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

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.

ST-SSMs: Spatial-Temporal Selective State of Space Model for Traffic Forecasting

Accurate and efficient traffic prediction is crucial for planning, management, and control of intelligent transportation systems. Most state-of-the-art methods for traffic prediction effectively predict both long-term and short-term by employing spatio-temporal neural networks as prediction models, together with transformers to learn global information on prediction objects (e.g., traffic states of road segments). However, these methods often have a high computational cost to obtain good performance. This paper introduces an innovative approach to traffic flow prediction, the Spatial-Temporal Selective State Space Model (ST-SSMs), featuring the novel ST-Mamba block, which can achieve good prediction accuracy with less computational cost. A comparative analysis highlights the ST-Mamba layer's efficiency, revealing its equivalence to three attention layers, yet with markedly reduced processing time. Through rigorous testing on diverse real-world datasets, the ST-SSMs model demonstrates exceptional improvements in prediction accuracy and computational simplicity, setting new benchmarks in the domain of traffic flow forecasting