跨城市人类移动行为预测研究综述

Survey on Cross-city Human Mobility Prediction

城市化进程积累了大量记录人类移动行为的时空数据,为研究人类移动行为建模及预测提供了良好的数据基础。在智慧城市建设背景下,跨城市人类移动预测是实现城市协同管理与治理的必然要求,时常面临数据匮乏以及数据分布不平衡等问题,传统机器学习方法难以取得理想的性能。因此,将人类移动相关知识从数据丰富的源城市迁移到数据稀疏乃至稀缺的目标城市至关重要。首先概述了现有跨城市人类移动行为预测研究所使用的数据集和评价指标,随后循序渐进地讨论人类个体和群体层面的跨城市移动预测问题并分类综述各自适用的研究方法。针对人类个体跨城市移动预测,主要分析协同过滤、矩阵分解、统计学习以及深度学习这4类模型方法的应用。针对人类群体跨城市移动预测,则聚焦知识迁移和元学习这两种面向少样本机器学习方法的应用。最后,展望了跨城市人类移动行为预测领域亟需解决的重要问题。

The advancement of urbanization has accumulated massive spatio-temporal data that records human mobility,providing a favorable data foundation for human mobility modeling and prediction.In the context of smart city construction,cross-city human mobility prediction is an inevitable requirement for achieving urban collaborative management and governance.At this time,there are often problems such as data scarcity and imbalanced data distribution.Traditional machine learning methods are difficult to achieve ideal performance.Therefore,it is crucial to transfer knowledge related to human mobility from the data-rich source cities to the data-scarce target cities.This paper firstly provides an overview of the datasets and commonly used evaluation metrics used in existing studies,followed by a gradual discussion of the cross-city mobility prediction problem at the human indivi-dual-level and group-level respectively,and then categorizes the applicable research methods.For the individual-level human mo-bility prediction,the application of four types of models,i.e.,collaborative filtering,matrix factorization,statistical learning,and deep learning,are analyzed.For group-level human mobility prediction,two types of machine learning methods for few samples,i.e.,knowledge transfer and meta learning,are specifically analyzed.In the end,important issues that urgently need to be addressed in the field of cross-city human mobility prediction are prospected.