arXiv:2509.00053v1 Announce Type: new
Abstract: Building a general model capable of analyzing human trajectories across different geographic regions and different tasks becomes an emergent yet important problem for various applications. However, existing works suffer from the generalization problem, ie, they are either restricted to train for specific regions or only suitable for a few tasks. Given the recent advances of multimodal large language models (MLLMs), we raise the question: can MLLMs reform current trajectory data mining and solve the problem? Nevertheless, due to the modality gap of trajectory, how to generate task-independent multimodal trajectory representations and how to adapt flexibly to different tasks remain the foundational challenges. In this paper, we propose texttt{Traj-MLLM}}, which is the first general framework using MLLMs for trajectory data mining. By integrating multiview contexts, texttt{Traj-MLLM}} transforms raw trajectories into interleaved image-text sequences while preserving key spatial-temporal characteristics, and directly utilizes the reasoning ability of MLLMs for trajectory analysis. Additionally, a prompt optimization method is proposed to finalize data-invariant prompts for task adaptation. Extensive experiments on four publicly available datasets show that texttt{Traj-MLLM}} outperforms state-of-the-art baselines by $48.05%$, $15.52%$, $51.52%$, $1.83%$ on travel time estimation, mobility prediction, anomaly detection and transportation mode identification, respectively. texttt{Traj-MLLM}} achieves these superior performances without requiring any training data or fine-tuning the MLLM backbones.
Expert Commentary: Transforming Trajectory Data Mining with Traj-MLLM
In the field of multimedia information systems, the integration of different modalities such as text and images has been a key research focus. The emergence of large language models has opened up new possibilities for analyzing complex data such as human trajectories across various geographic regions and tasks. The Traj-MLLM framework presented in this paper leverages the power of multimodal large language models to address the generalization problem in trajectory data mining.
One of the main challenges in trajectory data mining is the modality gap between raw trajectory data and textual representations. Traj-MLLM overcomes this challenge by transforming trajectories into interleaved image-text sequences, allowing for the preservation of key spatial-temporal characteristics while enabling the use of MLLMs for trajectory analysis. This approach not only enhances the interpretability of trajectory data but also provides a more comprehensive understanding of human movement patterns.
Furthermore, the proposed prompt optimization method in Traj-MLLM enables task adaptation without the need for additional training data or fine-tuning of MLLM backbones. This flexibility is crucial for real-world applications where adaptability to different tasks is essential.
In the broader context of multimedia information systems, Traj-MLLM highlights the importance of integrating multidisciplinary concepts such as natural language processing, computer vision, and spatial analysis. By bridging the gap between different modalities and leveraging the reasoning abilities of MLLMs, Traj-MLLM sets a new standard for trajectory data mining and paves the way for future research in artificial reality, augmented reality, and virtual realities.
Key Takeaways:
- Traj-MLLM leverages multimodal large language models for trajectory data mining.
- The framework transforms raw trajectories into image-text sequences for improved analysis.
- The prompt optimization method enables task adaptation without additional training data.
- Integrating multidisciplinary concepts is essential for advancing multimedia information systems.
- Traj-MLLM sets a new standard for trajectory data mining and opens up possibilities for related fields.