
Presentation File: No File Uploaded
Shared mobility is having transformative impacts on travelers’ mobility choices. Among the shared mobility applications, dockless bike-sharing has received much attention recently given its efficiency as an alternative mode of transportation for short-distance urban travel and the impacts on environment and health. Different from traditional docked-based bike share programs, this new service provides users the flexibility to use a smart phone app to locate and unlock a bike nearby, and ride it to the destination where she parks and locks the bike. Since its introduction, the adoption of dockless bike is rapid and the usage is steadily increasing. However, supply and demand imbalance is very common in a bike sharing system due to the uneven demand for the service across space and time. Such a problem is more serious in dockless systems as there is no restriction on where a user uses or parks a bike. Therefore, accurate demand characterization and forecasting is critical to understand the spatio-temporal demand variation and improve the system efficiency. Although various methods have been explored to predict the demand of a docked bike sharing system, relative limited research has examined the dockless system. This study proposes a dynamic dockless bike sharing demand model to analyze service demand in space and time using dockless bike trip-level data in Beijing. Our findings offer important insights into the efficiency improvement of dockless bike sharing and have the potential to contribute to sustainable transportation.