On the properties of human mobility
| Autor: | Matteo Zignani, Michela Papandrea, Gian Paolo Rossi, Silvia Giordano, Sabrina Gaito, Karim Keramat Jahromi |
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| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
Ubiquitous computing Point of interest Computer Networks and Communications business.industry Computer science Inference Distribution (economics) 020206 networking & telecommunications 02 engineering and technology 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Relevance (information retrieval) business Set (psychology) Simulation |
| Zdroj: | Computer Communications. 87:19-36 |
| ISSN: | 0140-3664 |
| DOI: | 10.1016/j.comcom.2016.03.022 |
| Popis: | Visited locations are classified in 3 main categories according to their relevance.People visit regularly just few places where they spend most of their time.People commute between places based on their time (not spatial) distance.HOME and WORK places are in the set of few places mostly visited.Mostly visited places semantic inference is based on user mobility/behavior analysis. The current age of increased people mobility calls for a better understanding of how people move: how many places does an individual commonly visit, what are the semantics of these places, and how do people get from one place to another. We show that the number of places visited by each person (Points of Interest - PoIs) is regulated by some properties that are statistically similar among individuals. Subsequently, we present a PoIs classification in terms of their relevance on a per-user basis. In addition to the PoIs relevance, we also investigate the variables that describe the travel rules among PoIs in particular, the spatial and temporal distance. As regards the latter, existing works on mobility are mainly based on spatial distance. Here we argue, rather, that for human mobility the temporal distance and the PoIs relevance are the major driving factors. Moreover, we study the semantic of PoIs. This is useful for deriving statistics on people's habits without breaking their privacy. With the support of different datasets, our paper provides an in-depth analysis of PoIs distribution and semantics; it also shows that our results hold independently of the nature of the dataset in use. We illustrate that our approach is able to effectively extract a rich set of features describing human mobility and we argue that this can be seminal to novel mobility research. |
| Databáze: | OpenAIRE |
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