Fine-grained identification method of home-work location based on travel characteristics of residents
Huang Xingru, Li Yixuan, Liu Zhongliang, Feng Hanbin, Wang Xizhao, Yan Long, Hu Bowen, Li Xuanzi, Li Dazhong
Data Intelligence Division,Unicom Digital Technology Co., Ltd.
Abstract： To address the simplicity and limitations of the traditional homework model calculation rules and reduce the identification errors caused by differences in the daily routines of residents in various regions or temporary changes, this study proposed a finegrained identification method of home-work location based on the travel characteristics of residents in different regions. Firstly, 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 analyzed. 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 homework identification results are significantly better than those of traditional single homework model calculation rules. This method is suitable for batch processing of homework problems in multiple regions simultaneously, particularly for cities where changes in routines are caused by unexpected events.
