Zepu Wang, Luyao Niu, Ruolin Li, Jeff, Ban · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26707 · cited by 0 works (OpenAlex) · open-access PDF
Networked mobility sensing pairs local trajectory inference with remote geospatial context, making communication decisions part of the prediction system. We present GeoSplit, a edge-server framework for travel-mode recognition that separates the utility of map evidence, its training-time transfer, and its selective acquisition at runtime. On GeoLife with 10% labels, we evaluate these components under user-day and user-disjoint grouping. For unseen users, aligned full-map context raises macro-F1 from 0.7154 to 0.7864, while map-informed distillation yields a motion-only student at 0.7225. Through a compact query interface, entropy giving achieves 0.7729 macro-F1 versus 0.7738 for all queries, query
Machine translation (Meta M2M100); the English original follows.
Networked mobility sensing couples local trajectory inference with remote geospatial context, making communication decisions part of the prediction system. We present GeoSplit, an edge--server framework for travel-mode recognition that separates the utility of map evidence, its training-time transfer, and its selective acquisition at runtime. On GeoLife with 10% labels, we evaluate these components under user-day and user-disjoint grouping. For unseen users, aligned full-map context raises macro-F1 from 0.7154 to 0.7864, while map-informed distillation yields a motion-only student at 0.7225. Through a compact query interface, entropy gating achieves 0.7729 macro-F1 versus 0.7738 for all queries, querying 39.5% of trajectories and reducing application payload by 60.5%. Under user-day grouping, selective and all-query scores are similarly close at a 21.6% query rate. Learned value estimation and probability fusion offer no consistent improvement. These results illustrate communication-efficient edge--server inference for an intelligent transportation sensing task.
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Topics: Computer science, Inference, Artificial intelligence, Geospatial analysis, Trajectory, Context (archaeology)
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