青岛大学学报（自然科学版）2025，Vol.38Issue(3)：50-56,7.DOI:10.3969/j.issn.1006-1037.2025.03.08
基于Tucker分解和季节性自回归移动平均模型的出租车出行需求预测
Taxi Travel Demand Forecasting Based on Tucker Decomposition and Seasonal Autoregressive Integrated Moving Average Model
摘要
Abstract
To enhance the accuracy and efficiency of taxi trip demand forecasting,a model combining Tucker decomposition and seasonal autoregressive moving average model was proposed.The spatiotemporal modeling of the core tensor after Tucker decomposition was carried out to better capture the internal multi-modal structure and spatiotemporal correla-tion of taxi travel demand,so as to improve the prediction ability of the model.The tensor representation of taxi demand data was constructed,the Tucker decomposition was used to extract core features,and the seasonal autoregressive integrated moving average model was used for forecasting.Experimental results show that the proposed method has better accuracy and computational efficiency compared with the baseline model.关键词
出行需求预测/Tucker分解/季节性自回归移动平均模型Key words
travel demand forecasting/tucker decomposition/seasonal autoregressive inte-grated moving average model分类
信息技术与安全科学引用本文复制引用
楚本嘉,颜鸿宇,李建波..基于Tucker分解和季节性自回归移动平均模型的出租车出行需求预测[J].青岛大学学报（自然科学版）,2025,38(3):50-56,7.基金项目
国家自然科学基金联合基金重点项目(批准号:U22B2057)资助. （批准号:U22B2057）
