A Map-Aware Destination Prediction Model for Location-Based Consumer Electronics

With the rapid development of smart devices and mobile Internet, Location-based Consumer Electronics (LCE) increasingly rely on accurate destination prediction to support location-aware services. However, existing destination prediction methods often rely on recurrent architectures or incorporate unfiltered map information, which can limit both prediction efficiency and accuracy. To address these issues, we propose a Map-Aware Full Attention Model (MFAM) tailored for LCE devices, featuring an encoder-only full attention network optimized for real-time trajectory prediction. The main novelty of MFAM lies in a trajectory-conditioned Map-Aware Attention mechanism, which uses the observed trajectory to selectively identify and aggregate spatial map regions that are relevant to the destination prediction task, rather than directly incorporating the entire map. In addition, we introduce a sufficient grid-size criterion for AOI-based map representation to reduce the risk of losing small but informative geographic regions. These designs enable MFAM to exploit map information while maintaining a compact model structure and efficient inference. Experimental results on real-world trajectory datasets show that MFAM achieves a maximum Top-5 accuracy of 84.3% and 78.9% on two datasets, surpassing state-of-the-art methods. We further evaluate MFAM in real-world consumer electronics scenarios, demonstrating its robustness across different urban environments and transportation modes.