Novel Algorithms for Trajectory Segmentation
Channel: Ocean Data Science Inc
Date: 2019-09-06

An enormous number of mobility datasets for tracking animals, vehicles, vessels, individuals and moving objects are currently available, and this number continues to grow. Mobility data has a diversity of applications including transportation, marine navigation, tourism, animal behavior analysis, environmental science. Since the rate of generation of the mobility data is exceptionally high, processing such data demands some reasonable pre-processing and cleaning steps. Segmenting the traces of movements in mobility data is an essential task for pre-processing, which is called trajectory segmentation. The significance of this pre-processing step has three dimensions. First, it is crucial to data summarization, so that it can alleviate the curse of dimensionality. Second, correctly using a segmentation method can contribute to increase the level of preserving the privacy of mobility data. Third, it can be employed in embedded systems to diminish energy consumption and make mobility sensor devices operating for a longer time.
This report scaffolds our research on trajectory segmentation. First, we review the related work in trajectory segmentation. Then we review our thesis plan including expected contribution, our approach, evaluation methods and timeline. After that we explain the work already carried out including our trajectory segmentation method called Octal Window Segmentation (OWS). We introduce OWS method which is a simple, swift unsupervised approach for segmenting trajectories and it can be embedded in mobility sensor devices. We assess OWS algorithm performance with three metrics: Purity, Coverage, and Harmonic mean of Purity and Coverage. Comparing our results with the state-of-the-art algorithm (GRASP-UTS) for trajectory segmentation unveiled that Octal Window Segmentation has better performance.