The ability to predict human mobility, i.e., transitions between a user's significant locations (the home, workplace, etc.) can be helpful in a wide range of applications, including targeted advertising, personalized mobile services, and transportation planning. Most studies on human mobility prediction have focused on the algorithmic perspective rather than on investigating human predictability. Human predictability has great significance, because it enables the creation of more robust mobility prediction models and the assignment of more accurate confidence scores to location predictions. In this study, we propose a novel method for detecting a user's stay points from millions of GPS samples. Then, after detecting these stay points, a long short-term memory (LSTM) neural network is used to predict future stay points. We explore the use of two types of stay point prediction models (a general model that is trained in advance and a personal model that is trained over time) and analyze the number of previous locations needed for accurate prediction. Our evaluation on two real-world datasets shows that by using our preprocessing approach, we can detect stay points from routine trajectories with higher accuracy than the methods commonly used in this domain, and that by utilizing various LSTM architectures instead of the traditional Markov models and advanced deep learning models, our method can predict human movement with high accuracy of more than 40% when using the Acc@1 measure and more than 59% when using the Acc@3 measure. We also demonstrate that the movement prediction accuracy varies for different user populations based on their trajectory characteristics and demographic attributes.

预测人类活动能力的能力，即用户重要位置（家庭，工作场所等）之间的转换，可以在广泛的应用程序中有所帮助，包括定向广告，个性化移动服务和交通规划。关于人类流动性预测的大多数研究都集中在算法的角度，而不是研究人类的可预测性。人类可预测性具有重要意义，因为它可以创建更强大的移动性预测模型，并为位置预测分配更准确的置信度得分。在这项研究中，我们提出了一种从数百万个GPS样本中检测用户停留点的新颖方法。然后，在检测到这些停留点之后，使用长短期记忆（LSTM）神经网络来预测将来的停留点。我们探索使用两种类型的停留点预测模型（一个预先训练的通用模型和一个随时间推移训练的个人模型），并分析准确预测所需的先前位置数量。我们对两个真实数据集的评估表明，通过使用预处理方法，我们可以以比该领域常用方法更高的精度从常规轨迹中检测停留点，并且可以利用各种LSTM架构代替传统的Markov模型和先进的深度学习模型，我们的方法可以预测使用Acc @ 1量度时超过40％的准确度，使用Acc @ 3量度时超过59％的准确度。