How VesselPing Could Use AI to Detect Abnormal Vessel Behaviour.
Artificial Intelligence and Maritime Analytics-
One of the most valuable uses of artificial intelligence in maritime intelligence is the ability to recognize when a vessel is behaving differently from what would normally be expected.
A conventional vessel-tracking platform may show that a ship changed course, reduced speed, stopped offshore, disappeared from AIS coverage, or entered an unfamiliar area.
But those events do not automatically mean something is wrong.
The real analytical challenge is determining:
Is this behaviour normal for this vessel, this route, this location and these operating conditions?
That is where AI could give VesselPing a significant advantage.
By analysing live AIS data together with historical vessel movements, routes, speeds, port calls, weather, geographic zones and patterns involving nearby vessels, VesselPing could build an abnormal-behaviour detection engine capable of identifying movements that deserve closer attention.
The goal would not be to label vessels as suspicious automatically.
The goal would be to identify statistically or operationally unusual behaviour and explain why it stands out.
From Vessel Tracking to Behavioural Intelligence
Traditional vessel tracking focuses heavily on location.
A user might see:
Vessel: MV Example
Speed: 13.4 knots
Course: 218°
Destination: Tema
Status: Under way
An AI-enabled VesselPing could add another layer:
Behavioural Assessment: The vessel has departed from its normal corridor and is travelling approximately 62 nautical miles east of its historical route. No similar deviation appears in its previous eight comparable voyages.
The first system reports what the vessel is doing.
The second system evaluates whether the behaviour is unusual.
That transition—from location monitoring to behavioural interpretation—is central to advanced maritime analytics.
1. AI Could Learn Each Vessel's Normal Behaviour
Different vessels operate differently.
A container ship may regularly travel between the same major ports.
A crude-oil tanker may frequently spend several days offshore awaiting instructions.
A bulk carrier may visit different commodity-export terminals on each voyage.
A tug may operate almost entirely within a restricted coastal area.
Therefore, VesselPing should not apply exactly the same definition of "normal" to every vessel.
AI could build an individual behavioural profile based on historical information such as:
normal cruising speeds;
common routes;
usual ports;
typical anchorage locations;
average voyage duration;
regular operating regions;
speed approaching ports;
typical stopping patterns;
historical AIS continuity.
This creates a baseline.
Future activity can then be compared against that baseline.
For example:
Vessel Behaviour Baseline
Typical cruising speed: 14–17 knots
Common route: Singapore → Mombasa
Typical deviation: Under 15 nautical miles
Normal offshore stops: Rare
Historical AIS continuity: High
If the vessel suddenly travels 80 nautical miles away from its usual corridor and remains stationary offshore for six hours, VesselPing could raise an alert.
2. Detecting Unusual Route Deviations
Route deviation would be one of the clearest forms of abnormal behaviour VesselPing could monitor.
Ships change course for many legitimate reasons:
weather;
traffic separation;
congestion;
security concerns;
operational instructions;
port diversion;
fuel optimization.
Therefore, the system should not assume wrongdoing simply because a route changes.
Instead, AI could compare the new route with:
The vessel's previous voyages
Routes used by similar vessels
Weather conditions
Nearby traffic
Declared destination
Geographic restrictions
The platform could then classify the deviation.
Example
Route deviation: 74 nautical miles
AI assessment: Moderate anomaly
Reason: This vessel normally follows the western shipping corridor. Current weather does not explain the deviation, and nearby comparable vessels have remained on the standard route.
Recommended action: Continue monitoring.
The important element is explanation.
3. Detecting Unexpected Stops
A vessel slowing or stopping can carry significant information.
A ship may stop because it is:
waiting for a berth;
entering anchorage;
conducting maintenance;
waiting for orders;
experiencing mechanical problems;
meeting another vessel;
avoiding adverse weather;
conducting legitimate offshore operations.
AI could distinguish between normal and unusual stops by analysing location and history.
For example:
Normal Situation
A container ship stops outside Lagos in an established anchorage where many vessels are waiting.
VesselPing might classify:
Behaviour: Normal anchorage activity.
Different Situation
The same ship stops for five hours in open water where it has never previously stopped and where nearby vessel traffic is minimal.
VesselPing might classify:
Behaviour: Unusual offshore stop.
The distinction is essential.
4. Detecting Abnormal Speed Changes
Speed changes can also indicate developing anomalies.
VesselPing could continuously compare:
current speed;
historical cruising speed;
expected speed for the route;
vessel type;
weather conditions;
proximity to port;
nearby traffic.
Suppose a tanker normally travels at approximately 13 knots but suddenly slows to 3 knots in open water.
AI could ask:
Is the vessel approaching anchorage?
Are weather conditions severe?
Are other nearby ships also slowing?
Has this vessel stopped here before?
If none of those explanations fit, VesselPing could flag the event.
VesselPing Speed Anomaly
Current speed: 3.2 knots
Normal speed: 12.6 knots
Location: Open sea
Duration: 2 hours 14 minutes
AI assessment: Unusual speed reduction.
Possible explanation: Not identifiable from currently available data.
That final sentence is important.
AI should distinguish between a detected anomaly and a confirmed explanation.
5. Detecting Unexpected Direction Changes
A vessel's heading and course normally change gradually during ocean passages.
Sharp or repeated direction changes may indicate:
traffic avoidance;
weather avoidance;
navigation problems;
search activity;
fishing activity;
waiting behaviour;
maneuvering near another vessel.
AI could identify movement patterns such as:
Repeated circles
Zig-zag movement
Sudden 180-degree turns
Unexpected return toward departure point
Repeated crossing of the same area
For example:
Course Pattern Alert
The vessel has changed direction more than six times during the past 90 minutes while remaining within a 12-nautical-mile area.
Historical comparison: No similar behaviour was identified during its previous voyages.
Classification: Unusual maneuvering.
This would give analysts a reason to examine the vessel more closely.
6. Detecting Unusual Vessel Encounters
AI can also analyse interactions between vessels.
Two vessels may legitimately come close together because they are:
entering port;
sharing anchorage;
receiving pilot services;
participating in towing operations;
operating within normal traffic lanes.
However, an offshore encounter outside normal traffic patterns can be analytically interesting.
VesselPing could detect when two vessels:
approach unusually closely;
reduce speed together;
remain close for an extended period;
depart in different directions afterward.
Example:
Vessel Encounter Alert
Vessel A: Tanker
Vessel B: Tanker
Closest distance: 0.25 nautical miles
Time in close proximity: 3 hours 42 minutes
Location: Open sea
Historical frequency: First detected encounter between these vessels.
AI assessment: Unusual offshore interaction.
Again, the system should not claim that illegal activity occurred.
It should identify the movement as worthy of review.
7. Detecting AIS Gaps
AIS signal disappearance can be another important behavioural indicator.
But it must be interpreted carefully.
A missing AIS signal may result from:
weak receiver coverage;
satellite reception gaps;
equipment malfunction;
technical interference;
data-provider problems;
operational or regulatory circumstances.
Therefore, VesselPing should not automatically treat every AIS gap as suspicious.
AI could compare an AIS gap against:
Normal coverage in that area
Signals from surrounding vessels
The vessel's historical transmission pattern
Length of the gap
Location before disappearance
Location after reappearance
For example:
AIS Gap Assessment
Signal interruption: 11 hours 26 minutes
Area coverage: Normally strong
Nearby vessels: Continued transmitting
Vessel historical pattern: Rare AIS interruptions
AI assessment: Significant anomaly.
That is considerably more useful than simply saying:
AIS unavailable.
8. Detecting Impossible or Implausible Movement
AI could also identify vessel-position data that appears physically inconsistent.
Suppose a vessel appears at one location and thirty minutes later appears hundreds of nautical miles away.
A commercial ship cannot move at such a speed.
VesselPing could calculate whether reported positions are plausible.
For example:
Position Integrity Alert
Distance between AIS reports: 412 nautical miles
Elapsed time: 48 minutes
Required speed: More than 500 knots
Assessment: Physically impossible vessel movement.
Possible causes could include:
corrupted data;
incorrect vessel identity;
spoofed position;
data-provider error;
AIS equipment configuration problem.
AI would help separate data anomalies from genuine navigational events.
9. Detecting Unexpected Port Calls
A vessel's port history can reveal recurring commercial patterns.
Suppose a vessel has made thirty voyages between Asia and West Africa.
If it suddenly enters a port it has never visited before, the event might be commercially significant.
VesselPing could compare current port calls with historical patterns.
Port Call Anomaly
Current destination: Port X
Previous visits: None in the past three years
Normal destinations: Tema, Lagos and Abidjan
AI assessment: Unusual destination change.
For commodity traders, insurers, supply-chain analysts and researchers, this may provide useful early intelligence.
10. Detecting Unusual Draft Changes
A vessel's draft can sometimes provide clues about loading and unloading activity.
For example, a tanker sitting deeper in the water after visiting a terminal may be carrying more cargo than before.
AI could monitor significant changes in reported draft together with port history.
Suppose:
Draft before port: 8.3 metres
Draft after port: 14.6 metres
The system might explain:
The significant increase in draft is consistent with the vessel having taken on substantial cargo during its latest port call.
Conversely, if a large draft change occurs without an obvious port visit, VesselPing could flag it for examination.
Because AIS-reported draft data may be imperfect or manually entered, this should remain an analytical indicator rather than definitive proof of cargo activity.
11. Detecting Behaviour Inside Sensitive Zones
VesselPing could use geofencing to monitor specific maritime areas.
These might include:
territorial waters;
environmental protection zones;
offshore oil infrastructure;
high-risk security regions;
port approaches;
anchorage zones;
shipping lanes;
restricted operational areas.
AI could identify abnormal behaviour when a vessel:
enters a monitored zone unexpectedly;
stays longer than normal;
reduces speed significantly;
switches course repeatedly;
loses AIS coverage nearby.
Example:
Geofence Behaviour Alert
The vessel entered the designated offshore infrastructure zone at 03:14 UTC and remained within the area for 2 hours 46 minutes.
No previous visits to this zone have been recorded.
Monitoring priority: Elevated.
12. Combining Multiple Weak Signals
The greatest value of AI may come from combining several small anomalies.
One event alone may not be important.
For example:
A speed reduction: Normal.
An AIS gap: Possibly technical.
A route deviation: Could be weather.
A vessel encounter: Could be legitimate.
But consider all four happening together:
Vessel deviates from normal route.
AIS disappears for nine hours.
Vessel reappears at low speed.
It remains close to another vessel for three hours.
It then returns toward its original route.
Individually, each event may be explainable.
Together, they create a much stronger behavioural anomaly.
VesselPing AI could calculate a combined anomaly score.
Behavioural Risk Score
Route deviation: 17 points
AIS gap: 21 points
Unusual encounter: 25 points
Speed anomaly: 10 points
Historical inconsistency: 14 points
Total Behavioural Anomaly Score: 87/100
Priority: High review
This is where machine learning could outperform simple rule-based alerts.
13. VesselPing Could Build a Behaviour Timeline
Users should be able to understand abnormal behaviour visually and chronologically.
For example:
Vessel Behaviour Timeline
02:10 — Vessel leaves normal route
03:42 — Speed falls below 5 knots
04:03 — AIS transmission stops
12:26 — AIS resumes
12:41 — Another tanker detected nearby
15:55 — Vessels separate
17:20 — Vessel returns toward normal route
AI Summary
The vessel displayed several unusual behaviours within a 15-hour period, including route deviation, AIS interruption and an extended close encounter with another tanker. These events differ significantly from its previous voyage history.
This is far easier for an analyst to interpret than thousands of raw AIS points.
14. Different Vessels Need Different Detection Models
Anomaly detection should reflect vessel type.
A fishing vessel may naturally:
circle;
move slowly;
change direction frequently.
A container ship normally follows relatively direct routes between major ports.
A tanker may remain offshore waiting for terminal instructions.
A tug may travel repeatedly within a small geographic area.
Therefore, VesselPing should compare a vessel primarily with:
its own history
and
similar vessels performing similar operations.
Otherwise, the system would generate too many false alerts.
A behaviour that is abnormal for a container ship may be completely normal for a fishing vessel.
15. AI Should Reduce False Alarms
A major weakness of poorly designed monitoring systems is alert overload.
If every route change, speed reduction or AIS gap produces a warning, customers will eventually ignore the alerts.
VesselPing could use AI to prioritize them.
For example:
Low Priority
Vessel reduces speed because it is approaching normal anchorage.
Medium Priority
Vessel deviates significantly from historical route, but weather conditions may explain the change.
High Priority
Vessel deviates unexpectedly, loses AIS coverage in a normally well-covered region and subsequently makes an unusual offshore encounter.
This ranking would help customers focus on events that genuinely warrant investigation.
16. AI Could Explain Every Alert
One of VesselPing's most important principles should be:
Never present an anomaly score without explaining it.
Instead of:
Abnormal Behaviour: 82/100
the system could say:
Why This Vessel Was Flagged
route is 76 nautical miles outside its historical corridor;
AIS transmission stopped for 13 hours;
coverage in this area is normally reliable;
vessel remained close to another tanker for 2.8 hours;
current voyage differs significantly from the vessel's previous 12 voyages.
This makes the AI auditable and useful.
Users should be able to see the evidence and form their own conclusions.
17. VesselPing Could Offer Real-Time Behaviour Alerts
Customers could define which anomalies matter to them.
For example:
Alert Me When
vessel deviates more than 25 nautical miles from expected route;
vessel stops unexpectedly for over two hours;
AIS disappears for more than six hours;
vessel meets another ship offshore;
vessel enters a selected geographic zone;
speed falls dramatically;
destination suddenly changes;
abnormal-behaviour score exceeds 70.
A user might receive:
VesselPing Behaviour Alert
MV Ocean Star has deviated 48 nautical miles from its historical route and reduced speed to 2.9 knots in open water. The behaviour differs substantially from its previous voyages. Monitoring priority: Moderate.
This turns VesselPing into a proactive intelligence platform.
18. Fleet-Level Abnormal Behaviour Monitoring
Larger customers may monitor hundreds of vessels.
Instead of manually checking every ship, VesselPing could provide:
Behaviour Monitoring Dashboard
536 vessels monitored
487 — Normal
31 — Minor anomalies
12 — Moderate anomalies
6 — High-priority review
The AI could rank the most important cases.
Highest-Priority Vessels
MV Atlantic Trader — Score 89
AIS gap + unusual encounter + route deviation
MV Ocean Pioneer — Score 83
Unexpected port call + major speed reduction
MV Global Energy — Score 78
Unusual offshore stop + destination change
This could be useful to:
shipping companies;
insurers;
commodity traders;
compliance teams;
maritime-security analysts;
port authorities;
logistics companies.
19. Africa-Focused Maritime Behaviour Analytics
VesselPing could differentiate itself by developing stronger anomaly intelligence for important African maritime corridors.
Potential focus regions include:
Gulf of Guinea
West African energy corridors
Cape of Good Hope
Mozambique Channel
Red Sea approaches
East African shipping routes
AI could learn region-specific operational patterns.
For example, a behaviour that is normal near a highly congested anchorage may be abnormal in a remote offshore region.
Regional intelligence would therefore improve both accuracy and commercial usefulness.
20. The Architecture of a VesselPing Anomaly Engine
A future VesselPing abnormal-behaviour system could be structured as:
Terrestrial AIS + Satellite AIS
↓
Historical Vessel Tracks
↓
Vessel Registry & Characteristics
↓
Ports + Anchorages + Geofences
↓
Weather + Ocean Conditions
↓
Nearby Vessel Activity
↓
AI Behavioural Analysis Engine
↓
Route Anomaly Detection
Speed Anomaly Detection
AIS Gap Analysis
Encounter Detection
Destination Change Detection
Unusual Stop Detection
Geofence Analysis
↓
Combined Anomaly Score
↓
AI Explanation
What happened?
Why is it unusual?
How does it compare with history?
What alternative explanations exist?
Should the user continue monitoring?
Avoiding a Critical Mistake: Abnormal Does Not Mean Illegal
This distinction must be built into VesselPing from the beginning.
AI may identify behaviour as:
unusual
unexpected
statistically abnormal
worthy of investigation
But none of those automatically means:
illegal
fraudulent
dangerous
or
criminal
A vessel could behave unusually for entirely legitimate operational reasons.
Therefore, VesselPing should use careful language such as:
“Unusual behaviour detected.”
rather than:
“Illegal activity detected.”
unless independent verified evidence supports such a conclusion.
This approach would make the platform more credible and reduce the risk of misleading users.
From Watching Ships to Understanding Behaviour
Vessel tracking is becoming increasingly sophisticated.
The first generation of maritime platforms answered:
Where is the vessel?
The next generation began answering:
Where has it been?
AI-powered platforms can move further:
Is the vessel behaving normally?
How is its behaviour different?
What may explain the change?
Does this combination of events deserve closer attention?
That represents a fundamental shift from tracking to behavioural maritime intelligence.
For VesselPing, abnormal-behaviour detection could eventually become one of its most valuable capabilities.
Rather than displaying every vessel movement with equal importance, the platform could help customers identify the few movements that genuinely stand out.
A conventional system might show a ship changing course.
An advanced VesselPing system could explain:
“This vessel has deviated substantially from its historical route, reduced speed in open water, experienced an unusual AIS interruption and subsequently remained close to another vessel for several hours. The combination differs significantly from its normal operating pattern and warrants closer monitoring.”
That is not merely a vessel position.
It is AI-generated maritime intelligence.
And that distinction could help transform VesselPing from a vessel-tracking website into a serious maritime analytics and decision-support platform.
Sponsored by vesselping.com
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