Human Mobility Modelling: Exploration and Preferential Return Meet the Gravity Model

Luca Pappalardo, Salvatore Rinzivillo, Filippo Simini

Research output: Contribution to journalSpecial issue (Academic Journal)peer-review

14 Citations (Scopus)
336 Downloads (Pure)

Abstract

Abstract Modeling the properties of individual human mobility is a challenging task that has received increasing attention in the last decade. Since mobility is a complex system, when modeling individual human mobility one should take into account that human movements at a collective level influence, and are influenced by, human movement at an individual level. In this paper we propose the d-EPR model, which exploits collective information and the gravity model to drive the movements of an individual and the exploration of new places on the mobility space. We implement our model to simulate the mobility of thousands synthetic individuals, and compare the synthetic movements with real trajectories of mobile phone users and synthetic trajectories produced by a prominent individual mobility model. We show that the distributions of global mobility measures computed on the trajectories produced by the d-EPR model are much closer to empirical data, highlighting the importance of considering collective information when simulating individual human mobility.
Original languageEnglish
Pages (from-to)934-939
Number of pages6
JournalProcedia Computer Science
Volume83
Early online date12 May 2016
DOIs
Publication statusPublished - 2016

Bibliographical note

Special Issue: The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016) / The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016) / Affiliated Workshops

Keywords

  • Human Mobility
  • Data Science
  • Mobility Modeling

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