Vehicle trajectory clustering method based on parallel ST-AGNES algorithm

Abstract

The invention discloses a vehicle trajectory clustering method based on a parallel ST-AGNES algorithm, and the method is characterized in that multiple processing units are utilized to work together,and a certain task is executed at the same time to achieve higher performance. Parallel computing is an effective means for improving the computing speed and the processing capacity of a computer system. Therefore, the invention provides the vehicle trajectory clustering method based on the parallel ST-AGNES algorithm from a brand-new perspective, and the vehicle trajectory time and space similarity measurement method based on dynamic time warping better solves the problems of different vehicle trajectory sampling rates and inconsistent time scales. The method comprises the steps: constructinga time and space similarity matrix of vehicle tracks; studying a splitting scheme of irregular trajectory data and a communication mechanism of parallel computing, wherein one of the object-orientedtrajectory data splitting schemes is an object-oriented trajectory data splitting scheme; carrying out clustering connection based on the minimum distance, and designing a parallel STAGNES algorithm oriented to vehicle trajectory data.

Description

technical field [0001] The invention relates to the field of vehicle trajectory algorithms, in particular to a vehicle trajectory clustering method based on a parallel ST-AGNES algorithm. Background technique [0002] Trajectory data is the data information obtained by sampling the motion process of one or more moving objects in the space-time environment. There are a lot of trajectory data in the real world, such as animal migration trajectories, vehicle trajectories, hurricane trajectories, etc. With the rapid development of communication technology and positioning technology, it is possible to effectively monitor moving objects in real time and obtain massive trajectory data. While large-scale trajectory data brings convenience to people's lives, it also poses many new challenges to the management and application of trajectory data. There are two main aspects: data scale and data mining. The rapid increase of trajectory data has not brought about the simultaneous rapid ...

AI Technical Summary

Problems solved by technology

Although improving the performance of a single computer can speed up the calculation speed, high-performance computers are generally expensive, which is not conducive to large-scale promotion and application

For large-scale vehicle trajectory data, the serial-based trajectory spatio-temporal clustering algorithm obviously cannot meet the needs of large-scale trajectory data analysis

Images