How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications

2019 ◽  
Vol 39 (6) ◽  
pp. 755-773 ◽  
Author(s):  
Dong Yang ◽  
Lingxiao Wu ◽  
Shuaian Wang ◽  
Haiying Jia ◽  
Kevin X. Li
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


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

2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.


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