  Understanding transportation mode from GPS (Global Positioning System) traces
is an essential topic in the data mobility domain. In this paper, a framework
is proposed to predict transportation modes. This framework follows a sequence
of five steps: (i) data preparation, where GPS points are grouped in trajectory
samples; (ii) point features generation; (iii) trajectory features extraction;
(iv) noise removal; (v) normalization. We show that the extraction of the new
point features: bearing rate, the rate of rate of change of the bearing rate
and the global and local trajectory features, like medians and percentiles
enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%)
scores. We also show that the noise removal task affects the performance of all
the models tested. Finally, the empirical tests where we compare this work
against state-of-art transportation mode prediction strategies show that our
framework is competitive and outperforms most of them.
