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
1
Outline
1.
Part I: Marine Transportation
2.
Part II: Automatic Identification System (AIS)
3.
Part III: Objectives
4.
Part IV: Methodology
5.
Part V: Conclusion
2
I. MARITIME TRANSPORTATION
3
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
4
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
*Rothblum A.M. (2006) “Human Error and Marine
Safety”, U.S. Coast Guard Research & Development
Center
Solution to such issues requires different levels of responses
taking into account :




People (activities)
Technology
Environment
Organisational factors
5
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
6
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
7
II. AUTOMATIC IDENTIFICATION
SYSTEM (AIS)
8
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
9
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
4s
Between 10 and 40 metres
Speed between 14-23 knots
6s
Between 55 and 80 metres
Speed between 14-23 knots
and changing course
2s
Between 25 and 35 metres
Speed over 23 knots
3s
> 45 metres
Speed over 23 knots and
changing course
2s
> 35 metres
General update rules have been compared to reality: it appears that update rates are lower
10
AIS Data

The AIS provide location-based information on 2D routes, this
defining point-based 3D trajectories
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)

Transmitted data include ship’s position and textual metainformation



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
→ Time does not exist in AIS frames : to be add by receivers
!AIVDM,1,1,,A,1Bwj:v0P1=1f75REQg>rPwv:0000,0*3B
11
III. OBJECTIVES
12
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 semiquantitative risk analysis of shipping traffic
→ Data coming from AIS
→ Application to the Aegean Sea
13
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.
14
Mediterranean Sea

European Maritime Safety Agency (EMSA) centralizes data
from EU states and provides them through a Web service
→ Data Volume is 100 million positions per month, that is about 2300
positions per minutes

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)
• 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)
15
Vessel Statistics
Country
Greece
Number of ships Flag of Convenience
263 No
Panama (Republic of)
112 Yes
Turkey
96 No
Malta
76 Yes
Liberia (Republic of)
32 Yes
Vincent
and
Grenadines
the
29 Yes
16
IV. METHODOLOGY
17
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” ?
18
MOD Engine and Rule-Based Analysis


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
Mixed top-down / bottom-up approach
involving an expert monitoring real-time
traffic on a touch table
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
http://infolab.cs.unipi.gr/hermes
19
Methodology Steps
Cleaning
Filter:
 Wrong CRC
 Duplicates
Decoding
AIS type:
 1/2/3  Position Report

5  Static and Voyage Related Data
Cleaning
Filter:
 Invalid MMSI
 GPS Error

Querying
 Timeslice
 Range



Temporal only
Spatial only
Spatio-Temporal

wrt. a reference static object
(point / segment / box)
wrt. a reference trajectory
 Nearest Neighbor (NN)

Hermes Loader
 Degrees to Meters
 Trajectory Update
 Outputs Trajectories
Advanced Querying
 Pair-wise similarity queries
 OD-Matrix
 origin/destination are spatial vs.
spatio-temporal boxes
 Trajectory Clustering
20
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 expertdefined areas of interest in the database
Coastlines
Starting, ending, passing, restricted
areas, waiting zones
Regulations and dangers (rocs, buoys,
seabed)
…
21
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.
Cloud of locations
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
22
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 →
← spacetime cube:
ship is late
23
V. CONCLUSION
24
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
Trajectory clustering
Q3. What kind of visualization is appropriate for vessel traffic
data / patterns

Current location vs. trajectory-based visual analytics
Frequent pattern mining
25
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.
26
Questions
27