  We present a simple model and algorithm for predicting driver destinations
and routes, based on the input of the latest road links visited as part of an
ongoing trip. The algorithm may be used to predict any clusters previously
observed in a driver's trip history. It assumes that the driver's historical
trips are grouped into clusters sharing similar patterns. Given a new trip, the
algorithm attempts to predict the cluster in which the trip belongs. The
proposed algorithm has low temporal complexity. In addition, it does not
require the transition and emission matrices of the Markov chain to be
computed. Rather it relies on the frequencies of co-occurrences of road links
and trip clusters. We validate the proposed algorithm against an experimental
dataset. We discuss the success and convergence of the algorithm and show that
our algorithm has a high prediction success rate.
