In order to aid prediction of future maritime vessel trajectories, it is useful to examine
historical vessel information. It is mandatory for large maritime vessels to broadcast,
among other fields, spatial, speed, and course information using Automatic Identi-
fication System (AIS) transponders. By processing a large historical dataset, it is
possible to predict future vessel trajectories. The region of interest is discretized into
a grid. Then, using offline computations, the historical data are used to determine
second-order transition probabilities and speed information. Predictions will be car-
ried out as an online process. If the destination is known, Dijkstra’s Algorithm is used
to predict the vessel’s path. If the destination is not known, a path can still be de-
termined using transition probabilities, but the prediction will be less accurate. The
path is then smoothed using a line of sight algorithm to produce more realistic paths.
Finally, the speed information is used to predict travel times. Real data were used to
build the graph structure, and predictions were judged against real trajectories. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/21237 |
| Date | January 2017 |
| Creators | Wilson, Paul |
| Contributors | Kirubarajan, Thia, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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