Using Automatic Identification System Data in Vessel Route Prediction and Seaport Operations

In this paper, the authors perform a comprehensive literature review on the use of data obtained from the Automatic Identification System, with an emphasis on vessel route prediction and seaport operations. The usage of Automatic Identification System vessel’s position data in the vessel route prediction and seaport operations has been analyzed, to prove that Automatic Identification System data has a large potential to improve the efficiency of maritime transport. The authors concluded that proper vessel route prediction and route planning can improve voyage safety and reduce unnecessary costs. Furthermore, AIS can provide port authorities with early warnings, allowing them to take preemptive action to avoid possible congestions and unnecessary costs

Workflow to detect ship encounters at sea with GIS support

Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceAccording to the United Nations, more than 80% of the global trade is currently transported by sea. The Portuguese EEZ has a very extensive area with high maritime traffic, among which illicit activities may occur. This work aims to contribute to the official control of illegal transshipment actions by studying and proposing a new way of detecting encounters between ships. Ships with specific characteristics use an Automatic Identification System (AIS) on board which transmits a signal via radio frequencies, allowing shore stations to receive static and dynamic data from the ship. Thus, there is an increase in maritime situational awareness and, consequently, in the safety of navigation. The methodology of this dissertation employs monthly and daily AIS data in the study area, which is located in southern mainland Portugal. A bibliometric and content analysis was performed in order to assess the state of the art concerning geospatial analysis models of maritime traffic, based on AIS data, and focus on anomalous behaviour detection. Maritime traffic density maps were created with the support of a GIS (QGIS software), which allowed to characterize the maritime traffic in the study area and, subsequently, to pattern the locations where ship encounters occur. The algorithm to detect ship-to-ship meetings at sea was developed using a rule-based methodology. After analysis and discussion of results, it was found that the areas where the possibility of ship encounters at sea is greatest are away from the main shipping lanes, but close to areas with fishing vessels. The study findings and workflow are useful for decision making by the competent authorities for patrolling the maritime areas, focusing on the detection of illegal transhipment actions.Segundo as Nações Unidas, mais de 80% do comércio global é, atualmente, transportado por via marítima. A ZEE portuguesa tem uma área muito extensa, com tráfego marítimo elevado, entre o qual podem ocorrer atividades ilícitas. Este trabalho pretende contribuir para o controlo oficial de ações de transbordo ilegal, estudando e propondo uma nova forma de deteção de encontros entre navios. Os navios com determinadas características, utilizam a bordo um Automatic Identification System (AIS) que transmite sinal através de frequências rádio, permitindo que estações em terra recebam dados estáticos e dinâmicos do navio. Deste modo, verifica-se um aumento do conhecimento situacional marítimo e, consequentemente, da segurança da navegação. Foi realizada uma análise bibliométrica e de conteúdo a fim de avaliar o estado da arte referente a modelos de análise geoespacial do tráfego marítimo, com base em dados AIS, e foco na deteção de comportamentos anómalos. Na metodologia desta dissertação, são utilizados dados AIS mensais e diários na área de estudo, situada a sul de Portugal Continental. Foram criados mapas de densidade de tráfego marítimo com o apoio de um SIG (software QGIS), o que permitiu caracterizar o tráfego marítimo na área de estudo e, posteriormente, padronizar os locais onde ocorrem encontros entre navios. O algoritmo para detetar encontros entre navios no mar foi desenvolvido através de uma metodologia baseada em regras. Após análise e discussão de resultados, constatou-se que as áreas onde a possibilidade de ocorrer encontros de navios no mar é maior, encontram-se afastadas dos corredores principais de navegação, mas próximas de zonas com embarcações de pesca. Os resultados do estudo e o workflow desenvolvidos são úteis à tomada de decisão pelas autoridades competentes por patrulhar as áreas marítimas, com incidência na deteção de ações de transbordo ilegal

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.Peer reviewe

A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off

The effect of nonconformities encountered in the use of technology on the occurrence of collision, contact and grounding accidents

Technology and its innovative applications make life easier and reduce the workload on seafarers. Today's ship bridges have much more modern and integrated navigation systems than before, and the ship's handling and management have become much easier. However, nonconformities encountered in the use of technological devices may cause accidents. In this study, the effect of human factor related errors associated with the use of the bridge's electronic navigational devices on grounding and collision-contact accidents was investigated. Non-conformities obtained from 175 collision-contact and 115 grounding accident reports were qualitatively analysed by means of human factor analysis and a classification system. Afterwards, relationships between nonconformities and their probabilities were evaluated quantitatively via a Bayesian network method. As a result of the study, the accident network was revealed. This accident network summarizes how operating errors in the use of technological equipment cause accidents. Recommendations on the prevention of accidents caused by operating errors associated with the use of new technologies are finally given

Operationalizing COLREGs in SMART ship navigation

The maritime industry is undergoing a transformation driven by digitalization and connectivity. The technological realization of Maritime Autonomous Surface Ships (MASS) presents significant challenges for the maritime human factors research community. These challenges relate to system design, human-automation interaction, stakeholder training, use and acceptance of new technology systems, and on a larger scale, how the regulatory framework, including the Collision Regulations (COLREGs) will be impacted within a MASS system. Decision support is the next step in the transformation towards more connected ships, however, such systems for navigation are largely unexplored from the users’ perspective.The decision support system studied in this project was developed by W\ue4rtsil\ue4 and is called Advanced Intelligent Manoeuvring (AIM), aligning with “low-level automation” or Level 1 (out of a 4-level progression) of MASS. AIM can generate suggestions for course or speed alterations based on data from surrounding traffic. A full-mission bridge simulator study was conducted at Chalmers University of Technology in Gothenburg, Sweden with nineteen Swedish navigators. Three traffic scenarios each with three ships were completed in both baseline (no AIM) and AIM conditions. A mixed methods data collection and analysis approach was employed using questionnaires, collective interviews, and an evaluation of the ship tracks. The results show that the navigators perceive AIM as an advisory tool, to visualize how traffic situations could unfold, an outcome currently difficult for most navigators to conceive. This report discusses the present and near future of the maritime sociotechnical system, highlighting the benefits of automation, while remaining vigilant about the potential dangers