  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.
