Representation learning is key in trajectory analysis, affecting downstream task performance. To reduce computational overhead from numerous trajectory points, spatial partitioning of the road network is often used. Existing spatial partitioning methods primarily include grid-based partitioning and road network partitioning, which aim to achieve a more uniform distribution of trajectory points. However, after partitioning, these methods typically employ one-hot encoding to represent subregions, failing to capture the spatial proximity relationships between adjacent subregions. Moreover, due to variations in road network structures, existing trajectory representation learning methods often require complete retraining when adapting to new maps, resulting in high computational costs. This paper proposes a Partition Transition Probability and Pretraining Graph Attention Network (PTP-PGAT). The model integrates a road network partitioning strategy and incorporates a partition transition probability mechanism to capture spatial proximity between sub-regions effectively. Additionally, leveraging the hierarchical partition tree structure of road network partitioning, PTP-PGAT supports a pretraining-finetuning transfer learning paradigm. After pretraining on one dataset, the model can efficiently adapt to new datasets with minimal fine-tuning. Experiments show PTP-PGAT outperforms baselines, improving accuracy by up to 15.5% and retaining over 90% performance in cross-region transfer while cutting training time by 35%.