  Trajectory prediction (TP) is of great importance for a wide range of
location-based applications in intelligent transport systems such as
location-based advertising, route planning, traffic management, and early
warning systems. In the last few years, the widespread use of GPS navigation
systems and wireless communication technology enabled vehicles has resulted in
huge volumes of trajectory data. The task of utilizing this data employing
spatio-temporal techniques for trajectory prediction in an efficient and
accurate manner is an ongoing research problem. Existing TP approaches are
limited to short-term predictions. Moreover, they cannot handle a large volume
of trajectory data for long-term prediction. To address these limitations, we
propose a scalable clustering and Markov chain based hybrid framework, called
Traj-clusiVAT-based TP, for both short-term and long-term trajectory
prediction, which can handle a large number of overlapping trajectories in a
dense road network. Traj-clusiVAT can also determine the number of clusters,
which represent different movement behaviours in input trajectory data. In our
experiments, we compare our proposed approach with a mixed Markov model
(MMM)-based scheme, and a trajectory clustering, NETSCAN-based TP method for
both short- and long-term trajectory predictions. We performed our experiments
on two real, vehicle trajectory datasets, including a large-scale trajectory
dataset consisting of 3.28 million trajectories obtained from 15,061 taxis in
Singapore over a period of one month. Experimental results on two real
trajectory datasets show that our proposed approach outperforms the existing
approaches in terms of both short- and long-term prediction performances, based
on prediction accuracy and distance error (in km).
