This work proposes the use of Information Theory for the characterization of
vehicles behavior through their velocities. Three public data sets were used:
i.Mobile Century data set collected on Highway I-880, near Union City,
California; ii.Borl\"ange GPS data set collected in the Swedish city of
Borl\"ange; and iii.Beijing taxicabs data set collected in Beijing, China,
where each vehicle speed is stored as a time series. The Bandt-Pompe
methodology combined with the Complexity-Entropy plane were used to identify
different regimes and behaviors. The global velocity is compatible with a
correlated noise with f^{-k} Power Spectrum with k >= 0. With this we identify
traffic behaviors as, for instance, random velocities (k aprox. 0) when there
is congestion, and more correlated velocities (k aprox. 3) in the presence of
free traffic flow
