Newest papers

GeoSplit: Selective Edge--Server Map Acquisition for Cross-User Travel-Mode Recognition

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

Chinese

网络移动传感器与遥远的地理空间背景相结合,使通信决策成为预测系统的一部分。我们介绍了GeoSplit,一个边缘服务器框架,用于旅行模式识别,分离地图证据的实用性,其训练时间传输,以及其在运行时的选择性收购。在GeoLife上,有10%的标签,我们在用户日和用户分离的组合下评估这些组件。对于未见的用户来说,对齐的全地图背景将宏F1从0.7154提高到0.7864,而地图信息的蒸则在0.7225时产生一个动作仅有的学生。通过紧凑的查询界面,传达了0.7729个宏F1和07738的任务,对于所有查询,查询39.5%

Machine translation (Meta M2M100); the English original follows.

Abstract

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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