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.

The rail network in the Netherlands is one of the busiest in Europe. To ensure a safe and reliable infrastructure, preventive maintenance is of utmost importance. ProRail, the sole maintainer of the railway infrastructure in the Netherlands, spends hundreds of millions of dollars on maintenance each year. Due to the complexity and busyness of the network, maintenance can cause major disruptions leading to longer travel times. Despite these factors, maintenance is currently scheduled mostly manually, leading to suboptimal schedules being created. A pilot study by Macomi, the company which enabled this study through an internship, showed great potential for improvement in scheduling this maintenance. In this thesis, the aim is to further improve the maintenance schedule of ProRail, as well as to find out what kind of solution method is most suitable to improve that schedule. To achieve this goal, various types of algorithms have been implemented and tested on the problem. An evolution strategy algorithm developed by Macomi was successfully improved. Through the use of multi-objective algorithms, the trade-off between maintenance costs and availability of the infrastructure was analyzed. These multi-objective algorithms were found to provide unsatisfactory solutions. Greedy algorithms were also developed to provide a quicker solution method, and the resulting solutions were of surprisingly high quality. Finally, a hybrid algorithm using the evolution strategy and a greedy algorithm was created. It was shown that this hybrid algorithm outperforms all other algorithms, and provides solutions that are better in terms of costs and constraints compared to the manual schedule of ProRail, and the baseline established by the pilot study of Macomi.