Road network hot spot region mining method

Abstract

The invention discloses a road network hot spot region mining method, belongs to the technical field of data mining and solves the problem when trajectory spatial-temporal similarity measurement and clustering calculation is adopted for trajectory clustering in the prior art. The method includes: step 1, performing trajectory segmentation on all trajectory sections, and calculating spatial-temporal similarity and spatial-temporal distance between two sub trajectory sections acquired after segmentation; step 2, performing clustering calculation on all trajectory section data in a grid space according to the spatial-temporal similarity and the spatial-temporal distance of the sub trajectories and a DBSCAN algorithm based on dynamic neighbor; step 3, selecting a remarkable class cluster set from class clusters of clustering calculation, and extracting stay spots from the remarkable class cluster set; step 4, according to the number of trajectory sections carried by the stay spots, acquiring hot regions of the stay spots, and acquiring hot spot regions in a road network from the regions where the stay spots are positioned. The method is used for spatial position locating.

Description

technical field [0001] A road network hotspot area mining method is used for spatial location positioning and belongs to the technical field of data mining. Background technique [0002] In recent years, the rapid development and application of spatial location positioning technology, along with the rapid popularization of these technologies, we can easily track the location information of almost any moving object, thus forming a huge trajectory database in the form of trajectory, these massive trajectory The data contains a large amount of deep information that can reflect certain motion behaviors of moving objects. As a kind of spatiotemporal data, spatiotemporal trajectory data mainly records the trend of the spatial position of moving objects over time, while vehicle spatiotemporal trajectory data is more special because it is limited to the road network. Therefore, the commonly used data mining methods Many of them cannot be directly applied to spatio-temporal trajecto...

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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that in the prior art, when trajectory clustering is performed using trajectory spatiotemporal similarity measure and clustering calculation, when the amount of trajectory data is large, the response time of the spatiotemporal similarity measure is relatively large; European coordinates cannot be accurately expressed The distance between two trajectories in the road network; and when the density clustering algorithm DBSCAN is used for clustering calculation, it is necessary to artificially input the neighborhood radius and neighborhood density threshold. When the value is inaccurate, it will directly affect the clustering result. Problem; The present invention provides a method for excavating hotspot areas of the road network

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