Automated Traffic Route Identification Through the Shared Nearest Neighbour Algorithm
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 lusteringmotion vectors rather thanreconstructed trajectories.The immediate benefit of the proposed approach is to avoid the reconstruction of trajectoriesin 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 promisingfor the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.
KeywordsMovement data Motion vectors Clustering Density-based clustering
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We would like to thank the Maritime Research Institute in the Netherlands, for making the data available for analysis under the MOVE EU Cost Action IC0903 (Knowledge Discovery from Moving Objects).
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