Ship Operational Efficiency from AIS Data Using Big Data Technology

Author(s):  
Suraj Ingle

Abstract: The Energy Efficiency Design Index (EEDI) is a necessary benchmark for all new ships to prevent pollution from ships. MARPOL has also applied the Ship Energy Efficiency Management Plan (SEEMP) to all existing ships. The Energy Efficiency Operational Indicator (EEOI) provided by SEEMP is used to measure a ship's operational efficiency. The shipowner or operator can make strategic plans, such as routing, hull cleaning, decommissioning, new construction, and so on, by monitoring the EEOI. Fuel Oil Consumption is the most important factor in calculating EEOI (FOC). It is possible to measure it when a ship is in operation. This means that the EEOI of a ship can only be calculated by the shipowner or operator. Other stakeholders, such as the shipbuilding firm and Class, or those who do not have the measured FOC, can assess how efficiently their ships are working relative to other ships if the EEOI can be determined without the real FOC. We present a method to estimate the EEOI without requiring the actual FOC in this paper. The EEOI is calculated using data from the Automatic Identification System (AIS), ship static data, and publicly available environmental data. Big data technologies, notably Hadoop and Spark, are used because the public data is huge. We test the suggested method with real data, and the results show that it can predict EEOI from public data without having to use actual FOC Keywords: Ship operational efficiency, Energy Efficiency Operational Indicator (EEOI), Fuel Oil Consumption (FOC), Automatic Identification System (AIS), Big data

2021 ◽  
Vol 9 (4) ◽  
pp. 438
Author(s):  
Min-Jae Oh ◽  
Myung-Il Roh ◽  
Sung-Woo Park ◽  
Do-Hyun Chun ◽  
Myeong-Jo Son ◽  
...  

The shipping company or the operator determines the mode of operation of a ship. In the case of container ships, there may be various operating patterns employed to arrive at the destination within the stipulated time. In addition, depending on the influence of the ocean’s environmental conditions, the speed and the route can be changed. As the ship’s fuel oil consumption is closely related to its operational pattern, it is possible to identify the most economical operations by analyzing the operational patterns of the ships. The operational records of each shipping company are not usually disclosed, so it is necessary to estimate the operational characteristics from publicly available data such as the automatic identification system (AIS) data and ocean environment data. In this study, we developed a visualization program to analyze the AIS data and ocean environmental conditions together and propose two categories of applications for the operational analysis of container ships using maritime big data. The first category applications are the past operation analysis by tracking previous trajectories, and the second category applications are the speed pattern analysis by shipping companies and shipyards under harsh environmental conditions. Thus, the operational characteristics of container ships were evaluated using maritime big data.


Author(s):  
Carsten Hilgenfeld ◽  
Nina Vojdani ◽  
Frank Heymann ◽  
Evamarie Wiessner ◽  
Bettina Kutschera ◽  
...  

For the international exchange of goods, an exact estimated time of arrival (ETA), especially in case of delays, is of great importance. Using global data of the automatic identification system (AIS) a grid node is generated. The sum of such nodes and their connections form a routing graph. As an example, with one node of in total more than 100,000 nodes it is described how this point gets the maximum vessel length and draft assigned.


2019 ◽  
Vol 16 (2) ◽  
pp. 159
Author(s):  
Abdul Karim

Nasional Institute Aerounautics and Space (LAPAN) has two satellites (LAPAN-A2 and LAPAN-A3) that are carry Automatic Identification System (AIS) sensors. It can be use for monitoring Indonesian maritime. The altitude of the satellite about 642 Km and 500 km so it has a wide area covered and receive big data. The problem is the AIS technology use the Time Division Multiple Access (TDMA) system that has limitations in handling big data so that some data received can be damaged due to collision. Therefore, in this research has been done the analysis and correction data using interpolation and extrapolation methods. The results  is improvements of valid data about 22,6 % for LAPAN-A2 satellite and 20,8 % for LAPAN-A3 satellite.


Author(s):  
Febus Reidj G. Cruz ◽  
Jeremiah A. Ordiales ◽  
Malvin Angelo C. Reyes ◽  
Pinky T. Salvanera

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