TEMPT: Trajectory Mixing Decomposition for Low-resource GPS Trajectory Prediction

Trajectory prediction plays a pivotal role in diverse applications, including intelligent transportation, urban planning, and autonomous navigation. However, prevailing trajectory prediction methods, particularly Transformer-based approaches demand substantial computational resources, making them unsuitable for resource-constrained environments like edge devices, mobile platforms, and embedded systems. To address this challenge, we propose TEMPT (Trajectory Mixing Decomposition), a lightweight yet highly accurate framework for GPS trajectory prediction. TEMPT employs a decomposition-inspired stacked architecture with lightweight MLP-Mixer blocks to extract multi-scale trajectory components, capturing both coarse and fine-grained patterns. Additionally, a dynamic weighted aggregation mechanism fuses adaptive patch representations into a unified embedding space, further enabling efficient handling of variable-length inputs. Extensive experiments on four trajectory datasets show that TEMPT achieves state-of-the-art accuracy while reducing computational costs by 56$\%$ compared to conventional approaches. It further sustains real-time single-trajectory inference on one CPU thread, so a dedicated accelerator is not required at deployment time. The source code and trained models will be made publicly available.