计算机与数字工程2024，Vol.52Issue(3)：653-658,676,7.DOI:10.3969/j.issn.1672-9722.2024.03.003
基于自适应网格密度算法的出行模式时空分析
Spatiotemporal Analysis of Travel Modes Based on an Adaptive Grid Density Algorithm
摘要
Abstract
A trajectory data preprocessing method and a region mining algorithm based on adaptive grid density big data are proposed to address the drawbacks of noise in taxi GPS raw trajectory data,high processing costs for big data,difficulty in parame-ter selection,and susceptibility to clustering effects in traditional density algorithms.The research results indicate that the adaptive grid density algorithm can effectively avoid the parameter adjustment process,has strong sample space adaptability,and high clus-tering quality.Compared with conventional density clustering algorithms,it has lower computational complexity and higher computa-tional efficiency.The spatiotemporal characteristics of Chongqing residents'travel patterns provided are in line with reality and have practical value.关键词
出租车GPS轨迹/数据清理/网格密度/热点区域/聚类挖掘Key words
taxi GPS track/data cleaning/grid density/hot area/cluster mining分类
信息技术与安全科学引用本文复制引用
熊敏,石超峰,张玺..基于自适应网格密度算法的出行模式时空分析[J].计算机与数字工程,2024,52(3):653-658,676,7.基金项目
国家社会科学基金项目(编号:16BJL121) （编号:16BJL121）
重庆市教委科学技术研究项目(编号:KJ1705148)资助. （编号:KJ1705148）
