Abstract
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 lustering 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.
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BibTeX (Download)
@incollection{Santos2012,
title = {Automated Traffic Route Identification Through the Shared Nearest Neighbour Algorithm},
author = { Maribel Yasmina Santos and Joaquim P. Silva and João Moura Pires and Monica Wachowicz},
editor = {Jérôme Gensel and Didier Josselin and Danny Vandenbroucke},
url = {http://dx.doi.org/10.1007/978-3-642-29063-3_13},
isbn = {978-3-642-29062-6},
year = {2012},
date = {2012-01-01},
booktitle = {Bridging the Geographic Information Sciences},
pages = {231-248},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Geoinformation and Cartography},
abstract = {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 lustering 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.},
keywords = {Clustering, Density-based Clustering, Motion Vectors, Movement Data},
pubstate = {published},
tppubtype = {incollection}
}