The cruising of ride-hailing vehicles generates negative externalities such as traffic congestion and vehicular emissions. These externalities can be mitigated by reducing cruising driving via operating book-ahead ride-hailing services, where the platform dispatches and routes drivers based on precise information on travelers’ departure time and origin–destination (OD). However, the effects of factors influencing book-ahead ride-hailing trips have rarely been empirically examined with real data. Using six-month trip data from China, this study employs a gradient boosting decision tree (GBDT) method with hyperparameters optimized by the Bayesian optimization algorithm to examine the factors associated with book-ahead ride-hailing trips across OD pairs (hexagon cells-to-hexagon cells) at various spatial scales. The relative importance rankings generated from this study indicate that trip features, weather conditions, and accessibility to transportation hubs are significant determinants correlated with the usages of book-ahead ride-hailing. The partial dependence plots demonstrate the nonlinear threshold effects of these determinants on the hourly number of book-ahead ride-hailing per OD pair. Moreover, this study compares the differences in associations between peak and non-peak hours as well as weekdays and weekends. The disparity in the nonlinear threshold effects between weekdays and weekends is only observable during the evening peak period and not at other times. These findings provide valuable insights into developing practical strategies for promoting book-ahead ride-hailing services.