Efficient AIS Data Processing for
Environmentally Safe Shipping
Marios Vodas
1
, Nikos Pelekis
1
,
Yannis
Theodoridis
1
,
Cyril Ray
2
, Vangelis Karkaletsis
3
,
Sergios
Petridis
3
,
Anastasia Miliou
4
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.
4
MarineTraffic.com
HELCOME
AIS
IRENav
(NATO)
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
5
*
Rothblum
A.M.
(2006) “Human Error and Marine
Safety”, U.S. Coast Guard Research & Development
Center
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
7
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
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
9
→
AIS is mandatory (IMO) for big ships and
passengers’ boats
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
10
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 14
-
23 knots
6 s
Between 55 and 80 metres
Speed between 14
-
23 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
2 s
> 35 metres
General update rules have been compared to reality: it appears that update rates are lower
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
11
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
!AIVDM,1,1,,A,1Bwj:v0P1=1f75REQg>rPwv:0000,0*3B
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 semi
-
quantitative risk analysis of shipping
traffic
13
→
Data
coming from
AIS
→
Application to the Aegean Sea
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
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
)
15
→
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)
Vessel Statistics
16
Country
Number of ships
Flag
of
Convenience
Greece
263
No
Panama
(
Republic
of)
112
Yes
Turkey
96
No
Malta
76
Yes
Liberia
(Republic
of)
32
Yes
Vincent
and
the
Grenadines
29
Yes
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
18
“
What
is the (sub)trajectory of a ship during its
presence in an area
” ?
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
19
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
Mixed top
-
down / bottom
-
up approach
involving an expert monitoring real
-
time
traffic on a touch table
http://
infolab.cs.unipi.gr/hermes
Methodology Steps
20
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
Hermes Loader
Degrees to Meters
Trajectory Update
Outputs Trajectories
Querying
Timeslice
Range
Temporal only
Spatial only
Spatio
-
Temporal
Nearest Neighbor
(
NN
)
wrt
.
a reference stati c object
(
point
/
segment
/
box
)
wrt
.
a reference trajectory
Advanced Querying
Pair
-
wise similarity queries
OD
-
Matrix
origin
/
destination are spatial vs
.
spatio
-
temporal boxes
Trajectory Clustering
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
21
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
)
…
S
-
57 ENC (Electronic Nautical
Chart)
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.
22
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
Visualizing Trajectories and Patterns
23
←
space
-
time cube:
ship is late
space
-
time cube:
trajectory too far
on the right
→
s
peed
behaviour
frequent
patterns
→
Web
-
based
visualisation
using
Google Maps / Earth applications,
Openlayers
(OSM)
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
Q3. What kind of
visualization
is appropriate for
vessel traffic
data /
patterns
Current location vs. trajectory
-
based visual
analytics
25
Trajectory clustering
Frequent pattern
mining
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
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