Predicting human mobility with activity changes

Wei Huang, Songnian Li, Xintao Liu, Yifang Ban

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)

Abstract

Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy (R2) derived from the data with and without implementing the activity change detection. The results show that the R2 is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies.
Original languageEnglish
Pages (from-to)1569-1587
Number of pages19
JournalInternational Journal of Geographical Information Science
Volume29
Issue number9
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes

Keywords

  • activity change
  • human mobility prediction
  • Markov chain
  • spatial-temporal pattern
  • spatio-temporal clustering

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

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