@article{1960,
  author = {Le QI, Zhongyi ZHENG},
  title = {Trajectory Prediction of Vessels based on Data Mining and Machine Learning},
  journal = {Journal of Digital Information Management},
  year = {2016},
  volume = {14},
  number = {1},
  doi = {},
  url = {http://dline.info/fpaper/jdim/v14i1/v14i1_5.pdf},
  abstract = {With the development of marine traffic information
digitalization, proactive information service
has become increasingly important in maritime intelligent
traffic systems (ITS). Trajectory prediction is one
of the kernel problems that must be addressed to realize
proactive information service. This study proposes
an intelligent model to solve the issue of the trajectory
prediction of vessels based on data mining and machine
learning methods. The spatial clustering algorithm
of data mining is used to cluster the historical trajectories
of vessels, and the cluster results represent the
distribution patterns of these historical trajectories. The
support vector machine algorithm of machine learning
is used to train the classifiers. The classifiers define
the pattern of the new trajectory of the vessel, which
must be predicted. In the experiment, the information
on 2862 trajectories is used as input to the model in
chronological order to simulate the data flow in realtime
situation. The predicted trajectories are compared
with the actual trajectories of vessels. Experimental
results show that future trajectories can be predicted
efficiently and accurately. The intelligent model can also
solve prediction problems with little human intervention
and can automatically adapt to dynamic applications.
The prediction results can provide accurate and reliable
data for proactive information service. The model
promotes the development of maritime ITS.},
}