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.

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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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