Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data.

Understanding human mobility is significant in many fields, such as geography, transportation, and sociology. Due to the wide spatiotemporal coverage and low operational cost, mobile phone data have been recognized as a major resource for human mobility research. However, due to conflicts between the data sparsity problem of mobile phone data and the requirement of fine-scale solutions, trajectory reconstruction is of considerable importance. Although there have been initial studies on this problem, existing methods rarely consider the effect of similarities among individuals and the patterns of missing data. To address this issue, we propose a multi-criteria data partitioning trajectory reconstruction (MDP-TR) method for large-scale mobile phone data. In the proposed method, a multi-criteria data partitioning (MDP) technique is used to measure the similarity among individuals in near real-time and investigate the spatiotemporal patterns of missing data. With this technique, the trajectory reconstruction from mobile phone data is then conducted with machine learning models. We verified the method using a real mobile phone dataset in a large city. Results indicate that the MDP-TR method outperforms competing methods in both accuracy and robustness. We argue that the MDP-TR method can be effectively utilized for grasping highly dynamic human movement status and improving the spatiotemporal resolution of human mobility research. • We present a method for reconstructing individuals' trajectories from mobile phone data. • Our method introduces an anchor-point-based clustering algorithm to address the data hungry problem. • Our method learns movement behaviors with different temporal patterns of missing data. • Our method provides more accurate and robust results than competing methods. • Our method can help to improve the spatiotemporal resolution of human mobility research. [ABSTRACT FROM AUTHOR]