Method and system for clustering movement tracks of vehicle objects in road network space

Abstract

The invention provides a method and system for clustering movement tracks of vehicle objects in road network space. The method includes acquiring positioning data of different vehicles on a road in real time, wherein the positioning data includes longitude data, latitude data and course angle data; converting the longitude data, the latitude data and the course angle data into projection coordinates including x-axis coordinates, y-axis coordinates and course angles; performing network gridding on the projection coordinates, obtaining a plurality of partitions and marking the partitions; dispersing data in each partition into a plurality of sub partitions according to the course angle data, obtaining the maximal radius of neighborhood through calculation and obtaining an E domain through calculation based on the maximal radius of neighborhood; performing clustering in the E domain through a DBSCAN algorithm and obtaining a clustering result. According to the invention, based on mass vehicle positioning data information, through improvement of the DBSCAN algorithm, course angles are added into the algorithm, so that the clustering effect can be improved substantially.

Description

technical field [0001] The invention relates to the field of big data processing, in particular to the field of a method and a system for clustering moving trajectories of vehicle objects in road network space. Background technique [0002] Directed density clustering is a method of mining traffic road network information from massive floating vehicle trajectories. Mining traffic geographic information (road network topology) based on floating vehicle trajectory data generally adopts the method of spatial clustering. Clustering is the The data set is divided into multiple meaningful clusters according to certain rules. The similarity in the same cluster is high, and the similarity between different clusters is low. Common clustering methods include hierarchical clustering, partition clustering, and grid clustering. classes, density methods, etc. [0003] Based on the characteristics of floating car data, the density method is used for clustering here. The density clustering...

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Problems solved by technology

[0004] Therefore, the defect in the prior art is: the existing clustering algorithm based on the track data of the floating car only considers the latitude and longitude data in the track data of the floating car, resulting in a large gap between the clustering result and the expected effect, which is not ideal

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