Analyzing movement predictability using human attributes and behavioral patterns

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.