The avalanche of mobility data like GPS and GSM daily produced by each user through mobile devices enables personalized mobility-services improving everyday life. The base for these mobility-services lies in the predictability of human behavior. In this paper we propose an approach for reproducing the user's personal mobility agenda that is able to predict the user's positions for the whole day. We reproduce the agenda by exploiting a data-driven personal mobility model able to capture and summarize different aspects of the systematic mobility behavior of a user. We show how the proposed approach outperforms typical methodologies adopted in the literature on four different real GPS datasets. Moreover, we analyze some features of the mobility models and we discuss how they can be employed as agents of a simulator for what-if mobility analysis.

There's a Path for Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas

Giannotti, Fosca;
2017-01-01

Abstract

The avalanche of mobility data like GPS and GSM daily produced by each user through mobile devices enables personalized mobility-services improving everyday life. The base for these mobility-services lies in the predictability of human behavior. In this paper we propose an approach for reproducing the user's personal mobility agenda that is able to predict the user's positions for the whole day. We reproduce the agenda by exploiting a data-driven personal mobility model able to capture and summarize different aspects of the systematic mobility behavior of a user. We show how the proposed approach outperforms typical methodologies adopted in the literature on four different real GPS datasets. Moreover, we analyze some features of the mobility models and we discuss how they can be employed as agents of a simulator for what-if mobility analysis.
Settore INF/01 - Informatica
Data Science and Advanced Analytics (DSAA), 2017 IEEE International Conference on
Tokyo, Japan
Data Science and Advanced Analytics (DSAA), 2017 IEEE International Conference on
IEEE Computer Society
978-1-5090-5004-8
Personal Mobility Data Model; Mobility Agenda Reproduction; Mobility Data Mining; Mobility Prediction; Mobility Simulation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/114508
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