A spatial–Temporal Large Language Model with Denoising Diffusion Implicit for predictions in centralized multimodal transport systems

Authors

• Zhiqi Shao
• Haoning Xi
• Haohui Lu
• Ze Wang
• Michael G.H. Bell
• Junbin Gao

Document Type

Journal Article

Publication Date

2025

Subject Area

place - australasia, place - urban, planning - methods, planning - travel demand management, operations - traffic

Keywords

Spatial-temporal prediction, Denoising Diffusion Implicit Model (DDIM), Large Language Model (LLM), Multimodal transport systems

Abstract

Centralized multimodal transport systems face significant challenges due to data isolation, missing values, and heterogeneous spatial–temporal features, which hinder accurate prediction in traffic flow and travel demand. To address these challenges, we propose Spatial–Temporal Large Language Model with Denoising Diffusion Implicit (STLLM-DF), an innovative which integrates a Spatial–Temporal Denoising Diffusion Implicit Model (ST-DDIM) with a Spatial–Temporal Large Language Model (ST-LLM) to improve the predictions in traffic flow and travel demand in multimodal transport systems. The ST-DDIM effectively learns data distributions to recover noisy and incomplete data, while the ST-LLM captures complex spatial–temporal dependencies across multimodal networks, eliminating manual feature engineering. Extensive experiments conducted on ten real-world datasets from Sydney demonstrate that STLLM-DF consistently outperforms baseline models in both single-task and multi-task predictions (e.g., ), while consistently excelling in short-term and long-term predictions. On average, STLLM-DF achieves improvements in Mean Absolute Error (MAE) by 2.40%, Root Mean Square Error (RMSE) by 4.50%, and Mean Absolute Percentage Error (MAPE) by 1.51%. Furthermore, we evaluate the noise tolerance of STLLM-DF, demonstrating its robust performance under data imperfections. This paper presents a scalable, data-driven solution for managing multimodal transport systems, offering actionable insights for transport regulators.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part C Home Page:

http://www.sciencedirect.com/science/journal/0968090X

Recommended Citation

Shao, Z., Xi, H., Lu, H., Wang, Z., Bell, M. G., & Gao, J. (2025). A spatial–Temporal Large Language Model with Denoising Diffusion Implicit for predictions in centralized multimodal transport systems. Transportation Research Part C: Emerging Technologies, 179, 105249.