A spatial–temporal dynamic attention-based Mamba model for multi-type passenger demand prediction in multimodal public transit systems

作者：Zhiqi Shao, Haoning Xi, David A. Hensher, Ze Wang, Xiaolin Gong, Junbin Gao · 发表于：Transportation Research Part E Logistics and Transportation Review · 年份：2025 · DOI：10.1016/j.tre.2025.104282 · 被引用次数：13 · 研究领域：Human Mobility and Location-Based Analysis、Traffic Prediction and Management Techniques、Transportation Planning and Optimization

Predicting passenger demand across multiple socio-demographic groups, such as adults, seniors, pensioners, and students, is essential for improving the operational efficiency, equity, inclusivity, and sustainability of multimodal public transit (PT) systems. Traditional demand prediction models, however, often fail to effectively capture the complex spatial–temporal variability inherent in heterogeneous socio-demographic groups. To bridge this gap, we propose a novel spatial–temporal dynamic attention-based state–space model, i.e., STDAtt-Mamba , tailored for multi-type passenger demand prediction in multimodal PT systems. The proposed STDAtt-Mamba model consists of three key components: an adaptive embedding layer that integrates station-level, passenger-type-specific, and temporal embeddings into a unified representation for efficient data processing; a spatial–temporal dynamic attention ( STDAtt ) module that employs sparse attention mechanisms to selectively capture crucial global spatial–temporal dynamics; and a spatial–temporal dynamic Mamba ( STDMamba ) module that extends the state–space modeling framework to fuse spatial and temporal dependencies dynamically. We prove that STDAtt-Mamba is a kind of spatial–temporal dual-path attention mechanism and theoretically validate the complementarity of STDMamba and STDAtt in capturing local and global dependencies, thereby improving the interpretability of the proposed STDAtt-Mamba . Extensive experiments are conducted on a l...