Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/19194

TitleAutomated traffic route identification through the shared nearest neighbour algorithm
Author(s)Santos, Maribel Yasmina
Silva, Joaquim P.
Pires, João Moura
Wachowicz, Monica
KeywordsMovement data
Motion vectors
Density-based clustering
Clustering
Issue dateApr-2012
PublisherSpringer
JournalLecture Notes in Geoinformation and Cartography
Abstract(s)Many organisations need to extract useful information from huge amounts of movement data. One example is found in maritime transportation, where the automated identification of a diverse range of traffic routes is a key management issue for improving the maintenance of ports and ocean routes, and accelerating ship traffic. This paper addresses, in a first stage, the research challenge of developing an approach for the automated identification of traffic routes based on clustering motion vectors rather than reconstructed trajectories. The immediate benefit of the proposed approach is to avoid the reconstruction of trajectories in terms of their geometric shape of the path, their position in space, their life span, and changes of speed, direction and other attributes over time. For clustering the moving objects, an adapted version of the Shared Nearest Neighbour algorithm is used. The motion vectors, with a position and a direction, are analysed in order to identify clusters of vectors that are moving towards the same direction. These clusters represent traffic routes and the preliminary results have shown to be promising for the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.
TypeconferencePaper
URIhttp://hdl.handle.net/1822/19194
ISBN978-3-642-29062-6
DOI10.1007/978-3-642-29063-3_13
ISSN1863-2246
Publisher versionhttp://www.springer.com/
Peer-Reviewedyes
AccessrestrictedAccess
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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