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

2. Increasing shipping traffic & data collection (AIS)
 Information overload for port operators
MOTIVATION

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

5. DATA-DRIVEN TRAJECTORY PREDICTION
Data-driven
Linear

6. THIS TALK IS NOT ABOUT
YET ANOTHER PREDICTION METHOD!

7. EXAMPLE RESULTS
(15 minutes)

8. … BUT HOW CAN WE EVALUATE THEIR
PERFORMANCE?

9. PREDICTION ERRORS IN THE LITERATURE

10. MEASURING PREDICTION PERFORMANCE

11. Linear prediction errors (after 20 min)
SPATIAL VARIABILITY

12. Similar trajectory prediction errors (after 20 min)
SPATIAL VARIABILITY

13. LinearData-driven

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

15. anita.graser@ait.ac.at
@underdarkGIS
anitagraser.com
ANITA GRASER