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.

翻译：移动设备持续与蜂窝基站交互，产生海量信令记录，为理解人类移动性提供了广泛覆盖。然而，这类记录仅能提供粗略的位置线索（如服务小区标识），因此在需要高精度GPS轨迹的应用中直接使用受限。本文研究Sig2GPS问题：从蜂窝信令重构GPS轨迹。受领域专家常将信令轨迹在地图上绘制并勾勒对应GPS路径的启发，不同于依赖复杂多阶段工程管道或回归坐标的传统解决方案，Sig2GPS被重新定义为一项图像到视频的生成任务，直接在地图可视化域中操作：信令轨迹被渲染在地图上，并训练视频生成模型绘制连续的GPS路径。为支持这一范式，构建了配对信令到轨迹视频数据集以微调开源视频模型，并引入了一种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。基于大规模真实数据集的实验表明，该方法相较于强大的工程方法和基于学习基线有显著改进，而关于下一步GPS预测的额外结果则展示了其可扩展性和跨城市迁移能力。总体而言，这些结果表明，地图可视化视频生成通过在地图约束下直接生成和优化连续路径，为轨迹数据挖掘提供了一种实用接口。