1. Efficient AIS Data Processing for Environmentally Safe Shipping
Marios Vodas1, Nikos Pelekis1, Yannis Theodoridis1, Cyril Ray2, Vangelis Karkaletsis3, Sergios Petridis3, Anastasia Miliou4
1 University of Piraeus 2 Naval Academy, France 3 NCSR “Demokritos” 4 Archipelago – Inst. of Marine Conservation

2. Outline Part I: Marine Transportation
Part II: Automatic Identification System (AIS)
Part III: Objectives
Part IV: Methodology
Part V: Conclusion

3. i. MARITIME TRANSPORTATION

4. Safety (and Environmental) Issues
Ships, control centers and marine officers have to face many security and safety problems due to:
Staff reduction, cognitive overload, human errors
Traffic increase (ports, maritime routes), dangerous contents
Terrorism, pirates
Technical faults (bad design, equipment breakdowns)
Bad weather
Etc.
HELCOME AIS
IRENav (NATO)
MarineTraffic.com

5. The Most Prominent Cause of Accidents
About 75-96% of marine casualties are caused, at least in part, by some form of human error * :
88% of tanker accidents
79% of towing vessel groundings
96% of collisions
75% of fires and explosions
Solution to such issues requires different levels of responses taking into account :
People (activities)
Technology
Environment
Organisational factors
*Rothblum A.M. (2006) “Human Error and Marine Safety”, U.S. Coast Guard Research & Development Center

6. Ways to Minimize Accidents
Level of education and practice for mariners
Work safety regulations (behaviour guidelines, normalised onboard equipments)
Navigation and decision support systems providing real-time information, predictions, alerts...
Integrate and use properly multiple and heterogeneous positioning systems : AIS, ARPA, Long Range Identification System (LRIT), Global Maritime Distress and Safety System (GMDSS), synthetic aperture radar, airborne radar, satellite based sensors
Generalisation of vessel traffic monitoring, port control, search and rescue systems, automatic communications

7. Traffic Monitoring Air-based support
Human and semi-automatic monitoring
On-demand and on a regular basis
Remote Sensing support
Semi-automatic monitoring
Every 2 to 6 hours
Sensor-based support
Almost automatic analysis and monitoring
Real-time

8. iI. Automatic Identification System (AIS)

9. → AIS is mandatory (IMO) for big ships and passengers’ boats
AIS Device
The Automatic Identification System identifies and locates vessels at distance
It includes an antenna, a transponder, a GPS receiver and additional sensors (e.g., loch and gyrocompass)
It is a broadcast system based on VHF communications
It is able to operate in autonomous and continuous mode
Ships fitted with AIS send navigation data to surrounding receivers (range is about 50 km)
Ships or maritime control centres on shore fitted with AIS receives navigation data sent by surrounding ships
→ AIS is mandatory (IMO) for big ships and passengers’ boats

10. AIS Transmission Rate and Accuracy
AIS accuracy is defined as the largest distance the ship can cover between two updates
The AIS broadcasts information with different rates of updates depending on the ship’s current speed and manoeuvre
The IMO assumes that accuracy of embedded GPS is 10m
Vessel behaviour
Time between updates
Accuracy (m)
Anchored
3 min
= 10 metres
Speed between 0-14 knots
12 s
Between 10 and 95 metres
Speed between 0-14 knots and changing course
4 s
Between 10 and 40 metres
Speed between knots
6 s
Between 55 and 80 metres
Speed between knots and changing course
2 s
Between 25 and 35 metres
Speed over 23 knots
3 s
> 45 metres
Speed over 23 knots and changing course
> 35 metres
General update rules have been compared to reality: it appears that update rates are lower

11. AIS Data
The AIS provide location-based information on 2D routes, this defining point-based 3D trajectories
Transmitted data include ship’s position and textual meta-information
Static: ID number (MMSI), IMO code, ship name and type, dimensions
Dynamic: Position (Long, Lat), speed, heading, course over ground (COG), rate of turn (ROT)
Route-based: Destination, danger, estimated time of arrival (ETA) and draught
That is, an ordered series of locations (X,Y,T) of a given mobile object O with T indicating the timestamp of the location (X,Y)
→ Time does not exist in AIS frames : to be add by receivers
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12. iII. Objectives

13. Big AIS Data Processing for Environmentally Safe Shipping
Objectives, based on Archipelagos Institute of Marine Conservation requests, was to
Investigate factors which contribute most to the risk of a shipping accident
Identify dangerous areas
How : traffic database processing in order to address some requirements / queries set by Archipelagos towards semi-quantitative risk analysis of shipping traffic
→ Data coming from AIS
→ Application to the Aegean Sea

14. Typical Questions From Domain Experts
Calculate average and minimum distances from shore or between two ships
Calculate the maximum number of ships in the vicinity of another ship
Find whether (and how many times) a ship goes through specified areas (e.g. narrow passages, biodiversity boxes)
Calculate the number of sharp changes in ship’s direction
Find typical routes vs. outliers
etc. etc.

15. Mediterranean Sea
European Maritime Safety Agency (EMSA) centralizes data from EU states and provides them through a Web service
We worked on a dataset on Mediterranean sea provided By IMIS Hellas (a Greek IT company related to IMIS Global, collecting AIS data, mariweb.gr)
→ Data Volume is 100 million positions per month, that is about 2300 positions per minutes
Focus on Aegean sea : 3 days, 3 million position records (933 distinct ships)
Full dataset is more than 2000 SQL tables for a total of 2 TB covering 2,5 years of vessel activity
Two datasets are available at Chorochronos.org interface (IMIS 3 days and AIS Brest)

16. Vessel Statistics Country Number of ships Flag of Convenience Greece
263 No
Panama (Republic of)
112
Yes
Turkey 96
Malta 76
Liberia (Republic of) 32
Vincent and the Grenadines 29

17. iV. Methodology

18. Populating a Database Relational database (postgres and postgis)
Data model based on AIS messages : positions, ships and trips
Parsing, Integration, error checking filtering
Reconstructing trajectories from raw data and feeding a trajectory DB
Apply “simple” queries to answer experts needs
“What is the (sub)trajectory of a ship during its presence in an area” ?

19. MOD Engine and Rule-Based Analysis
Mixed top-down / bottom-up approach involving an expert monitoring real-time traffic on a touch table
An integrated approach for maritime situation awareness based on an inference engine (drools)
The expert defines his rules according its needs and objectives
The engine executes rules using the AIS database
Hermes is a MOD engine providing extensible DBMS support for trajectory data
Defines trajectory data type
SQL extensions at the logical level
Efficient indexing techniques at the physical level
Includes trajectory clustering support

20. Methodology Steps

21. Take the Maritime Environment Into Account
The maritime domain is peculiar as there is no underlying network but some maritime rules define predefined paths and anchorage areas (polylines and polygons) that might constrain a given trajectory
S-57 ENC (Electronic Nautical Chart)
We added official vector chart and expert-defined areas of interest in the database
Coastlines
Starting, ending, passing, restricted areas, waiting zones
Regulations and dangers (rocs, buoys, seabed) …

22. Exploring the Data
Calculating trajectory aggregations and feeding a trajectory data warehouse
Performing OLAP analysis over aggregations (eg. O/D analysis)
Running KDD techniques : frequent pattern analysis, clustering, outlier detection, etc.
Association of points coming from the same source-destination set
Definition of a route and qualifying of positions at each time
Qualifying of a new trajectory compared to the identified route
Cloud of locations

23. Visualizing Trajectories and Patterns
→ Web-based visualisation using Google Maps / Earth applications, Openlayers (OSM)
frequent patterns
speed behaviour
space-time cube: trajectory too far on the right →
← space-time cube: ship is late

24. V. Conclusion

25. Some Open Questions
Q1. What kind of storage is appropriate for BIG volumes of vessel traffic data?
Serial vs. parallel/distributed processing (e.g. Hadoop)
(batch vs. streaming) MOD engines?
What about indexing BIG mobility data?
Q2. What kind of analysis on vessel traffic data makes sense?
Analysis on current (location, speed, heading, …) vs. historical information (trajectories)
Clusters (+ outliers), frequent patterns, next location prediction, etc.
Exploit on previous knowledge to improve real-time analysis
Q3. What kind of visualization is appropriate for vessel traffic data / patterns
Current location vs. trajectory-based visual analytics
Trajectory clustering
Frequent pattern mining

26. Research Challenges on Data – Just a Few Examples
Trajectory compression / simplification: how to compress / simplify trajectories keeping quality as high as possible?
Semantic trajectory reconstruction: how to extract semantics from raw (GPS-based) trajectory data?
Trajectory sampling: how to find a representative sample among a trajectory dataset?
Generating trajectories by example: how to build large synthetic datasets that simulate the ‘behavior’ of a small real one?
Etc.

27. Questions