地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (1): 136-144.doi: 10.12082/dqxxkx.2020.190655

• 专辑:地理智能 • 上一篇    下一篇

顾及上网行为特征的手机用户停留行为预测方法

方志祥1,*(), 倪雅倩2, 黄守倩1   

  1. 1. 武汉大学测绘遥感信息工程国家重点实验室,武汉 430079
    2. 高德软件有限公司,北京 102200
  • 收稿日期:2019-11-04 修回日期:2019-12-31 出版日期:2020-01-25 发布日期:2020-04-08
  • 通讯作者: 方志祥 E-mail:zxfang@whu.edu.cn
  • 作者简介:方志祥(1977— ),男,湖北咸宁人,教授,主要从事时空行为建模、导航与位置服务研究。
  • 基金资助:
    国家重点研发计划项目(2017YFB0503802);国家自然科学基金项目(41231171)

Mobile Phone User Stay Behavior Prediction Method Considering Mobile APP Usage Characterization

FANG Zhixiang1,*(), NI Yaqian2, HUANG Shouqian1   

  1. 1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    2. Autonavi Holdings Limited, Beijing 102200, China
  • Received:2019-11-04 Revised:2019-12-31 Online:2020-01-25 Published:2020-04-08
  • Contact: FANG Zhixiang E-mail:zxfang@whu.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2017YFB0503802);National Natural Science Foundation of China(41231171)

摘要:

随着信息通讯技术的发展,手机成为人类日常生活不可缺少的一部分,人类活动逐渐从现实空间延伸至网络空间,在移动互联网时代,网络空间的上网行为与现实空间的出行行为密不可分。当前个体出行行为预测建模较少考虑上网行为与出行行为间的关系,本文提出一种融合上网行为特征的手机用户停留行为预测模型,通过时空约束定义手机用户的停留行为,在考虑个体出行行为时空偏好的同时,融合手机用户使用的APP组合、上网流量、上网次数等上网行为特征以及天气信息等外部特征,从时间、空间的角度进行特征交叉,构建从特征到模型均具有高可解释性的手机用户停留行为预测模型。实验证明:本文模型预测准确率为80.31%,且在融合上网行为特征、天气等外部因素后,比仅使用个体出行特征进行手机用户停留行为预测提升了12.08%。

关键词: 手机数据, 手机上网流量数据, 移动行为模式, 停留行为预测, 上网行为特征, 特征融合, 机器学习, 逻辑回归模型

Abstract:

With the development of information and communication technology, mobile phones have become an indispensable part of human daily life. Human activities have gradually extended from real space to cyberspace. The online behavior of cyberspace in the era of mobile Internet is inseparable from the travel behavior of real space. The current individual travel behavior predictive modeling is less concerned with the relationship between online behavior and travel behavior. A mobile phone user stay behavior prediction model based on the characteristics of online app usage behavior is proposed. Firstly, the time-space constraint is used to define the mobile phone user's stay behavior. Then, from multi-source data, the paper extracts the individual travel behavior's space-time preference, the app usage characteristics such as the APP combination, Internet traffic, Internet access times and other Internet behavior characteristics and weather information, etc. Feature engineering is done in a time and space crossing way, and the mobile phone user stay behavior prediction model with high interpretability from feature to model is constructed. We found the following from the experimental results: (1) The prediction accuracy of the model is 80.31%. After the integration of online behavior characteristics, weather and other external factors, the prediction accuracy is improved by 12.08%, compared with the model only using individual travel characteristics. (2) The prediction accuracy of the model is higher than that of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT). And it is 1.23% lower than that of Random Forest (RF), but the model in this paper runs faster than RF, and the model solving process is easy to understand and interpretable. Besides, the first-order Markov model has a small amount of calculations and a fast running speed, but the accuracy is low. In general, the model in this paper has higher accuracy and fast running speed, which is more suitable for mobile phone user stay behavior prediction. (3) There is a big difference in the prediction accuracy of different users' stay behaviors prediction. The prediction accuracy of most users is concentrated between 70% and 90%. The highest prediction accuracy is 98.2%, and the worst prediction accuracy is 34.5%. (4) Among the app usage characteristics, the APP combination, Internet traffic and Internet access times contribute more to the prediction of mobile user stay behavior. The use of navigation, news and office apps has a particularly significant impact on the prediction results. In addition, comparing to the historical travel behavior characteristics, travel distance and activity radius in the current period have a stronger impact on the prediction of mobile phone user stay behavior.

Key words: mobile phone location data, mobile Internet data, mobile behavior patteren, stay behavior prediction, online behavior characteristics, feature fusion, machine learning, logistic regression model