optimizing vessel trajectory compression
TRANSCRIPT
Optimizing Vessel Trajectory Compression
Giannis Fikioris, Kostas Patroumpas, Alexander Artikis
MBDW 2020, MDM 2020
June 2020
Fikioris et al (MBDW 2020) Optimizing Vessel Trajectory Compression June 2020 1 / 11
Outline
1 Online Summarization of Vessel Trajectories
2 Fine-Tuning of Compression Parameters
3 Empirical Analysis
4 Future Work
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Online Summarization of Vessel Trajectories
Vessel Trajectory Compression
Single-pass filters to remove noise:duplicate/delayed points, invalidcoordinates, etc.
Online detection of critical points alongthe evolving trajectory of a vessel: e.g.,stop, turning points, slow motion, etc.
Trajectory synopsis: these critical pointscan approximately reconstruct the originalcourse.
The actual course is approximated viatime-based interpolation betweensuccessive critical points.
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Online Summarization of Vessel Trajectories
Compression Parameters
Symbol Parameter
∆θ Angle threshold (o)m Buffer size (locations)
∆T Gap Period (seconds)ω Historical timespan (seconds)vmin No speed threshold (knots)vθ Low speed threshold (knots)α Speed ratioD Distance threshold (meters)
Custom parametrization is required per dataset, since sampling rates may differ.
Different ship types have different motion patterns, e.g., tankers vs. fishing boats.
Original parameter values were common for all vessel types and picked after dataexploration, using domain expert knowledge.
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Fine-Tuning of Compression Parameters
Our Contribution
We introduce a method that automatically “learns” the best parameters per vessel type inan AIS dataset.
Two variables to minimize:
Error, i.e., the RMSE between the original noiseless trajectories and the ones approximatelyreconstructed from synopses:
RMSE =
√ ∑p∈noiseless points dist
2(p, approx(p))
number of noiseless points for all vessels
Compression Ratio of the resulting trajectory synopses:
Ratio =number of critical points for all vessels
number of noiseless points for all vessels
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Fine-Tuning of Compression Parameters
Optimization Function
To minimize both objectives we minimize a function of the following form:
(RMSE + r)n × Ratio
where r and n are hyper-parameters.
Our goal is to find parameters that keep the value of RMSE tolerable (usually close tothe length of a ship) and also minimize the value of Ratio.
Fikioris et al (MBDW 2020) Optimizing Vessel Trajectory Compression June 2020 6 / 11
Fine-Tuning of Compression Parameters
Optimization Algorithm
We employ a genetic algorithm (GA) thatiterates over several combinations of thecompression parameter values, tominimize the optimization function.
To set the hyper-parameters, we train theGA for different combinations of r and nto find values so that the resulting RMSEand Ratio are below certain thresholds.
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Empirical Analysis
Empirical Setup
To test the GA we used the Brest dataset, training it on the ship types with the most AISmessages.
For each ship type we did a 6-fold cross validation.
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Empirical Analysis
Brest Dataset - Information
Brest dataset information Threshold Hyper-parameters Training Cost (5/6 of the points)
Vessel type AIS messages Vessel count RMSE Ratio r n Mean time ± Std deviation
Passenger 4,792,487 17 30m 10% 17 0.8 5.2 hours ± 16 minutesUnknown 3,466,765 115 15m 15% 10 1.0 4.5 hours ± 8 minutesFishing 3,288,577 161 30m 30% 17 0.7 4.5 hours ± 39 minutesTug 1,411,761 15 15m 15% 2 1.6 1.8 hours ± 4 minutesCargo 1,198,228 184 30m 10% 13 0.8 1.5 hours ± 2 minutesMilitary 802,045 12 15m 15% 10 1.4 1 hour ± 3 minutes
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Empirical Analysis
Brest Dataset - Results
(a) Passenger Ships (b) Unknown Type (c) Fishing Boats
(d) Tug Boats (e) Cargo Ships (f) Military Vessels
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Future Work
Future Work
Evaluate the performance of the new synopses on recognizing complex events.
Introduce new methods for better/faster optimization.
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