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Ensemble mobility predictor based on random forest and Markovian property using LBSN data

Authors
  • Araújo, Felipe1
  • Araújo, Fábio1
  • Machado, Kássio2
  • Rosário, Denis1
  • Cerqueira, Eduardo1
  • Villas, Leandro A.3
  • 1 Federal University of Pará, Rua Augusto Corrêa, 01, Belém, Pará, 66075-110, Brazil , Belém, Pará (Brazil)
  • 2 Federal University of Minas Gerais (UFMG), Minas Gerais, 13083-852, Belo Horizonte, Brazil , Minas Gerais (Brazil)
  • 3 Institute of Computing, University of Campinas, Campinas, São Paulo, 13083-852, Brazil , Campinas, São Paulo (Brazil)
Type
Published Article
Journal
Journal of Internet Services and Applications
Publisher
Springer London
Publication Date
Nov 05, 2020
Volume
11
Issue
1
Identifiers
DOI: 10.1186/s13174-020-00130-7
Source
Springer Nature
Keywords
License
Green

Abstract

The ubiquitous connectivity of Location-Based Systems (LBS) allows people to share individual location-related data anytime. In this sense, Location-Based Social Networks (LBSN) provides valuable information to be available in large-scale and low-cost fashion via traditional data collection methods. Moreover, this data contains spatial, temporal, and social features of user activity, enabling a system to predict user mobility. In this sense, mobility prediction plays crucial roles in urban planning, traffic forecasting, advertising, and recommendations, and has thus attracted lots of attention in the past decade. In this article, we introduce the Ensemble Random Forest-Markov (ERFM) mobility prediction model, a two-layer ensemble learner approach, in which the base learners are also ensemble learning models. In the inner layer, ERFM considers the Markovian property (memoryless) to build trajectories of different lengths, and the Random Forest algorithm to predict the user’s next location for each trajectory set. In the outer layer, the outputs from the first layer are aggregated based on the classification performance of each weak learner. The experimental results on the real user trajectory dataset highlight a higher accuracy and f1-score of ERFM compared to five state-of-the-art predictors.

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