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.

Ship Trajectories Pre-processing Based on AIS Data

2018 ◽  
Vol 71 (5) ◽  
pp. 1210-1230 ◽  
Author(s):  
Liangbin Zhao ◽  
Guoyou Shi ◽  
Jiaxuan Yang
Keyword(s):  
Data Mining ◽  
Traffic Density ◽  
Automatic Identification ◽  
Identification System ◽  
Traffic Data ◽  
Density Maps ◽  
Different Types ◽  
Quality Dimensions ◽  
Time Accuracy

Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands from January to February 2015. The results indicate that the algorithms can effectively improve the quality of AIS trajectories.

scholarly journals Big data actionable intelligence architecture

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tian J. Ma ◽  
Rudy J. Garcia ◽  
Forest Danford ◽  
Laura Patrizi ◽  
Jennifer Galasso ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Digital Media ◽  
Decision Makers ◽  
Time Data ◽  
Human In The Loop ◽  
Course Of Action ◽  
Insufficient Time ◽  
Data Source ◽  
Different Sources

AbstractThe amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.

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

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.

Data Reception Analysis of the AIS on board the TianTuo-3 Satellite

2017 ◽  
Vol 70 (4) ◽  
pp. 761-774 ◽  
Author(s):  
Shiyou Li ◽  
Xiaoqian Chen ◽  
Lihu Chen ◽  
Yong Zhao ◽  
Tao Sheng ◽  
...  
Keyword(s):  
Detailed Analysis ◽  
Real Time ◽  
Long Range ◽  
Automatic Identification ◽  
Identification System ◽  
Detection Ability ◽  
Class A ◽  
Frequency Detection ◽  
Real Time Tracking ◽  
New Generation

The Automatic Identification System (AIS) receiver on board the main satellite of the TianTuo-3 constellation, LvLiang-1, is a new generation of AIS receiver. Having partly solved the signal conflict problems and with larger coverage over the ground, the AIS receiver on board TianTuo-3 greatly improves the signal detection ability. The data received by the AIS receiver during the TianTuo-3 debugging stage is employed for detailed analysis in this paper. Results include: TianTuo-3 implements four-frequency detection at the same time, and a time-flag is inserted into the received AIS data, a small portion of Class A vessels (at least 1480) have been equipped with AIS sending the long range AIS broadcast message with two new frequency channels and the hourly averaged count of the message received by TianTuo-3’s AIS is between 1500 ~ 2500. This AIS receiver is capable of real-time tracking a single vessel. In conclusion, the TianTuo-3 space-based AIS receiver is capable of continuously receiving AIS messages sent by global maritime vessels.

scholarly journals Near Real-time S-AIS

Pomorstvo ◽  
2018 ◽  
Vol 31 (2) ◽  
pp. 211-218
Author(s):  
Davor Šakan ◽  
Igor Rudan ◽  
Srđan Žuškin ◽  
David Brčić
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Service Providers ◽  
Traffic Monitoring ◽  
Environmental Research ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Industry ◽  
Recent Developments ◽  
Processing Techniques

The Automatic identification System (AIS) has been mainly designed to improve safety and efficiency of navigation, environmental protection, coastal traffic monitoring simplifying identification and communication. Additionally, historical AIS data have been used in many other areas of maritime safety, economic and environmental research. The probability of the detection of terrestrial AIS signals from space was presented in 2003, following the advancements in micro satellite technology. Through constant development, research and cooperation between governmental and private sectors, Satellite AIS (S-AIS) has been continuously evolving. Advancements in signal and data processing techniques have resulted in an improved detection over vast areas outside of terrestrial range. Some of the challenges of S-AIS technology include satellite revisit times, message collision and ship detection probability. Data processing latency and lacking the continuous real-time coverage made it less reliable for end user in certain aspects of monitoring and data analysis. Recent developments and improvements by leading S-AIS service providers have reduced latency issues. Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry.

scholarly journals Evaluation of AIS reception in Arctic regions from space by using a stratospheric balloon flight

Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Jesper Abildgaard Larsen ◽  
Jens Dalsgaard Nielsen ◽  
Hans Peter Mortensen ◽  
Ulrik Wilken Rasmussen ◽  
Troels Laursen ◽  
...  
Keyword(s):  
Barents Sea ◽  
The Arctic ◽  
Automatic Identification ◽  
Identification System ◽  
Balloon Flight ◽  
Ship Traffic ◽  
Arctic Regions ◽  
Increased Risk ◽  
Stratospheric Balloon

ABSTRACTDue to the increased melting season in the arctic regions, especially in the seas surrounding Greenland, there has been an increased interest in utilising these waterways, both as an efficient transport route and an attractive leisure destination. However, with heavier traffic comes an increased risk of accidents. Due to the immense size and poor infrastructure of Greenland, it is not feasible to deploy ground based ship monitoring stations throughout the Greenland coastline. Thus the only feasible solution is to perform such surveillance from space. In this paper it is shown how it is possible to receive transmissions from the Automatic Identification System (AIS) from space and the quality of the received AIS signal is analysed. To validate the proposed theory, a field study, utilising a prototype of AAUSAT3, the third satellite from Aalborg University, was performed using a stratospheric balloon flight in the northern part of Sweden and Finland during the autumn of 2009. The analysis finds that, assuming a similar ship distribution as in the Barents Sea, it is feasible to monitor the ship traffic around Greenland from space with a satisfactory result.

Research on Vessel Trajectory Multi-Dimensional Compression Algorithm Based on Douglas-Peucker Theory

2014 ◽  
Vol 694 ◽  
pp. 59-62 ◽  
Author(s):  
Fei Xiang Zhu ◽  
Li Ming Miao ◽  
Wen Liu
Keyword(s):  
Compression Algorithm ◽  
Spatial Distance ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Compression Process ◽  
Maritime Traffic ◽  
Offset Distance ◽  
Improved Algorithm

Currently, maritime safety administrations or shipping company had received a large number of vessel trajectory data from Automatic Identification System (AIS). In order to more efficiently carry out research of maritime traffic flow, ship behavior and maritime investigation, it is important to ensure the quality of the vessel trajectory data under compression condition. In classic Douglas-Peucker vector data compression algorithm, offset spatial distance of each point was the single factor in compression process. In order to overcome the shortcomings of classic Douglas-Peucker, a vessel trajectory multi-dimensional compression improved algorithm is proposed. In improved algorithm, the concept of single trajectory point importance which considers the point offset distance and other vessel handling factors, such as the vessel turning angle, speed variation, is proposed to as the compression index. Compared to classic Douglas-Peucker algorithm, experiment results show that the proposed multi-dimensional vessel trajectory compression improved algorithms can effectively retain characteristics of navigation.

scholarly journals An Enhanced Bandwidth Optimization in Un-reliable Network using Efficient Bandwidth Utilization based Scheduling Algorithm

2018 ◽  
Vol 10 (2) ◽  
pp. 596
Author(s):  
Sivashanmugam N ◽  
Jyoti Venkateshwaran
Keyword(s):  
Real Time ◽  
Scheduling Algorithm ◽  
Network Services ◽  
Quality Of Services ◽  
Traffic Data ◽  
Bandwidth Utilization ◽  
Average Delay ◽  
Data Interchange ◽  
Real Time Application

<span lang="EN-US">Nowadays, bandwidth utilization is a very challenging task for Subscriber Stations (SS) to predict a large amount of data. The existing techniques allow the SS to maintain the occupied bandwidth via risk of failure which does not satisfy the quality of services (QoS) needs.  Another challenge is the resource handling with QoS. In Web technology life, there is only few research focused on tackling the resource handling issues with different techniques. Current methods do not consider the data interchange during route switching.  To offer the best solution of above problems, An Efficient Bandwidth Utilization based Scheduling (EBS) Algorithm is designed to maintain proper bandwidth utilization in a real-time application. The EBS algorithm predicts the amount of bandwidth which should be requested according to backlogged traffic data. It’s also considering the data rate divergence between a packet received and transmissions in a queue to improve the bandwidth. The main objective of proposed design is to permits other complementary station (CS) and SSs to bring out the unutilized bandwidth by the availability of SS transmission. The unutilized bandwidth is not possible to get regularly. The proposed method is more flexible to apply in real time and research-oriented applications. The methods enhance the bandwidth utilization during maintenance of the same QoS guaranteed network services. A proposed method avoids the current bandwidth reservation collapse at the time of the same QoS guaranteed services.  The techniques permit SSs to find out the portion of un-utilized bandwidth accurately. Based on Experimental evaluations, proposed algorithm reduces 21.26 PLR (Packet Loses Ratio), 3.25 AD (Average Delay), and improves 8.65 BU (Bandwidth Utilization) and 51.2% (Throughput) compared than existing methods.</span>

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