论文标题
通过动态模式分解对人群的迁移进行建模
Modeling Population Human Mobility with Dynamic Mode Decomposition
论文作者
论文摘要
人类流动性研究涉及时空个体和人口运动。对人类流动性的准确建模和预测可以提供监控，管理和优化人类运动以改善社会经济利益的机会。在本文中，我们采用动态模式分解算法，使用2019年至2021年在美国不同州之间的访客流数据来对人类流动性进行建模[1]。我们训练具有不同排名结构不同的多个DMD模型，并评估其在新型测试数据上的建模准确性和可预测性。
Human mobility research concerns spatiotemporal individual and population movement. Accurate modeling and prediction of human mobility can provide opportunities to monitor, manage and optimize human movement for improved social-economic benefit. In this paper, we adopt the dynamic mode decomposition algorithm to model population human mobility using visitor flow data between different states in the United States from 2019 to 2021 [1]. We train multiple DMD models with different low rank structures, and evaluate their modeling accuracy and predictability on novel testing data.
