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Traj-MLLM: Can Multimodal Large Language Models Reform Trajectory Data Mining?

Created by
  • Haebom

Author

Shuo Liu, Di Yao, Yan Lin, Gao Cong, Jingping Bi

Outline

This paper addresses the challenge of building a general model capable of analyzing human movement paths across diverse regions and tasks. Existing research has been limited to training in specific regions or being suitable only for a small number of tasks. To address this challenge, we propose the Traj-MLLM framework, leveraging a multimodal large-scale language model (MLLM). Traj-MLLM integrates multi-view contexts to transform raw path data into image-text sequences and leverages the inference capabilities of MLLM to perform path analysis. Furthermore, we propose a prompt optimization technique that generates data-invariant prompts for task adaptation. Experimental results show that Traj-MLLM outperforms the existing best-performing models by 48.05%, 15.52%, 51.52%, and 1.83% on travel time prediction, mobility prediction, anomaly detection, and transportation mode identification tasks, respectively. Traj-MLLM does not require fine-tuning the MLLM backbone or training data.

Takeaways, Limitations

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Takeaways:
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We present a human movement path analysis model that can be generalized to various regions and tasks using a multimodal large-scale language model (MLLM).
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Overcoming the limitations of existing models, it achieves excellent performance in various tasks such as travel time prediction, mobility prediction, anomaly detection, and transportation mode identification.
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Leverage MLLM's inference capabilities to achieve performance improvements without separate training data or model fine-tuning.
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Improve task adaptability by creating data-invariant prompts using prompt optimization techniques.
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Limitations:
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It depends on the performance of MLLM, and the limitations of MLLM may affect the performance of Traj-MLLM .
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Because of the heavy reliance on prompt engineering, designing optimal prompts can be challenging.
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Further validation of generalization performance on different types of path data is needed.
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The size and computational cost of the MLLM used can be high.
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