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Collective Prediction of Individual Mobility Traces for Users with Short Data History

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  • Bartosz Hawelka
  • Izabela Sitko
  • Pavlos Kazakopoulos
  • Euro Beinat

Abstract

We present and test a sequential learning algorithm for the prediction of human mobility that leverages large datasets of sequences to improve prediction accuracy, in particular for users with a short and non-repetitive data history such as tourists in a foreign country. The algorithm compensates for the difficulty of predicting the next location when there is limited evidence of past behavior by leveraging the availability of sequences of other users in the same system that provide redundant records of typical behavioral patterns. We test the method on a dataset of 10 million roaming mobile phone users in a European country. The average prediction accuracy is significantly higher than that of individual sequence prediction algorithms, primarily constant order Markov models derived from the user’s own data, that have been shown to achieve high accuracy in previous studies of human mobility. The proposed algorithm is generally applicable to improve any sequential prediction when there is a sufficiently rich and diverse dataset of sequences.

Suggested Citation

  • Bartosz Hawelka & Izabela Sitko & Pavlos Kazakopoulos & Euro Beinat, 2017. "Collective Prediction of Individual Mobility Traces for Users with Short Data History," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-14, January.
  • Handle: RePEc:plo:pone00:0170907
    DOI: 10.1371/journal.pone.0170907
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    Cited by:

    1. Angela Chantre-Astaiza & Laura Fuentes-Moraleda & Ana Muñoz-Mazón & Gustavo Ramirez-Gonzalez, 2019. "Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability," Sustainability, MDPI, vol. 11(17), pages 1-32, August.
    2. Alessandro Crivellari & Euro Beinat, 2020. "LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists," Sustainability, MDPI, vol. 12(1), pages 1-18, January.
    3. Kimitaka Asatani & Fujio Toriumi & Junichiro Mori & Masanao Ochi & Ichiro Sakata, 2018. "Detecting interpersonal relationships in large-scale railway trip data," Journal of Computational Social Science, Springer, vol. 1(2), pages 313-326, September.

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