An Automatic Identification System (AIS) Database for Maritime Trajectory Prediction and Data Mining

Mao, Shangbo; Tu, Enmei; Zhang, Guanghao; Rachmawati, Lily; Rajabally, Eshan; Huang, Guang-Bin

doi:10.1007/978-3-319-57421-9_20

Cited by 79 publications

(50 citation statements)

References 8 publications

(9 reference statements)

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“…This problem can be tackled by a number of methods. Linear interpolation is one example -it was used, e.g., by Mao et al (2018). Lz et al (2015) presented a more sophisticated data interpolation method, which uses line, arc, and curve trajectories and is dedicated to inland AIS data.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

et al. 2020

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“…Mao [ 14 ] proposed an ELM-based marine trajectory prediction method to predict the trajectory of ships. Extreme Learning Machine (ELM) is a machine learning algorithm based on a single hidden layer feedforward neural network.…”

Section: Related Workmentioning

confidence: 99%

A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

Suo

Chen

Claramunt

et al. 2020

Sensors

103

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Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework and a Gate Recurrent Unit (GRU) model to predict vessel trajectories. First, series of trajectories are extracted from Automatic Identification System (AIS) ship data (i.e., longitude, latitude, speed, and course). Secondly, main trajectories are derived by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Next, a trajectory information correction algorithm is applied based on a symmetric segmented-path distance to eliminate the influence of a large number of redundant data and to optimize incoming trajectories. A recurrent neural network is applied to predict real-time ship trajectories and is successively trained. Ground truth data from AIS raw data in the port of Zhangzhou, China were used to train and verify the validity of the proposed model. Further comparison was made with the Long Short-Term Memory (LSTM) network. The experiments showed that the ship’s trajectory prediction method can improve computational time efficiency even though the prediction accuracy is similar to that of LSTM.

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“…That importance has led to g reat improvements in this domain over time, creating more customized solutions, according to the research problem we are trying to solve in each case, the tools we are using and the features we decided to work with. Mao et al, in [7], present us with a nice example of advanced preprocessing. They created a measure of route complexity that is based on the cosine between the geo-location points that comprise the trajectory.…”

Section: Related Work 31 Trajectory Predictionmentioning

confidence: 99%

Context agnostic trajectory prediction based on λ-architecture

Psomakelis

Tserpes

Zissis

et al. 2020

Future Generation Computer Systems

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Predicting the next position of movable objects has been a problem for at least the last three decades, referred to as 'trajectory prediction'. In our days, the vast amounts of data being continuously produced add the big data dimension to the trajectory prediction problem, which we are trying to tackle by creating a λ-Architecture based analytics platform. This platform performs both batch and stream analytics tasks and then combines them to perform analytical tasks that cannot be performed by analyzing any of these layers by itself. The biggest benefit of this platform is its context agnostic trait, which allows us to use it for any use case, as long as a time-stamped geo-location stream is provided. The experimental results presented prove that each part of the λ-Architecture performs well at certain targets, making a combination of these parts a necessity in order to improve the overall accuracy and performance of the platform.

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An Automatic Identification System (AIS) Database for Maritime Trajectory Prediction and Data Mining

Cited by 79 publications

References 8 publications

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

Context agnostic trajectory prediction based on λ-architecture

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