To better understand dockless bike-sharing (DBS) usage and advance the knowledge on shared bicycle service, this study empirically investigated the riding behavior in the time and space dimensions based on multisource datasets. Taking Central Business District (CBD) and Beijing West Railway Station (BWRS) as study areas, this study analyzed and compared the DBS usage based on the traffic grid between the two study areas. Furthermore, the random forest (RF) model was applied to investigate the contribution of influencing factors on origin/ destination and origin–destination pair trip volume. Partial Dependence Plots (PDP) analysis was conducted to explore the nonlinear effects of influencing factors. Results show considerable variation across different scenarios. Variables such as government agencies, restaurants, bus stop distance, and metro distance show nonlinear and threshold effects on DBS usage. The findings offer valuable insights for urban infrastructure development and bike rebalancing strategies, and the formulation of green and sustainable transportation policies.