现代信息科技2024，Vol.8Issue(15)：47-50,4.DOI:10.19850/j.cnki.2096-4706.2024.15.010
基于改进粒子群聚类算法的出行热点提取方法
Method of Extracting Travel Hotspots Based on Improved Particle Swarm Optimization Cluster Algorithm
摘要
Abstract
This paper proposes a clustering algorithm based on improved Particle Swarm Optimization to achieve urban travel hotspot mining.Firstly,it preprocesses the trajectory data through cleaning,standardization,and segmentation.Then,an improved Particle Swarm Optimization clustering algorithm is used to analyze the hotspot area.Finally,it takes the hotspots as network nodes and takes the roads as connecting edges to establish network model.Starting from the nodes and connecting edges,it achieves visualization of travel hotspots.The global optimization ability and distributed random search characteristics of the algorithm can solve the problem of traditional clustering algorithms easily falling into local optima.The algorithm introduces a compression factor and can control the update speed of the particle swarm by configuring the optimal parameters,so as to effectively improve the accuracy and global convergence of the Particle Swarm Optimization algorithm.关键词
轨迹数据/压缩因子/改进粒子群算法/聚类算法/热点挖掘Key words
trajectory data/compressibility factor/improved Particle Swarm Optimization algorithm/cluster algorithm/hotspot mining分类
信息技术与安全科学引用本文复制引用
陈瑛,吴明珠..基于改进粒子群聚类算法的出行热点提取方法[J].现代信息科技,2024,8(15):47-50,4.基金项目
广东省2022年度高等学校科研平台和项目重点领域专项(2022ZDZX1067) （2022ZDZX1067）
广东省教育科学规划2022年度课题(2022GXJK552) （2022GXJK552）
2023年广东省高职院校课程思政示范课程(KCSZ04168) （KCSZ04168）
2023年广州市教学成果培育项目(2023128737) （2023128737）
