This document discusses data-driven trajectory prediction and the spatial variability of prediction performance in maritime location-based services. It notes that Automatic Identification System data from ships provides billions of records per year but with irregular reporting intervals. The goal is to develop better early-warning systems using data-driven trajectory prediction, though performance varies between regions, impacting the evaluation of published prediction methods. The talk focuses on measuring prediction performance spatially rather than introducing another prediction method.
1. DON‘T TRUST THE NUMBERS!
Data-driven Trajectory Prediction & Spatial Variability of Prediction
Performance in Maritime Location Based Services
Anita Graser, Johanna Schmidt, Melitta Dragaschnig & Peter Widhalm
@underdarkGIS
3. AIS (Automatic Identification System)
Published by the Danish Maritime
Authority
4 billion records per year
730 GB CSV files per year
89,926 distinct vessel IDs
GPS locations, vessel status, …
Irregular reporting intervals
Terrestrial: 2sec to 3min
Satellite: hours
DATA
4. Goal: better early-warning systems
DATA-DRIVEN TRAJECTORY PREDICTION
Image source: Sang, L. Z., Yan, X. P., Wall, A., Wang, J., & Mao, Z. (2016). CPA calculation method based on AIS
position prediction. The Journal of Navigation, 69(6), 1409-1426.
Standard linear prediction
Potential data-driven prediction
14. Even simple data-driven prediction approaches outperform basic linear prediction in
areas of complex movement
BUT linear prediction is unbeatable in some areas
Performance varies between regions
Impacts evaluation of published methods
Comparisons largely meaningless
CONCLUSIONS
14
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Predicted future locations after 15 minutes for two incoming (red and blue) and two outgoing (orange and green) cargo vessel trajectories.