Large-scale trajectory data similarity query method based on multistage index structure

Abstract

The invention discloses a large-scale trajectory data similarity query method based on a multi-level index structure, and belongs to the field of urban traffic big data processing and application. The method is divided into an index establishment stage and a trajectory similarity query stage. In the index establishment stage, data preprocessing is firstly carried out on original trajectory data, a grid index is established for the trajectory data obtained after preprocessing based on a spatial grid index idea, and grid division is carried out on a trajectory data set through the grid index. Secondly, the feature information of each trajectory is represented by constructing a feature trajectory, a start-stop index is established for the start point and the end point of each trajectory in the space grid, and a feature point index is established according to the feature trajectory point of each trajectory; therefore, a feature trajectory formed by the trajectory points with the trajectory feature information is applied to the multi-level index structure. Finally, a multi-level index structure consisting of the grid index, the start-stop index and the feature point index is established.

Description

technical field [0001] The invention belongs to the field of urban traffic big data processing and application, and specifically relates to a large-scale trajectory data similarity query method based on a multi-level index structure. Background technique [0002] In recent years, with the development of satellite positioning technology, mobile phones, GPS and other mobile devices, a large amount of trajectory data is generated every day. Trajectory data contains huge value. By mining trajectory data, it can serve different types of applications in daily life. Since trajectories exist in every corner of our lives, abundant trajectory data resources also bring a huge demand for trajectory data research. Trajectory data is a kind of spatiotemporal data, which contains spatial information and time attributes, and the spatial position of the trajectory will change dynamically over time. Trajectory data has the characteristics of unstructured, time-sensitive and large-scale. Ho...

AI Technical Summary

Problems solved by technology

Compared with Simba, DITA can replace the trajectory calculation by selecting the axis value points in the trajectory, and reduce the calculation overhead by reducing the number of trajectory points participating in the trajectory similarity calculation. However, in the process of selecting axis value points, DITA only calculates the trajectory The distance between points is considered, and the selected axis value points cannot effectively represent the complete trajectory. Participate in the distance measurement between trajectory points and the selection of candidate trajectory sets in the trajectory similarity calculation.

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