In this paper we propose a map matching method to overcoming the limitations of standard best-match recon- struction strategies. We use a more flexible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the successive analysis to be performed such as KNN and clustering

K-BestMatch reconstruction and comparison of trajectory data

Nanni M;Trasarti R
2009

Abstract

In this paper we propose a map matching method to overcoming the limitations of standard best-match recon- struction strategies. We use a more flexible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the successive analysis to be performed such as KNN and clustering
2009
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Inglese
International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-09) In Cooperation with IEEE ICDM 2009
International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-09) In Cooperation with IEEE ICDM 2009
610
615
978-1-4244-5384-9
http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5360417&isYear=2009&count=114&page=4&ResultStart=100
IEEE
New York
STATI UNITI D'AMERICA
Sì, ma tipo non specificato
6 December 2009
Miami, Florida, USA
Spatio-temporal data
map matching
clustering
2
restricted
Nanni, M; Trasarti, R
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/58571
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