计算机与数字工程2025，Vol.53Issue(11)：3149-3154,6.DOI:10.3969/j.issn.1672-9722.2025.11.028
基于多图时空注意力网络的出行需求预测
Travel Demand Prediction Based on Multi-graph Spatio-temporal Attention Network
摘要
Abstract
Passenger demand prediction is a crucial but challenging task for intelligent transportation system construction.In this paper,a multi-graph attention spatiotemporal prediction model is proposed.LSTM is used to model the context information of temporal dependence in travel records,and then three graphs are used to model multiple correlations of spatial regions,and GAT is used to capture the spatial dependencies between regions.In addition,weather information is integrated into the model to realize the global prediction of travel demand.Finally,a real data set is used to verify the validity of the proposed model.关键词
乘客出行需求预测/智能交通/时空预测Key words
passenger demand prediction/intelligent transportation/spatio-temporal prediction分类
信息技术与安全科学引用本文复制引用
李瑞蒙,王蒙,马毓哲..基于多图时空注意力网络的出行需求预测[J].计算机与数字工程,2025,53(11):3149-3154,6.基金项目
陕西省自然科学基础研究计划"基于多元特征学习的电力现货市场日前电价预测研究"(编号:2023-JC-YB-558)资助. （编号:2023-JC-YB-558）
