While some studies have shown positive results, forecasting individual mobility still presents a significant challenge endeavor owing to the high variability and intricacy of personal movement patterns.The challenge lies in grasping the complex spatial and temporal relationships, as well as the ever-changing patterns of individual movement.We investigate the latent multi-scale structure of personalized mobility behaviors through multi-granular multiple timescales patterns, we introduce the Spatio-Temporal Multi-Granularity Gated Recurrent Transformer Model(STMGGRT), which integrates multi-granularity structural encoding with spatio-temporal data within a Gated Recurrent Transformer framework. The Gated Recurrent Transformer layer effectively captures intricate sequential patterns and long-range dependencies, which are essential for accurate mobility prediction. Through stacked multiple levels of modules, our method is able to reveal hidden multi-level patterns within mobility data, leading to improved prediction performance. Comprehensive evaluations across three publicly available datasets confirm that STMGGRT significantly surpasses current state-of-the-art methods in accuracy as well as efficiency, highlighting its robustness and effectiveness in predicting future mobility locations.