A Method for Mining Hotspot Areas of Road Network

Abstract

The invention discloses a road network hotspot area mining method, which belongs to the technical field of data mining and solves the problem in the prior art of clustering trajectories by using trajectory time-space similarity measurement and clustering calculation. Step 1 of the present invention, carry out trajectory segmentation to all trajectory segments, calculate the space-time similarity and the space-time distance between the two sub-track segments after the division; The DBSCAN algorithm clusters all trajectory segment data in the grid space; step 3, selects a significant cluster set from the cluster calculation clusters, and extracts the stay spots in the significant cluster set; step 4, according to The number of trajectory segments carried by the stop spot can be used to obtain the high heat area of ​​the stop spot, and the hot spot area in the road network can be obtained in the area where the high heat stop spot is located. The invention is used for spatial position positioning.

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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