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 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.

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 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 An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fang Liu ◽  
Wei Bi ◽  
Wei Hao ◽  
Fan Gao ◽  
Jinjun Tang
Keyword(s):  
Large Scale ◽  
Spatial Clustering ◽  
Urban Traffic ◽  
Clustering Methods ◽  
Travel Patterns ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Clustering Method ◽  
Urban Travel ◽  
Fuzzy Clustering Method

Exploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to implement soft constraints in the clustering method and evaluate the effectiveness quantitatively is still a challenge. In this study, we propose an improved trajectory clustering method based on fuzzy density-based spatial clustering of applications with noise (TC-FDBSCAN) to conduct classification on trajectory data. Firstly, we define the trajectory distance which considers the influence of different attributes and determines the corresponding weight coefficients to measure the similarity among trajectories. Secondly, membership degrees and membership functions are designed in the fuzzy clustering method as the extension of the classical DBSCAN method. Finally, trajectory data collected in Shenzhen city, China, are divided into two types (workdays and weekends) and then implemented in the experiment to explore different travel patterns. Moreover, three indices including Silhouette Coefficient, Davies–Bouldin index, and Calinski–Harabasz index are used to evaluate the effectiveness among the proposed method and other traditional clustering methods. The results also demonstrate the advantage of the proposed method.

scholarly journals Risk Assessment Methodology for Vessel Traffic in Ports by Defining the Nautical Port Risk Index

2019 ◽  
Vol 8 (1) ◽  
pp. 10 ◽  
Author(s):  
Xavier Bellsolà Olba ◽  
Winnie Daamen ◽  
Tiedo Vellinga ◽  
Serge P. Hoogendoorn
Keyword(s):  
Risk Assessment ◽  
Traffic Management ◽  
Risk Index ◽  
Analytic Network Process ◽  
Assessment Method ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Assessment Methodology ◽  
Network Process

Ports represent a key element in the maritime transportation chain. Larger vessels and higher traffic volumes in ports might result in higher risks at the navigational level. Thus, the dire need for a comprehensive and efficient risk assessment method for ports is felt. Many methodologies have been proposed so far, but their application to aggregated vessel traffic risks for the overall assessment of ports is not developed yet. Hence, the development of an approach for the appraisal of the vessel traffic risks is still a challenging issue. This research aims to develop an assessment methodology to appraise the potential risk of accident occurrence in port areas at an aggregated level by creating a ‘Nautical Port Risk Index’ (NPRI). After identifying the main nautical risks in ports, the Analytic Network Process (ANP) is used to derive the risk perception (RP) weights for each criterion from data collected through surveys to expert navigators. The consequences related to each nautical risk are identified in consultation with risk experts. By combining the RP values and the consequence of each criterion for a time period, the NPRI is calculated. The risks in the Port of Rotterdam are presented in a case study, and the method has been validated by checking the results with experts in assessing nautical port risks from the Port of Rotterdam Authority. This method can be used to assess any new port design, the performance of different vessel traffic management measures, changes in the fleet composition, or existent ports using the Automatic Identification System (AIS) data.

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 Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

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.

AIS Database Powered by GIS Technology for Maritime Safety and Security

2008 ◽  
Vol 61 (4) ◽  
pp. 655-665 ◽  
Author(s):  
Ziqiang Ou ◽  
Jianjun Zhu
Keyword(s):  
Traffic Management ◽  
Surveillance Program ◽  
Automatic Identification ◽  
Identification System ◽  
Gis Technology ◽  
Aerial Surveillance ◽  
Maritime Traffic ◽  
Land Control ◽  
Maritime Domain Awareness ◽  
Fundamental Requirement

The Automatic Identification System (AIS) is an efficient tool to exchange positioning data among participating naval units and land control centres. It was developed primarily as an advanced tool for assistance to sailors during navigation and for the safety of the life at sea. Maritime security has become a major concern for all coastal nations, especially after September 11, 2001. The fundamental requirement is maritime domain awareness via identification, tracking and monitoring of vessels within their waters and this is exactly what an AIS could bring. This paper will be focused on how the AIS-derived information could be used for coastal security, maritime traffic management, vessel tracking and monitoring with the help of GIS technology. The AIS data used in this paper was collected by the Canadian national aerial surveillance program.

scholarly journals Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8133
Author(s):  
Clara I. Valero ◽  
Enrique Ivancos Pla ◽  
Rafael Vaño ◽  
Eduardo Garro ◽  
Fernando Boronat ◽  
...  
Keyword(s):  
Traffic Management ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Legacy System ◽  
Cognitive Component ◽  
Cognitive Services ◽  
Oceanographic Data ◽  
Reducing Costs ◽  
Easy Integration

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

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