深思轨迹：利用视频生成技术从蜂窝信号中重建GPS轨迹

Think over Trajectories: Leveraging Video Generation to Reconstruct GPS Trajectories from Cellular Signaling

March 27, 2026

作者: Ruixing Zhang, Hanzhang Jiang, Leilei Sun, Liangzhe Han, Jibin Wang, Weifeng Lv

cs.AI

摘要

移动设备持续与蜂窝基站交互，产生海量信令记录，为理解人类移动行为提供了广覆盖的数据基础。然而此类记录仅能提供粗略的位置线索（如服务小区标识符），因此难以直接应用于需要高精度GPS轨迹的场景。本文研究Sig2GPS问题：从蜂窝信令数据重建GPS轨迹。受领域专家常将信令轨迹映射至地图并勾勒对应GPS路线的启发，与传统依赖复杂多阶段工程流水线或坐标回归的解决方案不同，Sig2GPS被重新定义为直接在地图可视化域操作的图像-视频生成任务：将信令轨迹渲染于地图上，通过训练视频生成模型绘制连续GPS路径。为支撑该范式，本研究构建了配对的信令-轨迹视频数据集用于微调开源视频模型，并引入基于轨迹感知强化学习的优化方法，通过奖励机制提升生成保真度。在大规模真实数据集上的实验表明，该方法较现有工程化方案及学习基线均有显著提升，而后续GPS预测实验进一步验证了其可扩展性与跨城市迁移能力。总体而言，这些结果表明地图可视化视频生成为轨迹数据挖掘提供了实用接口，能够在地图约束下直接生成并优化连续路径。

English

Mobile devices continuously interact with cellular base stations, generating massive volumes of signaling records that provide broad coverage for understanding human mobility. However, such records offer only coarse location cues (e.g., serving-cell identifiers) and therefore limit their direct use in applications that require high-precision GPS trajectories. This paper studies the Sig2GPS problem: reconstructing GPS trajectories from cellular signaling. Inspired by domain experts often lay the signaling trace on the map and sketch the corresponding GPS route, unlike conventional solutions that rely on complex multi-stage engineering pipelines or regress coordinates, Sig2GPS is reframed as an image-to-video generation task that directly operates in the map-visual domain: signaling traces are rendered on a map, and a video generation model is trained to draw a continuous GPS path. To support this paradigm, a paired signaling-to-trajectory video dataset is constructed to fine-tune an open-source video model, and a trajectory-aware reinforcement learning-based optimization method is introduced to improve generation fidelity via rewards. Experiments on large-scale real-world datasets show substantial improvements over strong engineered and learning-based baselines, while additional results on next GPS prediction indicate scalability and cross-city transferability. Overall, these results suggest that map-visual video generation provides a practical interface for trajectory data mining by enabling direct generation and refinement of continuous paths under map constraints.