STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting

• URL: http://arxiv.org/abs/2409.05921v1
• Date: Sun, 8 Sep 2024 15:29:27 GMT
• Title: STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting
• Authors: Zhiqi Shao, Haoning Xi, Haohui Lu, Ze Wang, Michael G. H. Bell, Junbin Gao,
• Abstract summary: We propose the Spatial-Temporal Large Language Model (STLLM-DF) to improve multi-task transportation prediction. The DDPM's robust denoising capabilities enable it to recover underlying data patterns from noisy inputs. We show that STLLM-DF consistently outperforms existing models, achieving an average reduction of 2.40% in MAE, 4.50% in RMSE, and 1.51% in MAPE.
• Score: 32.943673568195315
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid advancement of Intelligent Transportation Systems (ITS) presents challenges, particularly with missing data in multi-modal transportation and the complexity of handling diverse sequential tasks within a centralized framework. To address these issues, we propose the Spatial-Temporal Large Language Model Diffusion (STLLM-DF), an innovative model that leverages Denoising Diffusion Probabilistic Models (DDPMs) and Large Language Models (LLMs) to improve multi-task transportation prediction. The DDPM's robust denoising capabilities enable it to recover underlying data patterns from noisy inputs, making it particularly effective in complex transportation systems. Meanwhile, the non-pretrained LLM dynamically adapts to spatial-temporal relationships within multi-modal networks, allowing the system to efficiently manage diverse transportation tasks in both long-term and short-term predictions. Extensive experiments demonstrate that STLLM-DF consistently outperforms existing models, achieving an average reduction of 2.40\% in MAE, 4.50\% in RMSE, and 1.51\% in MAPE. This model significantly advances centralized ITS by enhancing predictive accuracy, robustness, and overall system performance across multiple tasks, thus paving the way for more effective spatio-temporal traffic forecasting through the integration of frozen transformer language models and diffusion techniques.

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