Identification and analysis of movement patterns in trajectories

Ongoing development in the field of digitization is making increasingly powerful sensors available, which are also being integrated into the everyday life of human society; moreover, ongoing development in the field of storage and computing capacities is making it possible to analyze very large data sets (Big Data). An example of Big Data is trajectory datasets recorded over a long period of time; analysis of such datasets offers the possibility of identifying phenomena that are not visible in the data at first glance (including movement patterns). In this work, movement patterns in trajectory datasets are identified with respect to the respective visited locations of a trajectory. For this purpose, further semantic information is assigned to the whereabouts points depending on the position, time of day, and duration of stay; the assignment of semantic information with respect to position is done using OpenStreetMap data. Another focus was on the identification of related trajectory segments, since the given dataset was anonymized as a consequence of data protection; for this purpose, coordinate prediction was performed for all trajectory endpoints in order to identify a suitable continuing starting point of another trajectory using a proximity search and temporal proximity. Recurrent motion pattern detection performed based on the whereabouts points does not produce meaningful patterns detected in multiple trajectories throughout the dataset for the datasets used; however, meaningful recurrent patterns are found for individual trajectories. An increasing level of detail in assigning categories with respect to whereabouts results in fewer recurring patterns, which,
on the other hand, allow for greater meaningfulness given the interpretation of an observed person’s movement behavior.