Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal

2013 ◽  
Vol 66 (6) ◽  
pp. 879-898 ◽  
Author(s):  
P.A.M. Silveira ◽  
A.P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Relative Importance ◽  
Traffic Patterns ◽  
Collision Risk ◽  
Expected Number ◽  
Maritime Traffic ◽  
Marine Traffic ◽  
Control Centre ◽  
Ship Collision

This paper studies the risk of ship collision off the coast of Portugal based on Automatic Identification System (AIS) data, which is recorded and maintained by the Portuguese coastal Vessel Traffic Service (VTS) control centre (CCTMC). Computer programs for decoding, visualization and analysis of the AIS data have been developed. From analysis of the AIS data available, maritime traffic off the coast of Portugal is characterized and a statistical analysis of traffic in the Traffic Separation Schemes is provided. An algorithm has been developed to assess the risk profile and the relative importance of routes associated with ports. A method is proposed to calculate the collision risk from the assessment of the number of collision candidates by estimating future distances between ships based on their previous positions, courses and speeds, and comparing those distances with a defined collision diameter. Values of causation probability suggested in several studies are used to calculate the expected number of collisions in the period of time under investigation based on the number of collision candidates. The results of this study are then compared with the number of collisions that have occurred between 1997–2006, registered and maintained by the Portuguese Maritime Authority.

scholarly journals Improving Near Miss Detection in Maritime Traffic in the Northern Baltic Sea from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 180
Author(s):  
Lei Du ◽  
Osiris A. Valdez Banda ◽  
Floris Goerlandt ◽  
Pentti Kujala ◽  
Weibin Zhang
Keyword(s):  
Baltic Sea ◽  
Perceived Risk ◽  
Maritime Transportation ◽  
Near Miss ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Ship Collision ◽  
The Impact ◽  
Ship Behavior

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.

scholarly journals Navigation Safety for Marine Traffic in the Malacca Strait using AIS Data

2019 ◽  
Vol 7 (4) ◽  
Author(s):  
Muhammad Badrus Zaman
Keyword(s):  
Traffic Safety ◽  
Traffic Management ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Traffic ◽  
Marine Traffic ◽  
International Regulations ◽  
Ship Safety ◽  
Malacca Strait ◽  
Navigation Safety

The Malacca Strait experiences high-density vessel traffic, and therefore is a busy area with high potential for collisions. Analyses of marine traffic that reflect the real conditions of ship navigation are performed to enhance maritime traffic safety. An automatic identification system (AIS) allows for the accurate investigation of actual ship encounters, ship collisions, and sea traffic management systems. For this study, an AIS receiver installed at the Universiti Teknologi Malaysia (UTM) provided AIS data, which focused on a selected area in the Malacca Strait. The 1972 International Regulations for Preventing Collisions at Sea (COLREG) guided the assessment of navigation safety based on real conditions using AIS and geographic identification systems (GIS). Based on estimates of the probability and consequence indices from a risk matrix, the time and encounter conditions determined the level of risk. This study also conducted safety measurements. The analysis indicated that ship safety would improve significantly if the vessels followed the guidelines established in this study

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  
Keyword(s):  
Least Squares Method ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Automatic Identification System ◽  
Domain Models ◽  
System Data ◽  
Quantification Model ◽  
Highly Correlated

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.

scholarly journals Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

2021 ◽  
Vol 10 (11) ◽  
pp. 757
Author(s):  
Pin Nie ◽  
Zhenjie Chen ◽  
Nan Xia ◽  
Qiuhao Huang ◽  
Feixue Li
Keyword(s):  
Traffic Management ◽  
Similarity Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Motion Direction ◽  
Maritime Traffic ◽  
A Cell ◽  
Robustness To Noise ◽  
Trajectory Similarity

Automatic Identification System (AIS) data have been widely used in many fields, such as collision detection, navigation, and maritime traffic management. Similarity analysis is an important process for most AIS trajectory analysis topics. However, most traditional AIS trajectory similarity analysis methods calculate the distance between trajectory points, which requires complex and time-consuming calculations, often leading to substantial errors when processing AIS trajectory data characterized by substantial differences in length or uneven trajectory points. Therefore, we propose a cell-based similarity analysis method that combines the weight of the direction and k-neighborhood (WDN-SIM). This method quantifies the similarity between trajectories based on the degree of proximity and differences in motion direction. In terms of its effectiveness and efficiency, WDN-SIM outperformed seven traditional methods for trajectory similarity analysis. Particularly, WDN-SIM has a high robustness to noise and can distinguish the similarities between trajectories under complex situations, such as when there are opposing directions of motion, large differences in length, and uneven point distributions.

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 Ship-Collision Avoidance Decision-Making Learning of Unmanned Surface Vehicles with Automatic Identification System Data Based on Encoder—Decoder Automatic-Response Neural Networks

2020 ◽  
Vol 8 (10) ◽  
pp. 754
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Time Series ◽  
Collision Avoidance ◽  
Time Series Data ◽  
Series Data ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Response ◽  
Ship Collision

Intelligent unmanned surface vehicle (USV) collision avoidance is a complex inference problem based on current navigation status. This requires simultaneous processing of the input sequences and generation of the response sequences. The automatic identification system (AIS) encounter data mainly include the time-series data of two AIS sets, which exhibit a one-to-one mapping relation. Herein, an encoder–decoder automatic-response neural network is designed and implemented based on the sequence-to-sequence (Seq2Seq) structure to simultaneously process the two AIS encounter trajectory sequences. Furthermore, this model is combined with the bidirectional long short-term memory recurrent neural networks (Bi-LSTM RNN) to obtain a network framework for processing the time-series data to obtain ship-collision avoidance decisions based on big data. The encoder–decoder neural networks were trained based on the AIS data obtained in 2018 from Zhoushan Port to achieve ship collision avoidance decision-making learning. The results indicated that the encoder–decoder neural networks can be used to effectively formulate the sequence of the collision avoidance decision of the USV. Thus, this study significantly contributes to the increased efficiency and safety of maritime transportation. The proposed method can potentially be applied to the USV technology and intelligent collision-avoidance systems.

scholarly journals Evaluating Strong Currents at a Fairway in the Finnish Archipelago Sea

2018 ◽  
Vol 6 (4) ◽  
pp. 122 ◽  
Author(s):  
Hedi Kanarik ◽  
Laura Tuomi ◽  
Pekka Alenius ◽  
Mikko Lensu ◽  
Elina Miettunen ◽  
...  
Keyword(s):  
Cross Section ◽  
Acoustic Doppler Current Profiler ◽  
Mean Value ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Traffic ◽  
Current Profiler ◽  
System Data ◽  
The Cross ◽  
Oceanographic Conditions

Safe navigation in complex archipelagos requires knowledge and understanding of oceanographic conditions in the fairways. We have studied oceanographic conditions and their relation to weather in a crossing in the Finnish archipelago, which is known to have events when strong currents affect marine traffic. Our main dataset is ADCP (Acoustic Doppler Current Profiler) current measurements, done in the cross section of five months in 2013. We found that the local currents flow mainly to two directions, either to north-northeast (NNE) or to south-southwest (SSW), which is nearly perpendicular to the deepest fairway in the area. The mean value of the currents in the surface layer was 0.087 ms - 1 , but during the high wind situations, the current speed rose over 0.4 ms - 1 . These strong currents were also shown, according to AIS (Automatic Identification System) data, to cause drift of the vessels passing the cross section, though the effect of wind and current to the ship may sometimes be hard to separate. We studied whether the strong currents could be predicted from routine observations of wind and sea level available in the area, and we found that prediction of these currents is possible to some extent. We also found that winds of over 10 ms - 1 blowing from NW (300 ∘ –350 ∘ ) and SE (135 ∘ –180 ∘ ) generated strong currents of over 0.2 ms - 1 , whereas most commonly measured winds from SW (190 ∘ –275 ∘ ) did not generate currents even with winds as high as 15 ms - 1 .

scholarly journals Distributed Stochastic Search Algorithm for Multi-ship Encounter Situations

2017 ◽  
Vol 70 (4) ◽  
pp. 699-718 ◽  
Author(s):  
Donggyun Kim ◽  
Katsutoshi Hirayama ◽  
Tenda Okimoto
Keyword(s):  
Distributed Algorithms ◽  
Search Algorithm ◽  
Real Data ◽  
Stochastic Search ◽  
Automatic Identification ◽  
Identification System ◽  
Local Search Algorithm ◽  
Ship Collision ◽  
Stochastic Search Algorithm ◽  
Made In

Ship collision avoidance involves helping ships find routes that will best enable them to avoid a collision. When more than two ships encounter each other, the procedure becomes more complex since a slight change in course by one ship might affect the future decisions of the other ships. Two distributed algorithms have been developed in response to this problem: Distributed Local Search Algorithm (DLSA) and Distributed Tabu Search Algorithm (DTSA). Their common drawback is that it takes a relatively large number of messages for the ships to coordinate their actions. This could be fatal, especially in cases of emergency, where quick decisions should be made. In this paper, we introduce Distributed Stochastic Search Algorithm (DSSA), which allows each ship to change her intention in a stochastic manner immediately after receiving all of the intentions from the target ships. We also suggest a new cost function that considers both safety and efficiency in these distributed algorithms. We empirically show that DSSA requires many fewer messages for the benchmarks with four and 12 ships, and works properly for real data from the Automatic Identification System (AIS) in the Strait of Dover.

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