A probabilistic approach is proposed, which adopts filter-refinement framework for query processing. First, all objects that possibly satisfy a query are retrieved as candidate results. Then, probabilities that the candidates will satisfy the query are evaluated based on a probability model proposed in the paper. Finally, a user defined minimum probability threshold is used to filter unqualified candidates to get a final predictive result. The future location of a moving object is defined as a random variable in the probability model. Two modes are proposed to describe object’s movement status in spatio-temporal query range, and the corresponding methods are presented to compute the probability that an object will satisfy the query in the proposed modes. A trajectory analyzing algorithm is proposed to estimate the probability density functions (PDF) from the historical trajectories. An index structure is designed to efficiently support the storing and accessing of the PDFs. The experimental result shows that the proposed solution can effectively process the predictive spatio-temporal range query and improve the correctness of the predictive results. It is suitable for processing the query with small spatial range and long-term future time interval.