Clustering and Analysis of GPS Trajectory Data using Distance-based Features
This work addresses the problem of understanding non-Work, non-Home mobility patterns for researchers in mobility studies, but it is incremental as it builds on existing clustering methods with new metrics.
The authors tackled the lack of a standardized framework for analyzing mobility patterns from GPS data by proposing a new metric, Daily Characteristic Distance, and using it with Origin-Destination features to cluster users via k-means, resulting in three clusters per day type (Workday and Offday) and enabling the discernment of interesting user behaviors through new analysis metrics.
The proliferation of smartphones has accelerated mobility studies by largely increasing the type and volume of mobility data available. One such source of mobility data is from GPS technology, which is becoming increasingly common and helps the research community understand mobility patterns of people. However, there lacks a standardized framework for studying the different mobility patterns created by the non-Work, non-Home locations of Working and Nonworking users on Workdays and Offdays using machine learning methods. We propose a new mobility metric, Daily Characteristic Distance, and use it to generate features for each user together with Origin-Destination matrix features. We then use those features with an unsupervised machine learning method, $k$-means clustering, and obtain three clusters of users for each type of day (Workday and Offday). Finally, we propose two new metrics for the analysis of the clustering results, namely User Commonality and Average Frequency. By using the proposed metrics, interesting user behaviors can be discerned and it helps us to better understand the mobility patterns of the users.