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J.L.K. van Loon
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2 records found
1
Short-term vessel trajectory and manoeuvre prediction
Predicting vessel trajectories including swing manoeuvres in a scoped area of the Port of Antwerp-Bruges
Situational awareness within port areas is crucial to avoid collisions, navigate efficiently and reduce congestion. Maritime-traffic controllers constantly monitor the situation in the port and intervene when needed. This study proposes a deep learning model that predicts future vessel positions to assist in this process. The model employs a target conditioned trajectory prediction component composed of two recurrent neural networks arranged in an encoder-decoder structure that utilizes historical data points to forecast future trajectories. The model considers multiple factors, including vessel speed, location, length, depth, draught, and the tide. Additionally, this study addresses the prediction of swing manoeuvres, which are special U-turn-like manoeuvres executed during terminal arrival or departure. These manoeuvres can block a significant portion of the waterway and, as such, are essential to consider when gaining a complete understanding of future situations within the port. An integration of both models is applied to a use case study in a scoped area of the Port of Antwerp-Bruges. The models were
trained using AIS and VTS data collected at 30-second intervals. Swing manoeuvres are predicted with an accuracy of 84%, the locations of these manoeuvres are predicted with an average deviation of 212 meter and the duration error is 1.6 minutes on average. The complete predicted trajectories, including potential swing manoeuvres, have an average displacement error and final displacement error of 147 and 117 meter on average, respectively. Overall, the study demonstrates the potential of deep learning models for improving situational awareness within port areas and assisting traffic controllers in making
informed decisions. ...
trained using AIS and VTS data collected at 30-second intervals. Swing manoeuvres are predicted with an accuracy of 84%, the locations of these manoeuvres are predicted with an average deviation of 212 meter and the duration error is 1.6 minutes on average. The complete predicted trajectories, including potential swing manoeuvres, have an average displacement error and final displacement error of 147 and 117 meter on average, respectively. Overall, the study demonstrates the potential of deep learning models for improving situational awareness within port areas and assisting traffic controllers in making
informed decisions. ...
Situational awareness within port areas is crucial to avoid collisions, navigate efficiently and reduce congestion. Maritime-traffic controllers constantly monitor the situation in the port and intervene when needed. This study proposes a deep learning model that predicts future vessel positions to assist in this process. The model employs a target conditioned trajectory prediction component composed of two recurrent neural networks arranged in an encoder-decoder structure that utilizes historical data points to forecast future trajectories. The model considers multiple factors, including vessel speed, location, length, depth, draught, and the tide. Additionally, this study addresses the prediction of swing manoeuvres, which are special U-turn-like manoeuvres executed during terminal arrival or departure. These manoeuvres can block a significant portion of the waterway and, as such, are essential to consider when gaining a complete understanding of future situations within the port. An integration of both models is applied to a use case study in a scoped area of the Port of Antwerp-Bruges. The models were
trained using AIS and VTS data collected at 30-second intervals. Swing manoeuvres are predicted with an accuracy of 84%, the locations of these manoeuvres are predicted with an average deviation of 212 meter and the duration error is 1.6 minutes on average. The complete predicted trajectories, including potential swing manoeuvres, have an average displacement error and final displacement error of 147 and 117 meter on average, respectively. Overall, the study demonstrates the potential of deep learning models for improving situational awareness within port areas and assisting traffic controllers in making
informed decisions.
trained using AIS and VTS data collected at 30-second intervals. Swing manoeuvres are predicted with an accuracy of 84%, the locations of these manoeuvres are predicted with an average deviation of 212 meter and the duration error is 1.6 minutes on average. The complete predicted trajectories, including potential swing manoeuvres, have an average displacement error and final displacement error of 147 and 117 meter on average, respectively. Overall, the study demonstrates the potential of deep learning models for improving situational awareness within port areas and assisting traffic controllers in making
informed decisions.
This paper introduces an electricity price extension to the intention-aware routing system (IARS) for electric vehicles (EV). The existing intention-aware routing system is used to route electric vehicles who require to charge en-route through a road network. To achieve the objective of minimising the average journey time, the intentions of EVs and waiting times at charging stations are com- municated. Instead of only minimizing travel time, the model extension presented in this paper makes it possible to express a decision trade-off between price and time. A vehicle computes its routing policy such that the combined utility of price and time is as high as possible. In this paper the performance of IARS with a price extension is compared to a greedy maximising algorithm (MAX) in several settings. The increase in utility by using IARS depends on the population of electric vehicles. However, in most experiments conducted in this research IARS achieves a significantly higher average utility.
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This paper introduces an electricity price extension to the intention-aware routing system (IARS) for electric vehicles (EV). The existing intention-aware routing system is used to route electric vehicles who require to charge en-route through a road network. To achieve the objective of minimising the average journey time, the intentions of EVs and waiting times at charging stations are com- municated. Instead of only minimizing travel time, the model extension presented in this paper makes it possible to express a decision trade-off between price and time. A vehicle computes its routing policy such that the combined utility of price and time is as high as possible. In this paper the performance of IARS with a price extension is compared to a greedy maximising algorithm (MAX) in several settings. The increase in utility by using IARS depends on the population of electric vehicles. However, in most experiments conducted in this research IARS achieves a significantly higher average utility.