Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity

Zhang, Shu kai; Shi, Guo you; Liu, Zheng Jiang; Zhao, Zhongguo; Zhao, Wei

doi:10.1016/j.oceaneng.2018.02.060

Cited by 103 publications

(44 citation statements)

References 12 publications

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“…Deng introduced a Markov based model to explore ship movement patterns, which were further used to identify the abnormal AIS data samples [18]. Zhang et al proposed a hierarchical density-based spatial clustering of applications with noise based model to cluster and denoise the raw AIS trajectories [19]. Rong et al cleansed the raw AIS data in the lateral and longitudinal dimensions with a novel probability trajectory prediction model [20].…”

Section: Introductionmentioning

confidence: 99%

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Chen

Ling

Yang

et al. 2020

Mathematical Problems in Engineering

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Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

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Section: Introductionmentioning

confidence: 99%

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Chen

Ling

Yang

et al. 2020

Mathematical Problems in Engineering

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“…In [24], Li et al, present a two-step method to achieve a balance between computational time and performance; first performing data simplification by applying the Douglas-Peucker (DP) algorithm before processing the simplified trajectories with Kernel Density Estimation. Similarly in [27] a DBSCAN is used for clustering purposes, before Ant Colony algorithm is used to find the optimal path from the starting turning node to the ending turning node. In [13], a big data analytical approach that analyses ship traffic demand and the spatiotemporal dynamics of ship traffic in Singapore's port waters using big AIS data is described.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Spatial Method for Modeling Maritime Routes

et al. 2020

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In this work we propose a novel spatial knowledge discovery pipeline capable of automatically unravelling the ''roads of the sea'' and maritime traffic patterns by analysing voluminous vessel tracking data, as collected through the Automatic Identification System (AIS). We present a computationally efficient and highly accurate solution, based on a MapReduce approach and unsupervised learning methods, capable of identifying the spatiotemporal dynamics of ship routes and most crucially their characteristics, thus deriving maritime ''patterns of life'' at a global scale, without the reliance on any additional information sources or a priori expert knowledge. Experimental results confirm high accuracy of results and superior performance in comparison to other methods, with the entire processing duration completing in less than 3 hours for more than a terabyte of non-uniform spatial data. Finally, to clearly demonstrate the applicability and impact of our proposed method, we evaluate its ability to detect real world ''anomalies'', such as maritime incidents reported in the European Marine Casualty Information Platform. Numerical results show the advantages of our scheme in terms of accuracy, with an achieved anomaly detection accuracy of higher than 93%, by detecting 313 out of 335 relevant maritime incidents. INDEX TERMS AIS, anomaly detection, data driven maritime traffic, patterns of life, routes.

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“…Given historical AIS data, the presented method aims at constructing a network reflecting vessel traffic. This approach is also a response to the lack of methods that discover the critical maritime waypoints in an efficient manner, based on the analysis of big amounts of historical data, thus aiming to advance practice of maritime voyage planning that is typically done manually by a ship's navigation officers (Zhang et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

et al. 2020

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The presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The genetic algorithm with spatial partitioning is used for waypoints discovery. Finally, edges connecting these waypoints form the final maritime traffic network. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The method is evaluated by comparing the results with an on-line voyage planning application. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network.

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Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity

Cited by 103 publications

References 12 publications

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

A Distributed Spatial Method for Modeling Maritime Routes

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

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