scholarly journals A Novel Framework of Real-Time Regional Collision Risk Prediction Based on the RNN Approach

2020 ◽  
Vol 8 (3) ◽  
pp. 224 ◽  
Author(s):  
Dapei Liu ◽  
Xin Wang ◽  
Yao Cai ◽  
Zihao Liu ◽  
Zheng-Jiang Liu
Keyword(s):  
Real Time ◽  
Shapley Value ◽  
Spatial Clustering ◽  
Risk Identification ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Short Time ◽  
Sea Area

Regional collision risk identification and prediction is important for traffic surveillance in maritime transportation. This study proposes a framework of real-time prediction for regional collision risk by combining Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique, Shapley value method and Recurrent Neural Network (RNN). Firstly, the DBSCAN technique is applied to cluster vessels in specific sea area. Then the regional collision risk is quantified by calculating the contribution of each vessel and each cluster with Shapley value method. Afterwards, the optimized RNN method is employed to predict the regional collision risk of specific seas in short time. As a result, the framework is able to determine and forecast the regional collision risk precisely. At last, a case study is carried out with actual Automatic Identification System (AIS) data, the results show that the proposed framework is an effective tool for regional collision risk identification and prediction.

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 Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

2021 ◽  
Vol 9 (6) ◽  
pp. 566
Author(s):  
Lianhui Wang ◽  
Pengfei Chen ◽  
Linying Chen ◽  
Junmin Mou
Keyword(s):  
Traffic Management ◽  
Performance Metrics ◽  
Spatial Clustering ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Clustering Methods ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Shape Characteristics

The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability. On this basis, a re-clustering method is proposed and comprehensive clustering performance metrics are introduced to optimize the clustering results. The AIS data of the estuary waters of the Yangtze River in China has been utilized to conduct a case study and compare the results with three popular clustering methods. Experimental results prove that this method has good clustering results on ship trajectories in complex waters.

scholarly journals Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN

2021 ◽  
Vol 13 (15) ◽  
pp. 8162
Author(s):  
Xuyang Han ◽  
Costas Armenakis ◽  
Mojgan Jadidi
Keyword(s):  
Situational Awareness ◽  
Spatial Clustering ◽  
Route Planning ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Transportation ◽  
Dbscan Clustering ◽  
Clustering Model ◽  
Data Driven Approach

Today, maritime transportation represents a substantial portion of international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this paper, we present an enhanced density-based spatial clustering of applications with noise (DBSCAN) method to model vessel behaviours based on trajectory point data. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis distance metric, which considers the correlation between the points representing vessel locations. This research proposes applying the clustering method to historical Automatic Identification System (AIS) data using an algorithm to generate a clustering model of the vessels’ trajectories and a model for detecting vessel trajectory anomalies, such as unexpected stops, deviations from regulated routes, or inconsistent speed. Further, an automatic and data-driven approach is proposed to select the initial parameters for the enhanced DBSCAN approach. Results are presented from two case studies using an openly available Gulf of Mexico AIS dataset as well as a Saint Lawrence Seaway and Great Lakes AIS licensed dataset acquired from ORBCOMM (a maritime AIS data provider). These research findings demonstrate the applicability and scalability of the proposed method for modeling more water regions, contributing to situational awareness, vessel collision prevention, safe navigation, route planning, and detection of vessel behaviour anomalies for auto-vessel development towards the sustainability of marine transportation.

scholarly journals AIS-Based Multiple Vessel Collision and Grounding Risk Identification based on Adaptive Safety Domain

2019 ◽  
Vol 8 (1) ◽  
pp. 5 ◽  
Author(s):  
Azzeddine Bakdi ◽  
Ingrid Kristine Glad ◽  
Erik Vanem ◽  
Øystein Engelhardtsen
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Risk Identification ◽  
Automatic Identification ◽  
Identification System ◽  
Large Area ◽  
Continuous Growth ◽  
Country Level ◽  
Spatial Risk

The continuous growth in maritime traffic and recent developments towards autonomous navigation have directed increasing attention to navigational safety in which new tools are required to identify real-time risk and complex navigation situations. These tools are of paramount importance to avoid potentially disastrous consequences of accidents and promote safe navigation at sea. In this study, an adaptive ship-safety-domain is proposed with spatial risk functions to identify both collision and grounding risk based on motion and maneuverability conditions for all vessels. The algorithm is designed and validated through extensive amounts of Automatic Identification System (AIS) data for decision support over a large area, while the integration of the algorithm with other navigational systems will increase effectiveness and ensure reliability. Since a successful evacuation of a potential vessel-to-vessel collision, or a vessel grounding situation, is highly dependent on the nearby maneuvering limitations and other possible accident situations, multi-vessel collision and grounding risk is considered in this work to identify real-time risk. The presented algorithm utilizes and exploits dynamic AIS information, vessel registry and high-resolution maps and it is robust to inaccuracies of position, course and speed over ground records. The computation-efficient algorithm allows for real-time situation risk identification at a large-scale monitored map up to country level and up to several years of operation with a very high accuracy.

scholarly journals DBSCAN OPTIMIZATION FOR IMPROVING MARINE TRAJECTORY CLUSTERING AND ANOMALY DETECTION

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 455-461
Author(s):  
X. Han ◽  
C. Armenakis ◽  
M. Jadidi
Keyword(s):  
Human Error ◽  
Spatial Clustering ◽  
Autonomous Systems ◽  
Computational Cost ◽  
Route Planning ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Clustering ◽  
Clustering Model

Abstract. Today maritime transportation represents 90% of international trade volume and there are more than 50,000 vessels sailing the ocean every day. Therefore, reducing maritime transportation security risks by systematically modelling and surveillance should be of high priority in the maritime domain. By statistics, majority of maritime accidents are caused by human error due to fatigue or misjudgment. Auto-vessels equipped with autonomous and semi-autonomous systems can reduce the reliance on human’s intervention, thus make maritime navigation safer. This paper presents a clustering method for route planning and trajectory anomalies detection, which are the essential part of auto-vessel system design and development. In this paper, we present the development of an enhanced density-based spatial clustering (DBSCAN) method that can be applied on historical or real-time Automatic Identification System (AIS) data, so that vessel routes can be modelled, and the trajectories’ anomalies can be detected. The proposed methodology is based on developing an optimized trajectory clustering approach in two stages. Firstly, to increase the attribute dimension of the vessel’s positioning data, therefore other characteristics such as velocity and direction are considered in the clustering process along with geospatial information. Secondly, the DBSCAN clustering model has been enhanced by introducing the Mahalanobis Distance metric considering the correlations of the position cluster points aiming to make the identification process more accurate as well as reducing the computational cost.

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.

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 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.

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