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MPE: a mobility pattern embedding model for predicting next locations

Abstract

The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each record having at least three attributes: object ID, location ID, and time-stamp. In this paper, we propose a novel mobility pattern embedding model called MPE to shed the light on people’s mobility patterns in traffic trajectory data from multiple aspects, including sequential, personal, and temporal factors. MPE has two salient features: (1) it is capable of casting various types of information (object, location and time) to an integrated low-dimensional latent space; (2) it considers the effect of “phantom transitions” arising from road networks in traffic trajectory data. This embedding model opens the door to a wide range of applications such as next location prediction and visualization. Experimental results on two real-world datasets show that MPE is effective and outperforms the state-of-the-art methods significantly in a variety of tasks.

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Notes

  1. The detailed information about the data can be found here https://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i/data

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61572289, and the NSERC Discovery Grants.

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Correspondence to Xiaohui Yu.

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This article belongs to the Topical Collection: Special Issue on Social Computing and Big Data Applications

Guest Editors: Xiaoming Fu, Hong Huang, Gareth Tyson, Lu Zheng, and Gang Wang

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Chen, M., Yu, X. & Liu, Y. MPE: a mobility pattern embedding model for predicting next locations. World Wide Web 22, 2901–2920 (2019). https://doi.org/10.1007/s11280-018-0616-8

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  • DOI: https://doi.org/10.1007/s11280-018-0616-8

Keywords

  • Human mobility patterns
  • Embedding learning
  • Traffic trajectory data
  • Next location prediction