青岛大学学报（自然科学版）2026，Vol.39Issue(1)：16-27,12.DOI:10.3969/j.issn.1006-1037.2026.01.04
基于GAT-Attention-Mamba组合模型的轨道交通短时客流预测
Short-term Passenger Flow Prediction in Urban Rail Transit Based on a GAT-Attention-Mamba Hybrid Model
摘要
Abstract
Traditional methods for short-term passenger flow prediction in urban rail tran-sit often rely on a single feature or a single spatial correlation graph.To address these lim-itations,multiple associated features such as weather,air quality,and temporal proximity were incorporated.A short-term passenger flow prediction method based on the GAT-At-tention-Mamba hybrid model was proposed.A multi-branch structure was employed to enhance the correlation between temporal and spatial features of stations.A two-layer Graph Attention Network(GAT)was utilized to capture the dependencies from three types of relational graphs:Origin-Destination based passenger travel graph,spatial dis-tance-based station graph,and passenger flow similarity-based station graph.The fused relational features were subsequently fed into the Attention mechanism and parallel Mam-ba blocks to capture temporal dependencies.The Attention module strengthens the repre-sentation of temporal features,while the Mamba module focuses on the deep modeling of sequential patterns.Experimental results show that,compared with baseline models,the proposed method achieves a 16.3%～19.7%reduction in MAE and a 22.6%～32.9%re-duction in RMSE under 10～30 min forecasting horizons,validating its effectiveness in multi-feature fusion and spatiotemporal dynamic modeling.关键词
智能交通/短时客流预测/组合模型/轨道交通客流Key words
intelligent transportation/short-time passenger flow prediction/hybrid model/urban rail transit passenger flow分类
交通工程引用本文复制引用
李京娜,秦利燕,王颖,王丙雨..基于GAT-Attention-Mamba组合模型的轨道交通短时客流预测[J].青岛大学学报（自然科学版）,2026,39(1):16-27,12.基金项目
国家自然科学基金(批准号:51978592)资助 （批准号:51978592）
福建省自然科学基金(批准号:RCS2021K003)资助. （批准号:RCS2021K003）
