Environmentally Safe Shipping

pogonotomygobbleAI and Robotics

Nov 15, 2013 (3 years and 4 months ago)

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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