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 hu-man 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 met-rics 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 ad-dressed in the field of cross-city human mobility prediction are prospected.
万方数据
维普
