Road network clustering-based hotspot region mining method

Abstract

The invention relates to a road network trajectory clustering-based travel hotspot region mining method. According to the method, taxi trajectories are mapped into a road network, and a clustering method combining points of interest and trajectories collected in actual roads is adopted; on the basis of a density peak clustering algorithm, an OPAM algorithm which optimizes an initial center based on density peaks is provided, namely, a namely DP-OPAM algorithm is provided; according to the algorithm, the local density of data points and shortest distances from the points to points with higher density are adopted, and a decision graph is adopted to select the category of data points with higher density and closest distance, and the category is adopted as an initial clustering center; and onthe basis of the initial clustering center, an OPAM clustering algorithm additionally adopting inverse learning is used to obtain a clustering result. Compared with an original OPAM algorithm, the newalgorithm can not only automatically determine a clustering center, but also improve accuracy and shorten clustering time and realize the analysis of user travel hotspots.

Description

technical field [0001] The invention belongs to a data mining method, in particular to a taxi track clustering method based on a road network. Background technique [0002] As a hot spot in the development of transportation in the world today, intelligent transportation not only supports transportation management, but also pays more attention to meeting the needs of people's travel and public transportation. In recent years, the construction of intelligent transportation systems has developed rapidly, and many advanced technologies have been widely used in intelligent transportation systems. The wide application of GPS devices makes trajectory extraction more convenient. These GPS devices can collect a large amount of mobile location sequence information and vehicle status information, which contains rich traffic information and user behavior information. By analyzing and mining trajectory data, we can understand traffic conditions, plan trips reasonably, discover crowd be...

AI Technical Summary

Problems solved by technology

But the disadvantage is that the method of trajectory segmentation has a great impact on the clustering results, and different segmentation methods may cause great differences in the results.

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