Ship trajectory clustering method based on curve length distance

Abstract

The invention discloses a ship trajectory clustering method based on a curve length distance, and relates to the technical field of ship navigation trajectory analysis. The method mainly comprises the steps: obtaining a weighted length after the normalization of a curve length between a tail end trajectory point in a current trajectory segment and a head end trajectory point of a corresponding ship trajectory; according to the proportion of the weighted length of the current tail end trajectory point in the corresponding ship trajectory, obtaining trajectory points at the same weighted length proportion in other ship trajectories through a linear interpolation method to serve as paired trajectory points; calculating comprehensive similarity measurement according to the navigation data of the current tail end track point and the corresponding paired track point; obtaining a clustering result through a DBSCAN clustering method; and extracting a ship typical channel. According to the method, physical information contained in the channel is mined through multi-dimensional data in an existing ship AIS, the track clusters are formed through clustering of ship tracks to represent ship behavior characteristics, and therefore the track clustering method small in difference interference and low in complexity can be achieved.

Description

technical field [0001] The invention relates to the technical field of ship navigation track analysis, in particular to a ship track clustering method based on curve length and distance. Background technique [0002] With the globalization and integration of the economy, waterway transportation has become one of the most important methods in the comprehensive transportation system at home and abroad. With the development of the shipbuilding industry and the improvement of shipbuilding technology, ships are showing a trend of large-scale, specialized, and high-speed development. The density of water navigation has also increased significantly, and the waterway traffic environment has become more complex. raised higher demands. [0003] Automatic Identification System (AIS) is a new type of digital navigation aid system and equipment. With the help of multi-dimensional navigation information in AIS data, it can not only capture the coordinate information of the ship’s continu...

AI Technical Summary

Problems solved by technology

However, in practical applications, Hausdorff will select the maximum value of all distances, and outliers in the sequence may greatly affect the performance of the clustering algorithm. Therefore, for ordered sequences, Hausdorff distance is not a suitable similarity measure method.

On the other hand, although Frechet can avoid the shortcomings of the Hausdorff method and can maintain the sequence order of the data on the curve, Frechet has a high computational complexity and is prone to overflow in the algorithm stack or the number of recursive layers exceeds the limit. Not suitable for cluster analysis of long trajectory segments

Not only that, Frechet is very sensitive to noise, and outliers in the data will have a great impact on the clustering results

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