With the rapid development of the global economy, transport safety and security have become the key issues in maritime transportation all over the world. In practical applications, the Automatic Identification System (AIS)-based measurement of similarities between different vessel trajectories plays an important role in improving maritime transportation, e.g., maritime navigation, maritime supervision and management. However, the received AIS datasets are usually composed of a large amount of redundant information which could significantly increase the computational complexity. To deal with this problem, a Douglas-Peucker (DP)-based calculation method is introduced in this paper to accurately compress the spatio-temporal AIS trajectories while preserving the main geometrical structures. Based on the compressed trajectories, it is able to accelerate the Dynamic Time Warping (DTW) algorithm for the measurement of similarities between different vessel trajectories. In particular, the combination of DP and DTW has the capacity of significantly reducing the computational cost and guaranteeing the accuracy of similarity measures. The experimental results have demonstrated the superior performance of the proposed method in terms of computational cost and accuracy of similarity measures.

Recent approaches and opinions support that shipping companies must take precautionary measures against financial risks and design steadier steps for financial management. The key to developing, implementing, and managing a successful hedging strategy is to use effective forecasting systems and appropriate financial derivative products. The key objective of this study is to control risks from bunker cost fluctuations using financial derivative products. To do so, a time-series analysis is conducted using a dataset derived from a bunker index system. The artificial neural network method is used for time-series analysis with a mean absolute percentage error of 0.9182105. Next, progress predictions of bunker costs and hedging strategies are determined to use financial derivative products against their risks. Finally, this study concludes that forward agreements can serve as the perfect protection mechanism against bunker risks in tramp shipping.

The punctuality of containerships has become the prevalent issue in container liner shipping operations, as the on-time arrival of a containership at the next port of call is paramount. When a delay occurs at the previous port of call, it may also cause a delay at the next port of call. This paper proposes a departure punctuality model of analysis. This model employs a Fuzzy Rule-Based Bayesian Belief Network (FRBBN) for predicting the departure punctuality of a containership. To ensure the reliability of the model, two containerships were tested. The results show that the prediction values from the model are between 95.6% and 99% accurate provided that no tactical strategy is implemented during the voyage. In addition, the most significant factors that determine the punctuality of departure were found to be punctuality of arrival at the same (base) port prior to departure, dangerous events and other unexpected delays during the port stay. It is expected that this model is capable of helping researchers and practitioners to understand the influence of the dynamic environment and to make predictions on the departure punctuality of containerships.

The estimation of key indicators in a firm’s cost structure, such as marginal costs, economies of scale and elasticity of substitution are essential to defining the optimal pricing policy, investment and the regulation of ports. However, only a few studies have focused on the estimation of such indicators due to the unavailability of cost data on seaports. In addition, other port cost studies focused on all port activities. The output is measured as a physical product rather than as a service product. The main purpose of this paper is then to concentrate on one service, cargo handling in the port of Saint-Brieuc, and to examine the real composition of its costs. The estimation of a translog cost function suggested that the port may have an over employment of administrative staff in the handling service and a limited capacity when it comes to efficiency in the long run. The results also indicated increasing returns to scale that may suggest that the application of pricing at a marginal cost might not aid the total recovery of the outlay. These results are useful from an operational as well as an investment policy perspective.

Recently, the migration of refugees has been drastically increasing and became a global issue which indirectly affects the shipmasters, authorities and shipowners during rescue operations. The objective of this paper is to study the challenges or difficulties in rescuing refugees at sea from the perspectives of shipmasters, authorities and shipowners in Malaysia. In this study, the key challenges faced by these three groups of individuals are collected and classified based on the primary data reviewed. The interview sessions were conducted with selected expertise and the data collected are analyzed based on the Analytical Hierarchy Process (AHP) or precisely the pair-wise comparison methods. Through the calculations, the challenges are ranked from the most important challenges to the least important challenges. Furthermore, the data is analyzed by using expert judgments focusing on the relatively most important challenges faced by the three groups of individuals. Finally, the most important challenge faced by shipmasters is the safety of ships and crews, while for authorities, the most important challenge faced is delays in the initiation of rescue operations. For ship-owners, the most important challenge faced is time sensitive cargo loss. Ultimately, two suggestions have been proposed to encounter the most important challenge faced by these groups.

For the German energy transition, offshore wind energy is a significant factor of success. The number of installed offshore wind energy turbines is steadily increasing. Currently, the subject matter of offshore wind energy turbine maintenance and its optimization is increasingly in the focus of research and development work. The present contribution examines the logistics of offshore wind energy turbine maintenance and the impact of the actual sea state and of sea state forecasts. To this end, the coordination processes between the players involved in the planning of service operations will be presented and analysed. Based on this, the impact of the quality of wave height forecasts on operation decisions and the mean time to repair will be determined as well as the availability of the turbines at different forecast quality levels. Proceeding on the basis of these results, the concept of a decision-making support system for operative logistics planning will be presented.

The use of radar-based systems for vessel monitoring is not suitable in populated areas, due to the high electromagnetic emissions. In this paper, a camera based vessel recognition system for application in the context of Vessel Traffic Services (VTS) and Homeland Protection (HP) is proposed. Our approach is designed to extend the functionality of traditional VTS systems by permitting the classification of both cooperative and non-cooperative targets, using camera images only. This allows enhancing the surveillance function in populated areas, where public opinion is strongly concerned about electromagnetic emissions and therefore antennas are suspiciously observed and radars are not allowed. Experiments have been carried out on a publicly available data set of images coming from the ARGOS boat traffic monitoring system in the City of Venice (Italy). The obtained classification accuracy of 89.6% (with 11 different classes of boats) demonstrates the effectiveness of the proposed approach.