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TTDM: A Travel Time Difference Model for Next Location Prediction
Next location prediction is of great importance for many location-based
applications and provides essential intelligence to business and governments.
In existing studies, a common approach to next location prediction is to learn
the sequential transitions with massive historical trajectories based on
conditional probability. Unfortunately, due to the time and space complexity,
these methods (e.g., Markov models) only use the just passed locations to
predict next locations, without considering all the passed locations in the
trajectory. In this paper, we seek to enhance the prediction performance by
considering the travel time from all the passed locations in the query
trajectory to a candidate next location. In particular, we propose a novel
method, called Travel Time Difference Model (TTDM), which exploits the
difference between the shortest travel time and the actual travel time to
predict next locations. Further, we integrate the TTDM with a Markov model via
a linear interpolation to yield a joint model, which computes the probability
of reaching each possible next location and returns the top-rankings as
results. We have conducted extensive experiments on two real datasets: the
vehicle passage record (VPR) data and the taxi trajectory data. The
experimental results demonstrate significant improvements in prediction
accuracy over existing solutions. For example, compared with the Markov model,
the top-1 accuracy improves by 40% on the VPR data and by 15.6% on the Taxi
data