scholarly journals Operational Analysis of Container Ships by Using Maritime 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 ◽  
...  
Keyword(s):  
Big Data ◽  
Environmental Conditions ◽  
Fuel Oil ◽  
Automatic Identification ◽  
Identification System ◽  
Oil Consumption ◽  
Shipping Company ◽  
Operational Analysis ◽  
Operational Characteristics ◽  
Ocean Environment

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.

Ship Operational Efficiency from AIS Data Using Big Data Technology

2022 ◽  
Vol 10 (1) ◽  
pp. 89-97
Author(s):  
Suraj Ingle
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Operational Efficiency ◽  
Fuel Oil ◽  
Automatic Identification ◽  
Identification System ◽  
Oil Consumption ◽  
Automatic Identification System ◽  
Public Data ◽  
Ship Operational Efficiency

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

Evaluation of Ship Performance Using Operation Records

2018 ◽  
Author(s):  
Kenichi Fukuda ◽  
Yoshihisa Okada ◽  
Akinori Okazaki ◽  
Hiroyuki Adachi ◽  
Yuichiro Hisamuara ◽  
...  
Keyword(s):  
Big Data ◽  
Fuel Oil ◽  
Time Dependent ◽  
Oil Consumption ◽  
Sea Trial ◽  
Before And After ◽  
Report Data ◽  
Unique Approach ◽  
Performance Conditions ◽  
Ship Performance

Recently, the big data can be employed as the economical ship operating or evaluation of ship performance conditions. However, such data cannot be easily obtained and analyzed for every ship. In this case, for example, an evaluation of ship performance during operation is usually dependent on ship owner’s experience. The time-dependent ship performance is an essential topic for ship owners because if they realize their current ship performance, they can implement something such as hull or propeller cleaning for their economical operation. This study is focused on the usage of noon report data rather than the big data due to their obtainability. Usually, such data are considered as references because different ship operational condition and environmental condition obscure current ship performance. However, our unique approach, which is used integrally the noon report data such as BHP, propeller revolution and fuel oil consumption, ship sea trial data and propeller performance, can be evaluated ship performance during ship in service. The analyzed output data can be produced as increasing of ship resistance (delta Rw) versus ship performance efficiency, fuel oil consumption (ton per day) or sea margin. Under this output conditions, it can be comparable at same conditions even though the conditions of operations are different. Therefore, this analyzed data has a potential ability to have a look at ship performance conditions during ship in service. The purpose of this paper is to introduce our unique approach using noon data for time-dependent ship performance and then discuss the verification of this approach. As the case study, the noon report data for Japanese domestic bulker was chosen and the ship performance was evaluated in terms of different points of views. It was done comparing the conditions of before and after dry dock to evaluate our approach. In addition, the potential application of this approach will be discussed in this paper.

scholarly journals Deep Learning in Unmanned Surface Vehicles Collision-Avoidance Pattern Based on AIS Big Data with Double GRU-RNN

2020 ◽  
Vol 8 (9) ◽  
pp. 682
Author(s):  
Jia-hui Shi ◽  
Zheng-jiang Liu
Keyword(s):  
Big Data ◽  
Collision Avoidance ◽  
Difficult Problem ◽  
Automatic Identification ◽  
Identification System ◽  
Unmanned Surface Vehicle ◽  
Effective Learning ◽  
Maritime Navigation ◽  
Ship Collision ◽  
Gated Recurrent Unit

There is a collection of a large amount of automatic identification system (AIS) data that contains ship encounter information, but mining the collision avoidance knowledge from AIS big data and carrying out effective machine learning is a difficult problem in current maritime field. Herein, first the Douglas–Peucker (DP) algorithm was used to preprocess the AIS data. Then, based on the ship domain the risk of collision was identified. Finally, a double-gated recurrent unit neural network (GRU-RNN) was constructed to learn unmanned surface vehicle (USV) collision avoidance decision from the extracted data of successful encounters of ships. The double GRU-RNN was trained on the 2015 Tianjin Port AIS dataset to realize the effective learning of ship encounter data. The results indicated that the double GRU-RNN could effectively learn the collision avoidance pattern hidden in AIS big data, and generate corresponding ship collision-avoidance decisions for different maritime navigation states. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could be potentially applied to USV technology and intelligence collision avoidance.

scholarly journals MARITIME BIG DATA ANALYSIS WITH ARLAS

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 71-76
Author(s):  
W. Gautier ◽  
S. Falquier ◽  
S. Gaudan
Keyword(s):  
Big Data ◽  
Open Source ◽  
Big Data Analysis ◽  
Machine Learning Algorithms ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Industry ◽  
Huge Amount ◽  
Processing Platform

Abstract. The maritime industry has become a major part of globalization. Political and economic actors are meeting challenges regarding shipping and people transport. The Automatic Identification System (AIS) records and broadcasts the location of numerous vessels and delivers a huge amount of information that can be used to analyze fluxes and behaviors. However, the exploitation of these numerous messages requires tools based on Big Data principles.Acknowledgement of origin, destination, travel duration and distance of each vessel can help transporters to manage their fleet and ports to analyze fluxes and focus their investigations on some containers based on their previous locations. Thanks to the historical AIS messages provided by the Danish Maritime Authority and ARLAS PROC/ML, an open source and scalable processing platform based on Apache SPARK, we are able to apply our pipeline of processes and extract this information from millions of AIS messages. We use a Hidden Markov Model (HMM) to identify when a vessel is still or moving and we create “courses”, embodying the travel of the vessel. Then we derive the travel indicators. The visualization of results is made possible by ARLAS Exploration, an open source and scalable tool to explore geolocated data. This carto-centered application allows users to navigate into the huge amount of enriched data and helps to take benefits of these new origin and destination indicators. This tool can also be used to help in the creation of Machine Learning algorithms in order to deal with many maritime transportation challenges.

Ship Surveillance by Integration of Space-borne SAR and AIS – Review of Current Research

2013 ◽  
Vol 67 (1) ◽  
pp. 177-189 ◽  
Author(s):  
Zhi Zhao ◽  
Kefeng Ji ◽  
Xiangwei Xing ◽  
Huanxin Zou ◽  
Shilin Zhou
Keyword(s):  
State Of The Art ◽  
Rapid Development ◽  
Automatic Identification ◽  
Identification System ◽  
Environment Monitoring ◽  
Ship Detection ◽  
Maritime Surveillance ◽  
Satisfactory Performance ◽  
Detection And Identification ◽  
Ocean Environment

Ship surveillance is important for maritime security and safety. It plays important roles in many applications including ocean environment monitoring, search and rescue, anti-piracy and military reconnaissance. Among various sensors used for maritime surveillance, space-borne Synthetic Aperture Radar (SAR) is valued for its high resolution over wide swaths and all-weather working capabilities. However, the state-of-the-art algorithms for ship detection and identification do not always achieve a satisfactory performance. With the rapid development of space-borne Automatic Identification System (AIS), near real-time and global surveillance has become feasible. However, not all ships are equipped with or operate AIS. Space-borne SAR and AIS are considered to be complementary, and ship surveillance using an integrated combination has attracted much attention. In order to summarize the achievements and present references for further research, this paper attempts to explicitly review the developments in previous research as the basis of a brief introduction to space-borne SAR and AIS.

scholarly journals Generating a node in an AIS-based routing graph for improved Estimated Time of Arrival. (Big) Data challenge: using AIS for generating a routing graph

2019 ◽  
pp. 45-56
Author(s):  
Carsten Hilgenfeld ◽  
Nina Vojdani ◽  
Frank Heymann ◽  
Evamarie Wiessner ◽  
Bettina Kutschera ◽  
...  
Keyword(s):  
Big Data ◽  
International Exchange ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Grid Node ◽  
Vessel Length ◽  
Automatic Identification System ◽  
Global Data

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.

scholarly journals Big Data on Vessel Traffic

2019 ◽  
Vol 2019 (275) ◽  
Author(s):  
Serkan Arslanalp ◽  
Marco Marini ◽  
Patrizia Tumbarello
Keyword(s):  
Big Data ◽  
Real Time ◽  
Automatic Identification ◽  
Identification System ◽  
Traffic Data ◽  
Data Source ◽  
Statistical Agencies ◽  
New Statistics ◽  
Existing Data

Vessel traffic data based on the Automatic Identification System (AIS) is a big data source for nowcasting trade activity in real time. Using Malta as a benchmark, we develop indicators of trade and maritime activity based on AIS-based port calls. We test the quality of these indicators by comparing them with official statistics on trade and maritime statistics. If the challenges associated with port call data are overcome through appropriate filtering techniques, we show that these emerging “big data” on vessel traffic could allow statistical agencies to complement existing data sources on trade and introduce new statistics that are more timely (real time), offering an innovative way to measure trade activity. That, in turn, could facilitate faster detection of turning points in economic activity. The approach could be extended to create a real-time worldwide indicator of global trade activity.

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