2024/09/08 by Haoning Xi, Shao, Zhiqi, Haohui Lu +8 · 1 citation
Decision Sciences · Engineering · Social Sciences · #Artificial Intelligence (cs.AI) #Data Quality and Management #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #I.2.1 #I.2.7 #Machine Learning (cs.LG) #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2409.05921
openalex publication_date 2024/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.