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Title:A map-matching algorithm dealing with sparse cellular fingerprint observations
Authors:Dalla Torre, Andrea (Author)
Gallo, Paolo (Author)
Gubiani, Donatella (Author)
Marshall, Chris (Author)
Montanari, Angelo (Author)
Pittino, Federico (Author)
Viel, Andrea (Author)
Files:.pdf A_map_matching_algorithm_dealing_with_sparse_cellular_fingerprint_observations.pdf (3,93 MB)
 
Language:English
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
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.
Keywords:Map-matching algorithm, trajectory, cellular fingerprint, Hidden Markov Model
Year of publishing:2019
Number of pages:18
Numbering:2, 22
COBISS_ID:5404411 Link is opened in a new window
URN:URN:SI:UNG:REP:C5IDSXWZ
DOI:10.1080/10095020.2019.1616933 Link is opened in a new window
License:CC BY-NC 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial 4.0 International
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Record is a part of a journal

Title:Geo-spatial Information Science
Publisher:Taylor and Francis
ISSN:1009-5020
Year of publishing:2019

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