Institute of Informatics and Telecommunications – NCSR “Demokritos”

Shipping Routes Project

Scott PhanAn Nguyen

Presentation27, June 2011

studyabroad.iit.demokritos.gr

ML & DM Lab 2

Shipping in the Aegean Sea

Mediterranean Sea supports between 4-18% of the worlds species

Aegean Sea is an area of the Mediterranean which carries high biological importance, due to the relatively low coastal development.

However, the preservation of this ecosystem is being left largely to chance, with few protection measures in place. If damage to the area increases or a major event the results could be severe.

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ML & DM Lab 3

Shipping in the Aegean Sea

Hundreds of cargo, tanker and passenger ships pass through the Aegean Sea every day. The potential impacts of shipping, commercial and recreational, are vast.

Ships can affect marine biota in the following ways:– Underwater noise created by ships– Anchoring– Grounding– Direct collisions– Carrying invasive species– Operational oil discharges– Accidental oil discharges– Thermal Discharges

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ML & DM Lab 4

Project Motivation

The Aegean Sea lacks efficient mechanisms to manage, monitor and regulate ship traffic conditions.

To decrease the chance of ecological disturbance events, strict shipping lanes must be established and enforced.

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ML & DM Lab 5

Project Motivation

Over the last few months, the marine and GIS teams from Archipelagos have been working on a major shipping project.

From November 12, 2009 to April 29, 2010, data on all of the

tankers and cargos that travelled between Samos, Ikaria, Mykonos, Andros, and Nisos Evia were recorded.

The data was received from www.marinetraffic.com and www.mariweb.gr, which track and record data from shipping vessels.

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ML & DM Lab 6

Accumulated Ship Trajectories in the Aegean

Article:“Update on Shipping Data collection at Archipelagos”- Chris Fletcher

Linkhttp://workjournal.archipelago.gr/?p=1348

Project Motivation

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ML & DM Lab 7

Project Goals

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Database Design/Implementations

Import/Decode AIS data

GUI Design/Data Visualization

Data Mining / Risk Management

ML & DM Lab 8

Project Goals: Details

1. Create a Graphical User Interface (GUI) [done]

2. Place ship markers at arbitrary pixel locations on the map [done]

3. Parse the AIS database and plot each ship at a given time interval on the map [done]

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ML & DM Lab 9

Project Goals: Details

4. Create a function to assign a scalar “risk value” for each ship given certain database attributes for each ship (cargo, size, proximity to coast and other ships, flag, etc) [In Progress]

5. Create a visual identifier to show the risk of each ship (change color, display numeric label, etc) [done]

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ML & DM Lab 10

Project Goals

7. Create a function to assess the risk of a particular map pixel location based on the number and proximity of ships within a given radius and proximity to land [In Progress]

8. Create a map view which shows risky areas in red and less risky areas in green/blue. This “risk density map [In Progress]

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ML & DM Lab 11

Approach: Area of Interest

Spatial Bounds

Lat [35, 39]Lon [21, 29]Aegean Sea

Ignore all data that falls outside the boundaries

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ML & DM Lab 12

Approach: About the Input

Huge data collections Good facilities to collect real time data Accurate information to predict the trend of routes and risk

management

Used dataset provided by International Maritime Information Systems (IMIS)– Covers 2 days worth of AIS messages

Challenges with the IMIS dataset:– Raw data - not decoded– Not well managed

Redundant data (3x redundant)– Database not as supportive for GIS development

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ML & DM Lab 13

Approach: About the Input

Automatic Identification System (AIS)– is an automated tracking system used on ships and by Vessel Traffic

Services (VTS) for identifying and locating vessels by electronically exchanging data with other nearby ships and VTS stations.

– AIS information supplements marine radar, which continues to be the primary method of collision avoidance for water transport.

Types of Info. Encapsulated within the Messages– Static [MMSI number, IMO number, callsign, ship name and type,

dimension]– Dynamic [position, time, speed, heading, course over ground,

rate of turn, navigational status]– Trajectory-based [destination, estimated time of arrival, draught]

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ML & DM Lab 14

Approach: About the Input

Encoded Spatial Data within the Database

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ML & DM Lab 15

Approach: About the Input

Decoding the Encoded Spatial Data

Input : 0101000020E6100000000000604A653840000000E0FBB14240

Decoding

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ML & DM Lab 16

Approach: Software Architecture

Database

IMIS Database NASA 3D maps API

Graphic User Interface

GUI

Control

Raw Data 3D maps

Controller

SQL Query

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ML & DM Lab 17

Approach: Graphical User Interface

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ML & DM Lab 18

Approach: Data Flow Architecture (Part I)

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ML & DM Lab 19

Approach: Data Flow Architecture

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ML & DM Lab 20

Approach: Output - Visualization

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ML & DM Lab 21

Approach: Output - Visualization

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One more thing

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How we get data?

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MySQL dump files

Extracted AIS Data

PostgreSQL/PostGIS

How long does it take?

3hours/file

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SQL Importing Tool

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Demo

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ML & DM Lab 27

Future Work

1. Prepare paper for publishing

2. Implement proximity function to find nearest land from a given point in the sea

3. Design and implement the data mining API and UI

4. Research shipping risk assessment methods

5. Implement density plotting with respect to risk assessment of a spatial area

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ML & DM Lab 28

Acknowledgements

Archipelagos - Institute of Marine Conservation “KNOWLEDGE DISCOVERY FROM MARITIME MOVING OBJECTS - APPLICATION

TO AEGEAN SEA” – Cyril Ray, Naval Academy Research Lab, December 2010

NCSR Demokritos

University of The Aegean

International Maritime Information Systems (IMIS)

CSE Dept. @ University of Texas at Arlington

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Questions?

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