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

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

Abstract

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.

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

Abstract

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.

Paper Structure

This paper contains 43 sections, 17 equations, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Trajectory Data Mining
  4. Video Generation
  5. Reinforcement Learning from Verifiable Reward
  6. Preliminaries
  7. Problem Defination
  8. Reinforcement Learning on Flow-based Model
  9. Methodology
  10. Pair Data Collection
  11. Data sources.
  12. Cross-system linkage
  13. Map-visual rendering for video supervision.
  14. SFT Initialization
  15. Trajectory-aware reinforcement learning from verifiable rewards
  16. ...and 28 more sections

Figures (8)

  • Figure 1: Overview of the Sig2GPS problem: coarse cellular signaling records are mapped to high-precision GPS trajectories via video generation.
  • Figure 2: Illustration of Think Over Trajectory. We highlight the difference between previous seq2seq methods, VLM-based methods and our proposed methods.
  • Figure 3: Overall architecture of our Think Over Trajectory framework for Sig2GPS. We first fine-tune the video generator on paired signaling-trajectory videos, and then apply trajectory-aware reinforcement learning from verifiable reward (RLVR) to further improve fidelity under map and temporal constraints.
  • Figure 4: Three Examples of Our Roll Out.
  • Figure 5: Ablation results on Sig2GPS.
  • ...and 3 more figures