网络安全与数据治理2024，Vol.43Issue(8)：44-48,5.DOI:10.19358/j.issn.2097-1788.2024.08.008
基于居民出行特征的职住地精细化识别
Fine-grained identification method of home-work location based on travel characteristics of residents
黄兴如 1李奕萱 1刘中亮 1冯瀚斌 1王希昭 1闫龙 1胡博文 1李炫孜 1李大中1
作者信息
- 1. 联通数字科技有限公司 数据智能事业部,北京 100010
- 折叠
摘要
Abstract
To address the simplicity and limitations of the traditional home-work model calculation rules and reduce the identifica-tion errors caused by differences in the daily routines of residents in various regions or temporary changes,this study proposed a fine-grained identification method of home-work location based on the travel characteristics of residents in different regions.First-ly,various methods such as"3-minute slicing"and"angle+stay time+connection frequency"are used to denoise and refine the mobile phone signaling data.Then,based on spatiotemporal constrained density clustering,stay points are identified and ana-lyzed.Finally,according to the daily travel characteristics of residents in various cities,weighted stay duration is introduced to dynamically update the home-work calculation rules for residents in different city areas,thereby refining the identification of home-work distribution for users in different cities.Experimental results show that the processes involved in this method are reasonable and effective,and the final home-work identification results are significantly better than those of traditional single home-work mod-el calculation rules.This method is suitable for batch processing of home-work problems in multiple regions simultaneously,par-ticularly for cities where changes in routines are caused by unexpected events.关键词
信令数据/出行特征/密度聚类/加权驻留时长/职住地识别Key words
cellular signaling data/travel characteristics/DBSCAN/weighted stay duration/home-work location identification分类
信息技术与安全科学引用本文复制引用
黄兴如,李奕萱,刘中亮,冯瀚斌,王希昭,闫龙,胡博文,李炫孜,李大中..基于居民出行特征的职住地精细化识别[J].网络安全与数据治理,2024,43(8):44-48,5.
