  We present here a general framework and a specific algorithm for predicting
the destination, route, or more generally a pattern, of an ongoing journey,
building on the recent work of [Y. Lassoued, J. Monteil, Y. Gu, G. Russo, R.
Shorten, and M. Mevissen, "Hidden Markov model for route and destination
prediction," in IEEE International Conference on Intelligent Transportation
Systems, 2017]. In the presented framework, known journey patterns are modelled
as stochastic processes, emitting the road segments visited during the journey,
and the ongoing journey is predicted by updating the posterior probability of
each journey pattern given the road segments visited so far. In this
contribution, we use Markov chains as models for the journey patterns, and
consider the prediction as final, once one of the posterior probabilities
crosses a predefined threshold. Despite the simplicity of both, examples run on
a synthetic dataset demonstrate high accuracy of the made predictions.
