基于居民出行特征的职住地精细化识别OA

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.