Computer Science ›› 2025, Vol. 52 ›› Issue (1): 102-119.doi: 10.11896/jsjkx.240100032

• Database & Big Data & Data Science • Previous Articles     Next Articles

Survey on Cross-city Human Mobility Prediction

ZHANG Yusong1, XU Shuai1,2, YAN Xingyu1, GUAN Donghai1, XU Jianqiu1   

  1. 1 College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
    2 State Key Laboratory for Novel Software Technology(Nanjing University),Nanjing 210023,China
  • Received:2024-01-02 Revised:2024-07-01 Online:2025-01-15 Published:2025-01-09
  • About author:ZHANG Yusong,born in 2000,postgraduate.His main research interests include geo-social networks and so on.
    XU Shuai,born in 1991,Ph.D,associate professor,master supervisor,is a member of CCF(No.D4709M).His main research interests include temporal-spatial data mining and smart city.
  • Supported by:
    National Natural Science Foundation of China(62302213,61972198),Natural Science Foundation of Jiangsu Province,China(BK20210280) and Fundamental Research Funds for the Central Universities(NS2022089).

Abstract: 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 humanmo-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.

Key words: Cross-city, Human mobility, Spatio-Temporal data, Transfer learning, Deep learning

CLC Number: 

  • TP181
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