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1.
From Trajectory Modeling to Social Habits and Behaviors Analysis
Donatella Gubiani, Marco Pavan, 2016, independent scientific component part or a chapter in a monograph

Abstract: In recent years, the widespread of mobile devices has made easier and popular the activities of recording locations visited by users and of inferring their trajectories. The availability of such large amount of spatio-temporal data opens new challenges to automatically extract information and get valuable knowledge. The many aspects of this issue have aroused the interest of researchers in several areas, such as information retrieval, data mining, context-aware computing, security and privacy issues, urban planning, and transport management. Recently, there has been a strong interest in understanding how people move during their common daily activities in order to get information about their behaviors and habits. In this paper we describe considerable recent research works related to the analysis of mobile spatio-temporal data, focusing on the study of social habits and behaviors. We provide a general perspective on studies on human mobility by depicting and comparing methods and algorithms, highlighting some critical issues related to information extraction from spatio-temporal data, and future research directions.
Found in: ključnih besedah
Keywords: Trajectory modeling, Social habits and behaviors, Spatio-temporal data, Data mining
Published: 18.11.2016; Views: 3573; Downloads: 0
.pdf Fulltext (652,73 KB)

2.
A map-matching algorithm dealing with sparse cellular fingerprint observations
Andrea Viel, Federico Pittino, Angelo Montanari, Chris Marshall, Donatella Gubiani, Paolo Gallo, Andrea Dalla Torre, 2019, original scientific article

Abstract: The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations.
Found in: ključnih besedah
Summary of found: ...the problem of reconstructing the most likely trajectory of a device on the road network...
Keywords: Map-matching algorithm, trajectory, cellular fingerprint, Hidden Markov Model
Published: 11.06.2019; Views: 2170; Downloads: 72
.pdf Fulltext (3,93 MB)

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