网络化移动感知将本地轨迹推断与远程地理空间上下文相结合，使通信决策成为预测系统的一部分。我们提出 GeoSplit，一种用于出行方式识别的边缘--服务器框架，该框架分离了地图证据的效用、训练时的传输以及运行时的选择性获取。在拥有 10% 标签的 GeoLife 数据集上，我们在用户-天和用户不相交分组下评估这些组件。对于未见过的用户，对齐的全地图上下文将宏 F1 分数从 0.7154 提升至 0.7864，而基于地图信息的蒸馏产生了一个仅依赖运动特征的学生模型，其宏 F1 分数为 0.7225。通过紧凑的查询接口，熵门控实现了 0.7729 的宏 F1 分数，接近全量查询的 0.7738，但仅查询了 39.5% 的轨迹并将应用负载减少了 60.5%。在用户-天分组下，选择性查询和全量查询的分数同样接近，查询率为 21.6%。学习到的价值估计和概率融合未带来一致的性能提升。这些结果展示了针对智能交通感知任务的通信高效型边缘--服务器推理。

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.