  The analysis of GPS trajectories is a well-studied problem in Urban Computing
and has been used to track people. Analyzing people mobility and identifying
the transportation mode used by them is essential for cities that want to
reduce traffic jams and travel time between their points, thus helping to
improve the quality of life of citizens. The trajectory data of a moving object
is represented by a discrete collection of points through time, i.e., a time
series. Regarding its interdisciplinary and broad scope of real-world
applications, it is evident the need of extracting knowledge from time series
data. Mining this type of data, however, faces several complexities due to its
unique properties. Different representations of data may overcome this. In this
work, we propose the use of a feature retained from the Ordinal Pattern
Transition Graph, called the probability of self-transition for transportation
mode classification. The proposed feature presents better accuracy results than
Permutation Entropy and Statistical Complexity, even when these two are
combined. This is the first work, to the best of our knowledge, that uses
Information Theory quantifiers to transportation mode classification, showing
that it is a feasible approach to this kind of problem.
