Using Complexity Measures of Movement for Automatically Detecting Movement Types of Unknown GPS Trajectories
Xun Li · American Journal of Geographic Information System · 2014
The application of trajectory classification to automatically detect movement types of unknown trajectories has been receiving increasing research attention in areas such as video surveillance, traffic management and location-based services. Existing research applies classic geometric shape-based classification approaches to classify trajectories by utilizing the geometric characteristics of movement to fulfill this task. However, this approach is limited to the geographic context of trajectory data. Classification methods based on movement parameters can overcome this problem but the accuracy of classification depends heavily on selecting appropriate movement features from trajectories. This research proposes an efficient trajectory classification model based on two types of complexity measures as new features for classifying movements: (1) the geometric complexity measures of trajectories based on Fractal Dimensions, and (2) structural complexity measures of movement parameters based on Approximate Entropy (ApEn). We suggest that ApEn, which provides complexity information about the subtle changes that occur in the structure of sequential movement parameters of trajectories, and Fractal Dimensions, which provide the overall description of geometric complexity, can be used together to improve the accuracy in trajectory classification. The feasibility of this proposed classification model is tested with 800 GPS trajectories that were shared and manually tagged with four movement types by Internet users on the website Openstreemap.org. The overall 85.4% average accuracy of prediction demonstrates the applicability of this classification model. By improving the quality of trajectory classification, the proposed approach in this research will benefit many applications of trajectory data analysis and mining.