Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm

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

Dynamiczna ocena ryzyka dla systemów wspomagania decyzji w sektorze morskim

Transport morski jest jednym z kluczowych elementów globalnego handlu. Obecnie ok. 90% towarów jest przewożonych drogą morską. Wraz ze wzrostem znaczenia światowego handlu morskiego pojawia się potrzeba oceny ryzyka stwarzanego przez statki. W artykule zaprezentowano propozycję podejścia do analizy ryzyka morskiego, opierając się na szacowaniu w czasie rzeczywistym poziomu ryzyka pojedynczego statku, a tym samym oceny bezpieczeństwa morskiego systemu transportowego w krótkim horyzoncie czasowym. Podejście skupia się na dynamicznej ocenie ryzyka, bazując na szeregu czynników i zmiennych odnoszących się do ryzyka. Zaproponowana metoda ma na celu ułatwić porównanie statków z punktu widzenia ryzyka, jakie stwarzają. Może ona być zastosowana w systemach wspomagania decyzji jako klasyfikator statków wymagających szczególnej uwagi. W artykule przedstawiono kilka scenariuszy biznesowych, w których proponowane podejście do analizy ryzyka statku może być stosowane.The oversea shipping is nowadays one of the key elements of the global trade. Currently about 90% of cargo is carried by sea. With the growing importance of the world seaborne trade, the need to assess the risk posed by ships appears. The paper presents an approach to analyze the maritime risk, by estimating in realtime the risk level of an individual ship, and thus assess the security of maritime transportation system in the short-term horizon. The approach is based on a dynamic evaluation of risk, using various risk factors and variables. The aim of the approach is to facilitate the automatic comparison of ships from the point of view of risk they pose. It can be used in decision support systems to classify ships, which require a special attention. The paper presents several business scenarios, where the approach to risk analysis can be applied

Artifcial intelligence-friend or foe in fake news campaigns

In this paper the impact of large language models (LLM) on the fake news phenomenon is analysed. On the one hand decent text‐generaotin capabilietis can be misused for mass fake news production. On the other, LLMs trained on huge volumes of text have already accumulated information on many facts thus one may assume they could be used for fact‐checking. Experiments were designed and conducted to verify how much LLM responses are aligned with actual fact‐checking verdicts. The research methodology consists of an experimental dataset preparation and a protocol for interacting with ChatGPT, currently the most sophisticate