Andrea Dalla Torre (Author), P. Gallo (Author), Donatella Gubiani (Author), Chris Marshall (Author), Angelo Montanari (Author), Federico Pittino (Author), Andrea Viel (Author)

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;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UNG - University of Nova Gorica
UDC: 004.8
COBISS: 5404411 Link will open in a new window
ISSN: 1009-5020
Views: 2694
Downloads: 95
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

URN: URN:SI:UNG
Type (COBISS): Not categorized
Pages: str. 89-106
Volume: ǂVol. ǂ22
Issue: ǂno. ǂ2
Chronology: 2019
DOI: 10.1080/10095020.2019.1616933
ID: 11156296