In future networks, Radio Resource Management (RRM) could benefit from Geo-Localized Measurements (GLM) thanks to the Minimization of Drive Testing (MDT) feature introduced in Long Term Evolution (LTE). Such measurements can be processed by the network and be used to optimize its performance. The purpose of this paper a is to use GLM to significantly improve scheduling. We introduce the concept of forecast scheduler for users in high mobility that exploit GLM. It is assumed that a Radio Environment Map (REM) can provide interpolated Signal to Interference plus Noise Ratio (SINR) values along the user trajectories. The diversity in the mean SINR values of the users during a time interval of several seconds allows to achieve a significant performance gain. The forecast scheduling is formulated as a convex optimization problem namely the maximization of an α−fair utility function of the cumulated rates of the users along their trajectories. Numerical results for thee different mobility scenarios illustrate the important performance gain achievable by the forecast scheduler. Index Terms—Forecast scheduler, alfa-fair, high mobility, Minimizing Drive Tests, MDT, Radio Environment Maps, REM, geo-localized measurements