Identifying Port Calls of Ships by Uncertain Reasoning with Trajectory Data

Considering that ports are key nodes of the maritime transport network, it is of great importance to identify ships’ arrivals and departures. Compared with partial proprietary data from a port authority or shipping company, approaches based on compulsory Automatic Identification System (AIS) data reported by ships can produce transparent datasets covering wider areas, which is necessary for researchers and policy makers. Detecting port calls based on trajectory data is a difficult problem due to the huge uncertainty inherent in information such as ships’ ambiguous statuses and ports’ irregular boundaries. However, we noticed that little attention has been paid to this fundamental problem of shipping network analysis, and considerable noise may have been introduced in previous work on maritime network assessment based on AIS data, which usually modeled each port as a circle with a fixed radius such as 1 or 2 km. In this paper, we propose a method for identifying port calls by uncertain reasoning with trajectory data, which represents each port with an arbitrary shape as a set of geographical grid cells belonging to berths inside this port. Based on this high-spatial-resolution representation, port calls were identified when a ship was in any of these cells. Our method was implemented with around 14 billion AIS messages worldwide over 8 months, and examples of the results are provided.

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Research on Ship Classification Based on Trajectory Features

Journal of Navigation ◽

10.1017/s0373463317000546 ◽

2017 ◽

Vol 71 (1) ◽

pp. 100-116 ◽

Cited By ~ 14

Author(s):

Kai Sheng ◽

Zhong Liu ◽

Dechao Zhou ◽

Ailin He ◽

Chengxu Feng

Keyword(s):

Pattern Mining ◽

Extraction Methods ◽

Behaviour Pattern ◽

Automatic Identification ◽

Identification System ◽

Trajectory Data ◽

Trajectory Features ◽

Ship Classification ◽

Historical Trajectory

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10110757 ◽

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.

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

Journal of Marine Science and Engineering ◽

10.3390/jmse8090682 ◽

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.

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Convolutional Neural Network-Based Gear Type Identification from Automatic Identification System Trajectory Data

Applied Sciences ◽

10.3390/app10114010 ◽

2020 ◽

Vol 10 (11) ◽

pp. 4010 ◽

Cited By ~ 3

Author(s):

Kwang-il Kim ◽

Keon Myung Lee

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Automatic Identification ◽

Identification System ◽

Fishing Gear ◽

Trajectory Data ◽

Data Set ◽

Prediction Module ◽

Fishing Gears ◽

Fully Connected

Marine resources are valuable assets to be protected from illegal, unreported, and unregulated (IUU) fishing and overfishing. IUU and overfishing detections require the identification of fishing gears for the fishing ships in operation. This paper is concerned with automatically identifying fishing gears from AIS (automatic identification system)-based trajectory data of fishing ships. It proposes a deep learning-based fishing gear-type identification method in which the six fishing gear type groups are identified from AIS-based ship movement data and environmental data. The proposed method conducts preprocessing to handle different lengths of messaging intervals, missing messages, and contaminated messages for the trajectory data. For capturing complicated dynamic patterns in trajectories of fishing gear types, a sliding window-based data slicing method is used to generate the training data set. The proposed method uses a CNN (convolutional neural network)-based deep neural network model which consists of the feature extraction module and the prediction module. The feature extraction module contains two CNN submodules followed by a fully connected network. The prediction module is a fully connected network which suggests a putative fishing gear type for the features extracted by the feature extraction module from input trajectory data. The proposed CNN-based model has been trained and tested with a real trajectory data set of 1380 fishing ships collected over a year. A new performance index, DPI (total performance of the day-wise performance index) is proposed to compare the performance of gear type identification techniques. To compare the performance of the proposed model, SVM (support vector machine)-based models have been also developed. In the experiments, the trained CNN-based model showed 0.963 DPI, while the SVM models showed 0.814 DPI on average for the 24-h window. The high value of the DPI index indicates that the trained model is good at identifying the types of fishing gears.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.694.59 ◽

2014 ◽

Vol 694 ◽

pp. 59-62 ◽

Cited By ~ 7

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.

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Massive Automatic Identification System Sensor Trajectory Data-Based Multi-Layer Linkage Network Dynamics of Maritime Transport along 21st-Century Maritime Silk Road

Sensors ◽

10.3390/s19194197 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4197 ◽

Cited By ~ 3

Author(s):

Hongchu Yu ◽

Zhixiang Fang ◽

Feng Lu ◽

Alan T. Murray ◽

Zhiyuan Zhao ◽

...

Keyword(s):

Traffic Flow ◽

21St Century ◽

Network Structure ◽

Network Dynamics ◽

Silk Road ◽

Automatic Identification ◽

Identification System ◽

Trajectory Data ◽

Automatic Identification System ◽

Maritime Silk Road

Automatic Identification System (AIS) data could support ship movement analysis, and maritime network construction and dynamic analysis. This study examines the global maritime network dynamics from multi-layers (bulk, container, and tanker) and multidimensional (e.g., point, link, and network) structure perspectives. A spatial-temporal framework is introduced to construct and analyze the global maritime transportation network dynamics by means of big trajectory data. Transport capacity and stability are exploited to infer spatial-temporal dynamics of system nodes and links. Maritime network structure changes and traffic flow dynamics grouping are then possible to extract. This enables the global maritime network between 2013 and 2016 to be investigated, and the differences between the countries along the 21st-century Maritime Silk Road and other countries, as well as the differences between before and after included by 21st-century Maritime Silk Road to be revealed. Study results indicate that certain countries, such as China, Singapore, Republic of Korea, Australia, and United Arab Emirates, build new corresponding shipping relationships with some ports of countries along the Silk Road and these new linkages carry significant traffic flow. The shipping dynamics exhibit interesting geographical and spatial variations. This study is meaningful to policy formulation, such as cooperation and reorientation among international ports, evaluating the adaptability of a changing traffic flow and navigation environment, and integration of the maritime economy and transportation systems.

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A dynamic adaptive grating algorithm for AIS-based ship trajectory compression

Journal of Navigation ◽

10.1017/s0373463321000692 ◽

2021 ◽

pp. 1-17

Author(s):

Yuanyuan Ji ◽

Le Qi ◽

Robert Balling

Keyword(s):

Data Storage ◽

Adaptive Threshold ◽

Compression Algorithm ◽

Ease Of Use ◽

Automatic Identification ◽

Identification System ◽

Vector Data ◽

Computation Complexity ◽

Trajectory Data ◽

Compression Performance

Abstract Automatic identification system (AIS)-based ship trajectory data are important for analysing maritime activities. As the data accumulate over time, trajectory compression is needed to alleviate the pressure of data storage, migration and usage. The grating algorithm, as a vector data compression algorithm with high compression performance and low computation complexity, has been considered as a very promising approach for ship trajectory compression. This algorithm needs the threshold to be set for each trajectory which limits the applicability over a large number of different trajectories. To solve this problem, a dynamic adaptive threshold grating compression algorithm is developed. In this algorithm, the threshold for each trajectory is dynamically generated using an effective approaching strategy. The developed algorithm is tested with a complex trajectory dataset from the Qiongzhou Strait, China. In comparison with the traditional grating method, our algorithm has improved advantages in the ease of use, the applicability to different trajectories and compression performance, all of which can better support relevant applications, such as ship trajectory data storage and rapid cartographic display.

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Inland Ship Trajectory Restoration by Recurrent Neural Network

Journal of Navigation ◽

10.1017/s0373463319000316 ◽

2019 ◽

Vol 72 (06) ◽

pp. 1359-1377 ◽

Cited By ~ 4

Author(s):

Cheng Zhong ◽

Zhonglian Jiang ◽

Xiumin Chu ◽

Lei Liu

Keyword(s):

Neural Networks ◽

Situational Awareness ◽

Short Term Memory ◽

Linear Method ◽

Automatic Identification ◽

Identification System ◽

Trajectory Data ◽

Proposed Model ◽

Data Restoration ◽

Complicated Geometry

The quality of Automatic Identification System (AIS) data is of fundamental importance for maritime situational awareness and navigation risk assessment. To improve operational efficiency, a deep learning method based on Bi-directional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs) is proposed and applied in AIS trajectory data restoration. Case studies have been conducted in two distinct reaches of the Yangtze River and the capability of the proposed method has been evaluated. Comparisons have been made between the BLSTM-RNNs-based method and the linear method and classic Artificial Neural Networks. Satisfactory results have been obtained by all methods in straight waterways while the BLSTM-RNNs-based method is superior in meandering waterways. Owing to the bi-directional prediction nature of the proposed method, ship trajectory restoration is favourable for complicated geometry and multiple missing points cases. The residual error of the proposed model is computed through Euclidean distance which decreases to an order of 10 m. It is considered that the present study could provide an alternative method for improving AIS data quality, thus ensuring its completeness and reliability.

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Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning

Journal of Marine Science and Engineering ◽

10.3390/jmse9080871 ◽

2021 ◽

Vol 9 (8) ◽

pp. 871

Author(s):

Yongpeng Wang ◽

Daisuke Watanabe ◽

Enna Hirata ◽

Shigeki Toriumi

Keyword(s):

Carbon Dioxide ◽

Real Time ◽

Time Management ◽

Carbon Dioxide Emissions ◽

Fixed Time ◽

Automatic Identification ◽

Identification System ◽

Time Interval ◽

Trajectory Data ◽

Fixed Time Interval

In this study, we propose an effective method using deep learning to strengthen real-time vessel carbon dioxide emission management. We propose a method to predict real-time carbon dioxide emissions of the vessel in three steps: (1) convert the trajectory data of the fixed time interval into a spatial–temporal sequence, (2) apply a long short-term memory (LSTM) model to predict the future trajectory and vessel status data of the vessel, and (3) predict the carbon dioxide emissions. Automatic identification system (AIS) database of a liquefied natural gas (LNG) vessel were selected as the sample and we reconstructed the trajectory data with a fixed time interval using cubic spline interpolation. Applying the interpolated AIS data, the carbon dioxide emissions of the vessel were calculated based on the International Towing Tank Conference (ITTC) recommended procedures. The experimental results are twofold. First, it reveals that vessel emissions are currently underestimated. This study clearly indicates that the actual carbon dioxide emissions are higher than those reported. The finding offers insight into how to accurately measure the emissions of vessels, and hence, better execute a greenhouse gases (GHGs) reduction strategy. Second, the LSTM model has a better trajectory prediction performance than the recurrent neural network (RNN) model. The errors of the trajectory endpoint and carbon dioxide emissions were small, which shows that the LSTM model is suitable for spatial–temporal data prediction with excellent performance. Therefore, this study offers insights to strengthen the real-time management and control of vessel greenhouse gas emissions and handle those in a more efficient way.

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