Abstract—

Aiming at the need to discover user behavior characteristics and knowledge from moving trajectory data, a user behavior profiling method based on moving trajectory information was proposed. Firstly, the trajectory coordinates were preprocessed to clean out good data. Secondly, the travel rules and the points of interest of the user were found by means of stay points detection, staying points’ semantics and frequent pattern mining. In the aspect of predicting user trajectory information, Key Points Long Short-Term Memory Networks (KP-LSTM) was proposed to predict the user’s future travel location; then the user’s important attribute characteristics were taken through the user profiling, intuitively depicting the characteristics and patterns of users’ lives. Finally, the availability of the method was proved by experiments, and the prediction accuracy was better than the traditional Linear regression and LSTM neural network.

中文翻译：

---

基于轨迹信息的用户行为预测与分析方法研究

摘要—

针对需要从移动轨迹数据中发现用户行为特征和知识的需求，提出了一种基于移动轨迹信息的用户行为特征分析方法。首先，对轨迹坐标进行预处理，以清理出良好的数据。其次，通过停留点检测，停留点语义和频繁模式挖掘等手段，找到用户的出行规则和兴趣点。在预测用户轨迹信息方面，提出了关键点长期短期记忆网络（KP-LSTM）来预测用户未来的出行位置。然后通过用户配置文件获取用户的重要属性特征，直观地描述用户的生活特征和模式。最后，通过实验证明了该方法的有效性，。