Repozitorij Univerze v Novi Gorici

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Naslov:A map-matching algorithm dealing with sparse cellular fingerprint observations
Avtorji:Dalla Torre, Andrea (Avtor)
Gallo, Paolo (Avtor)
Gubiani, Donatella (Avtor)
Marshall, Chris (Avtor)
Montanari, Angelo (Avtor)
Pittino, Federico (Avtor)
Viel, Andrea (Avtor)
Datoteke:.pdf A_map_matching_algorithm_dealing_with_sparse_cellular_fingerprint_observations.pdf (3,93 MB)
 
Jezik:Angleški jezik
Vrsta gradiva:Delo ni kategorizirano (r6)
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:UNG - Univerza v Novi Gorici
Opis: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.
Ključne besede:Map-matching algorithm, trajectory, cellular fingerprint, Hidden Markov Model
Leto izida:2019
Št. strani:18
Številčenje:2, 22
COBISS_ID:5404411 Povezava se odpre v novem oknu
URN:URN:SI:UNG:REP:C5IDSXWZ
DOI:10.1080/10095020.2019.1616933 Povezava se odpre v novem oknu
Licenca:CC BY-NC 4.0
To delo je dosegljivo pod licenco Creative Commons Priznanje avtorstva-Nekomercialno 4.0 Mednarodna
Število ogledov:228
Število prenosov:2
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Gradivo je del revije

Naslov:Geo-spatial Information Science
Založnik:Taylor and Francis
ISSN:1009-5020
Leto izida:2019

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