Trajectory similarity calculation method based on interpolation and integration

Abstract

The invention relates to a trajectory similarity calculation method based on interpolation and integration. The trajectory similarity calculation method comprises the following steps: 1) mapping trajectory sequence points to a large space with the same measurement based on a complete bijection function; 2) performing interpolation fitting on the mapped track sequence points by using a Lagrangian interpolation function to obtain a fitting curve; and 3) on the basis of the two contents, calculating similar attributes of the track sequence by applying an integration method. The method has the beneficial effects that the problems of low accuracy of a traditional point matching algorithm, weak resistance to the non-uniform sampling rate of noise and the efficiency of a deep learning algorithm are solved. A method of combining Lagrangian interpolation with integration is provided, a dynamic calculation method of a point matching algorithm is abandoned, and a static calculation method is adopted. A similarity attribute is defined, the similarity attribute is embedded into each trajectory, the similarity of any two trajectories is prevented from being repeatedly calculated, and the time complexity of similarity calculation is greatly reduced.

Description

technical field [0001] The invention relates to the field of trajectory similarity calculation methods, in particular to a trajectory similarity calculation method based on interpolation and integration. Background technique [0002] Trajectory similarity calculation has been widely studied due to its wide application prospects. So far, many research results have been achieved. Application prospects. Due to the development of the Internet of Things, GPS embedded devices are also widely used, making trajectory data easier to collect and greatly promoting the development of trajectory similarity research. However, the accuracy of trajectory similarity calculation is limited due to the inherent positioning bias of positioning equipment and the objective reality of uneven sampling rates. [0003] Most of the traditional trajectory similarity calculation methods are based on the idea of ​​point matching, which are highly interpretable and easy to operate, but the accuracy rate ...

AI Technical Summary

Problems solved by technology

However, due to the inherent positioning deviation of the positioning equipment and the objective reality of uneven sampling rate, the accuracy of trajectory similarity calculation is limited

[0003] Most of the traditional trajectory similarity calculation methods are based on point matching ideas, which are highly interpretable and easy to operate, but in many application scenarios, the accuracy rate is not high, and the re...

Images