2018
Journal article
Open Access
Data-driven generation of spatio-temporal routines in human mobility
Pappalardo L., Simini F.
Statistics and Probability (physics.data-an) I.6.5 Spatiotemporal data Statistics - Other Statistics Article Data science Urban dynamics Computer Networks and Communications FOS: Physical sciences Human mobility Computer Science - Social and Information Networks FOS: Computer and information sciences Computer Science Applications Physics - Physics and Society Big data Statistics and Probability H.2.8 Mathematical modelling Machine Learning (cs.LG) Physics and Society (physics.soc-ph) Smart cities Social and Information Networks (cs.SI) Information Systems Computer Science - Learning Complex systems Other Statistics (stat.OT) Mobile phone data Human dynamics Data Analysis Physics - Data Analysis Mathematical modeling GPS data
The generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals' recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present Ditras (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. Ditras operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate Ditras with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating Ditras with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility.
Source: Data mining and knowledge discovery 32 (2018): 787–829. doi:10.1007/s10618-017-0548-4
Publisher: Kluwer Academic Publishers, Dordrecht ;, Stati Uniti d'America
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BibTeX entry
@article{oai:it.cnr:prodotti:385727,
title = {Data-driven generation of spatio-temporal routines in human mobility},
author = {Pappalardo L. and Simini F.},
publisher = {Kluwer Academic Publishers, Dordrecht ;, Stati Uniti d'America},
doi = {10.1007/s10618-017-0548-4 and 10.48550/arxiv.1607.05952},
journal = {Data mining and knowledge discovery},
volume = {32},
pages = {787–829},
year = {2018}
}
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