An Adaptive Staying Point Recognition Algorithm Based on Spatiotemporal Characteristics Using Cellular Signaling Data

Cellular signaling data (CSD) have attracted unprecedented attention due to their large size, long observation period, and high followability. Before applying CSD, a series of data processing steps are indispensable; among those steps, staying point recognition is the basis for recognizing individual travel states and thus the influence of further application of CSD. Previous work indicates that the existing staying point recognition algorithms have two common aspects. One is the requirement of a fixed spatiotemporal threshold to analyze the user’s travel characteristics. The other is the insufficiency of accuracy assessment, which indicates that further studies are expected owing to the lack of ground truth data in CSD. In this work, a “spatiotemporal window”-based algorithm is proposed to recognize individual staying and moving states. First, an iterative-learning-based model is designed to cluster individual trajectory points without predefined spatiotemporal thresholds. Then, rules to distinguish the staying or moving cluster are made from individual travel characteristics. Moreover, verification work is carried out by collecting volunteers’ ground truth data using our developed smartphone application, which achieves an accuracy of 91.3%. Finally, the results demonstrate the effectiveness and robustness of the algorithm through the performance of comparison and sensitivity analyses.