Vessel Trajectory Prediction Using Radial Basis Function Neural Networks

Stogiannos, Marios; Papadimitrakis, Myron; Sarimveis, Haralambos; Alexandridis, Alex

doi:10.1109/eurocon52738.2021.9535562

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Thus, several studies were conducted to investigate research questions in the field of maritime traffic using historical AIS data. Examples of these studies include the detection of abnormal ship behavior [48], prediction of vessel trajectory [32,47], data analysis, such as outlier detection [5] and collision risk analysis [29], and the application of machine learning algorithms [46] to improve the quality of AIS data and enhance the performance of handling maritime processes.…”

Section: Related Workmentioning

confidence: 99%

Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Gouareb

Can²,

Ferdowsi

et al. 2022

Network Science

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As the world's population continues to expand, maritime transport is critical to ensure economic growth. To improve security and safety of maritime transportation, the Automatic Identification System AQ1 (AIS) collects real-time data about vessels and their positions. While a large portion of the AIS data is provided via an automatic tracking system, some key fields, such as destination and draught, are entered manually by the ship navigator and are thus prone to errors. To support decision making in maritime industries, in this paper we propose a datadriven vessel destination prediction algorithm based on heterogeneous graph and machine learning models. We design the task as a multi-class classification problem, where the destination port is the category to be predicted given the vessel and origin information. Then, we use a link prediction model in a weighted heterogeneous graph to predict the vessel destination. Experimental comparison against baseline methods, such as logistic regression and k-nearest neighbors, showed that our model provides a robust performance, outperforming the baseline algorithms by 9% and 33% in terms of accuracy and F1-score, respectively. Thus, heterogeneous graph models provide a powerful alternative to predict port destination, and could support enhancing AIS data quality and better decision making in maritime transportation industries.

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Section: Related Workmentioning

confidence: 99%

Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Gouareb

Can²,

Ferdowsi

et al. 2022

Network Science

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“…After the establishment of the Automatic identification system (AIS), and as moving data volumes and availability increased, transportation intelligence also evolved with increased need for data-driven methods combined with DT technology to optimize moving objects operation. Various ML and data-driven methods based on AIS have been proposed in the past years to address the route (trajectory) forecasting problem in the maritime [24][25] [26][27] [28][29][30] [31]. In addition, some previous works discuss digital twins' implementations for planning and security monitoring of the transport corridors [7][32] [33].…”

Section: Related Workmentioning

confidence: 99%

Data Driven Digital Twins for the Maritime Domain

Troupiotis-Kapeliaris

Zygouras²,

Kaliorakis³

et al. 2022

Progress in Marine Science and Technology

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Digital twins are computational models that replicate the structure, behaviour and overall characteristics of a physical asset in the digital world. In the maritime domain, conventional approaches have relied on mathematical modeling (e.g., linearised equations of motion) and heavy computations for estimating ship resistance and propulsion, seakeeping and maneuverability and overall hull form optimization, treating the vessel as a point body. For instance, the ability to predict a vessel’s future track in confined or congested waters presents a significant challenge due to the fact that as time passes, these models often fall out of sync with their digital counterparts due to changes that happen to the ship (e.g., foulding affecting maneuverability). In addition to this, mostly due to computational resources required, in real world deployments models are simplified, thus reducing their overall prediction accuracy. In our work, we implement AI-enabled coupled abstractions of the asset-twin system, which rely on machine learning methods for constant learning of the evolving over time behavior of a vessel based on historical trip data and information related to vessel’s structure and loading capacity. The evaluation results indicate that the inclusion of vessel and journey specific information is beneficial for the predictions.

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Predicting Co-movement patterns in mobility data

et al. 2022

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Predictive analytics over mobility data is of great importance since it can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example is anticipated location prediction, where the goal is to predict the future location of a moving object, given a look-ahead time. What is even more challenging is to be able to accurately predict collective behavioural patterns of movement, such as co-movement patterns as well as their course over time. In this paper, we address the problem of Online Prediction of Co-movement Patterns. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates the comparison between the predicted clusters and the actual ones. Finally, we calculate the clusters’ evolution through time (survive, split, etc.) and compare the cluster evolution predicted by our framework with the actual one. Our experimental study uses two real-world mobility datasets from the maritime and urban domain, respectively, and demonstrates the effectiveness of the proposed framework.

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Vessel Trajectory Prediction Using Radial Basis Function Neural Networks

Cited by 5 publications

References 19 publications

Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Vessel Destination Prediction Using a Graph-Based Machine Learning Model

Data Driven Digital Twins for the Maritime Domain

Predicting Co-movement patterns in mobility data

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