Regional hotspot path identification based on grid clustering optimization
WENG Xuyan
ZHENG Shuni
Abstract：To solve the problem that the trajectory clustering method is difficult to accurately identify high-similarity hotspot paths,a hotspot path identification method that can distinguish between start and end points or local sections is proposed.The travel trajectory is mapped and compressed into a moving mesh sequence,and the spatial similarity measurement between sequences is distinguished from the boundary and the interior,integrated and transformed into distance,and spatial clustering based on grid sequencedensity-based spatial clustering of applications with noise(GS-DBSCAN)is performed.Taking the trajectory data of some taxis in Shinan District,Qingdao as an example,the clustering method that only considers internal similarity and is based on the shorter and longer sequences in the comparison sequence is verified.The results show that the GS-DBSCAN algorithm,which considers both boundary and internal similarity and is based on longer sequences,can correctly distinguish the separation,convergence,and cross-coupling hotspot paths with large length differences under the distribution of multiple travel start and end points.The influence of variable differences such as path length and grid size is less than 2％,and the clustering operation efficiency is high.
Keywords：hotspot pathboundaryinteriortrajectory clustering
Publication Date：2024-06-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：8( 89-96 )