Maritime traffic risk is increasing rapidly with the growth of marine traffic volume and construction of marine facilities, water bridges, port development, marine wind farm, etc. Given this emerging trend, this paper presents a bibliometric analysis and mapping of the broad academic literature related to maritime traffic safety, focusing on the influences of international collaborations and knowledge sources on the developments of this research domain. To identify trends, patterns and the knowledge distribution of the research on maritime traffic safety, the visualisation of similarities (VOS) viewer software, the bibliometric analysis, and scientometric mapping of the literature have been performed from the perspectives of publication and citation distribution over time, leading authors, countries (regions), institutions, the corresponding collaboration networks, most cited publications and references, focused research fields and topics, research trend evolution over time, etc. The paper provides a comprehensive and quantitative overview and significant picture representation of the domain's leading and evolutionary trends by employing specific aforementioned bibliometric analysis factors. In addition, by reviewing the evolutionary trends of the journals and the proposed investigated factors, such as the influential works, main research topics, and the research frontiers, this paper reveals the scientific literature's main research objectives and directions that could be addressed and explored in future studies.

Collisions are one of the major accidents in the shipping industry, causing significant losses. In this work, a framework of marine collision risk identification strategy was developed to quantitatively analyse collision risks and provide an easy and convenient way to monitor traffic flow in relevant waters to mitigate the chances of collision. The model was verified by using automatic identification system data obtained from Tianjin Port. When compared to previous research, the proposed model can identify risks earlier and give people more time to analyse and take action. The results indicate that it also can provide a visual display to alert relevant personnel. The model can be used as a reference to identify potential collision risks or as an information source for future research.

Maritime video surveillance has become an essential part of the vessel traffic services system, intended to guarantee vessel traffic safety and security in maritime applications. To make maritime surveillance more feasible and practicable, many intelligent vision-empowered technologies have been developed to automatically detect moving vessels from maritime visual sensing data (i.e., maritime surveillance videos). However, when visual data is collected in a low-visibility environment, the essential optical information is often hidden in the dark, potentially resulting in decreased accuracy of vessel detection. To guarantee reliable vessel detection under low-visibility conditions, the paper proposes a low-visibility enhancement network (termed LVENet) based on Retinex theory to enhance imaging quality in maritime video surveillance. LVENet is a lightweight deep neural network incorporating a depthwise separable convolution. The synthetically-degraded image generation and hybrid loss function are further presented to enhance the robustness and generalisation capacities of LVENet. Both full-reference and no-reference evaluation experiments demonstrate that LVENet could yield comparable or even better visual qualities than other state-of-the-art methods. In addition, it takes LVENet just 0⋅0045 s to restore degraded images with size 1920 × 1080 pixels on an NVIDIA 2080Ti GPU, which can adequately meet real-time requirements. Using LVENet, vessel detection performance can be greatly improved with enhanced visibility under low-light imaging conditions.

What are the major factors contributing to ship accidents, and how do these factors evolve in the long term? This study addresses these two questions by leveraging an unsupervised machine learning method named structural topic modelling to identify the causes of ship accidents. The study analysed 2,341 task errors manually collected from 441 reports issued by four government agencies covering a 45-year time span. The results show that the structure of causes of ship accidents remained essentially the same during this period. This highlights the social-material aspect of navigation technology, indicating that the use of advanced technology may not necessarily lead to safer navigation practices, and the interaction between the technology and human agency must be focused on in the bridge management context. Additionally, the computer-assisted textual data analysis highlights pilot-related factors, which might be rooted in the unsupervised and difficult-to-verify handover procedures between pilots and captains, thereby underlining the importance of appropriate piloting regulations.

This paper presents a methodology to support the decision-making process during the planning of ship operations. The methodology is designed with the aim of identifying and providing the operator with the best Estimated Time of Departure (ETD)–Estimated Time of Arrival (ETA) window of opportunity to execute the journey/operation between two predefined locations. To achieve this purpose, the International Maritime Organization (IMO) stability criteria are exploited in the process to formulate an operational safety criterion based on fuzzy reasoning as a function of the METeorological and OCeanographic (METOC) and sailing conditions. This allows for the analysis of the set of Pareto routes computed by a weather routing systems relying on a multi-objective set-up. The proposed methodology is tested in an operational scenario in the Mediterranean Sea.

Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.

Non-compliance with or misinterpretation of the International Regulations for Preventing Collisions at Sea (COLREGs) when assessing vessel encounters, and the lack of good communication between the vessels involved in a critical situation, are primary contributing factors in collisions. Vessels engaged in an encounter should be aware that they are part of the same scenario and situation, which can become critical. Sharing and contrasting their information about the encounter would help those responsible to take manoeuvring decisions in a consistent way. There are situations whose evaluation by the respective officers in charge of the navigational watch may diverge and lead to disagreements on the actions to be taken. If there is no proper inter-ship communication, a collision may result. This paper presents a proposal for safety communication implemented in a programmable system using common equipment (automatic identification system), and applies it to a case study of one such special situation, showing how it could help to reduce the risk of collision.

The ongoing development of diverse maritime autonomous vehicles for varied ocean activities—ranging from scientific research, security surveillance, transportation of goods, military purposes and commission of crimes—is prompting greater consideration of how existing legal frameworks accommodate these vehicles. This article brings together the core legal issues, as well as current developments in relation to commercial shipping, the law of naval warfare, and maritime security. This article captures how these issues are now being addressed and what other legal questions will likely emerge as the newest technology impacts on one of the oldest bodies of international law.

Safety in ice-covered polar waters can be optimised via the choice of a ship's route. This is of utmost importance for conventional as well as autonomous ships. However, the current state of the art in e-Navigation tools has left two open questions. First, what essential information are these tools still missing, and second, how they are seen by sea captains. In order to address these questions, we organised an ice navigation workshop to systematically collect routing justifications given by and waypoints planned by experienced sea captains that are particularly seasoned in ice navigation. Here, we report the outcome of that workshop. Our key findings include the reasoning and the commentary of the participants in looking for a better and safer route. These comments shed light upon both the official and unofficial code of conduct in open waters and boil down into a list of additional prerequisite information if further steps towards system autonomy are sought. Finally, the expert-planned waypoints are to be published alongside this paper to act as a benchmark for future maritime studies.

Ship collision risk is an important aspect of ship navigation safety. A systematic method to assess collision risk by monitoring parameter states continually is necessary and has proven effective. Another important factor in risk assessment is ship size, but the effect of the size of ship pairs has not been considered properly in many previous studies. This research utilises a systematic perspective to study collision risk of near-misses in ship-ship encounters. This fills a secondary research gap where previous risk assessments only investigated near-misses from the perspective of a single vessel. Following this proposed approach, ship pair encounter states can be continually tracked. Ultimately, a method of improved Vessel Collision Risk Operator (VCRO) to merge risk assessments of both ships is proposed through integration of near-miss collision risks in a systematic way, which overcomes the disadvantages of prior VCROs that only consider the maximum value, from which it is difficult to track and judge the risk trend. Utilising a case study, the effectiveness of the proposed method is validated through analysis of ship encounters, with ships of different sizes in the Baltic Sea.