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.

Ship density is widely accepted as a basic and major indicator to reflect the marine traffic situation, but it has some limitations in representing the compactness and complexity of ship traffic. To overcoming these limitations, the paper proposes a novel ship density model based on the radial distribution function in molecular dynamics. The proposed model can identify the density and compactness of traffic around each ship and then map the ship density from a microscopic perspective. In addition, the proposed model can identify the global density and the complexity of ship traffic to some extent in the macroscopic perspective. Utilising case studies, the effectiveness of the proposed model is validated through the analysis of ship density in several regions in the Bohai Strait area. The proposed model is developed to help marine surveillance operators gain a better understanding of the traffic situation and to assist them in their work, eventually contributing to navigational safety.

This paper proposes a framework to assess the influence of Offshore Wind Farms (OWFs) on maritime traffic flow based on raw Automatic Identification System (AIS) data collected before and after the installation of the offshore wind turbines. The framework includes modules for data acquisition, data filtering and statistical analysis. The statistical analysis characterises the influence of an OWF on maritime traffic in terms of minimum passing distances and lateral distribution of the ship trajectories near the OWF. The framework is applied to a specific route for which AIS data is available before and after an OWF installation. The impacts of the OWF on marine traffic are diverse and depend on the ship type categories. This paper quantitatively characterises an OWF's influence on a specific route that is probabilistically modelled, which is important for further studies on OWF site selection and maritime traffic risk assessment and management.

The absence of a regional, open water vessel collision risk assessment system endangers maritime traffic and hampers safety management. Most recent studies have analysed the risk of collision for a pair of vessels and propose micro-level risk models. This study proposes a new method that combines density complexity and a multi-vessel collision risk operator for assessing regional vessel collision risk. This regional model considers spatial and temporal features of vessel trajectory in an open water area and assesses multi-vessel near-miss collision risk through danger probabilities and possible consequences of collision risks via four types of possible relative striking positions. Finally, the clustering method of multi-vessel encountering risk, based on the proposed model, is used to identify high-risk collision areas, which allow reliable and accurate analysis to aid implementation of safety measures.

The quality of Automatic Identification System (AIS) data is of fundamental importance for maritime situational awareness and navigation risk assessment. To improve operational efficiency, a deep learning method based on Bi-directional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs) is proposed and applied in AIS trajectory data restoration. Case studies have been conducted in two distinct reaches of the Yangtze River and the capability of the proposed method has been evaluated. Comparisons have been made between the BLSTM-RNNs-based method and the linear method and classic Artificial Neural Networks. Satisfactory results have been obtained by all methods in straight waterways while the BLSTM-RNNs-based method is superior in meandering waterways. Owing to the bi-directional prediction nature of the proposed method, ship trajectory restoration is favourable for complicated geometry and multiple missing points cases. The residual error of the proposed model is computed through Euclidean distance which decreases to an order of 10 m. It is considered that the present study could provide an alternative method for improving AIS data quality, thus ensuring its completeness and reliability.

When ships approach each other, they should keep a minimum area around them clear of other vessels in order to remain safe. The geometrical shape of this area has been studied since the early 1970s and is defined as the ship domain. The progress in computer capacity since then and the introduction of the Automatic Identification System (AIS) provides the potential to further investigate the size and the governing factors of the domain. This investigation revisits and proposes a method using data based on 600,000 ship encounters at 36 locations. It is concluded that the ship domain has the shape of an ellipse with half axis radii of 0.9 and 0.45 nautical miles. However, there are two factors that greatly affect the ship domain: how large the area is that is used to gather vessel intersections and whether they are constrained by water depth. In contradiction to some previous research, it is found that the ship domain is unrelated to the length of the ship.

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to generate spatio-temporal matrices and spatio-temporal tensors (i.e., multidimensional arrays). We then imposed a sparse bilinear decomposition on the matrices and a sparse multi-linear decomposition on the tensors. Experimental results on a real-world dataset demonstrated the effectiveness of this methodology, with which we show the existence of connection among regions, time, and vessel attributes.

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.

This paper studies the risk of ship collision off the coast of Portugal based on Automatic Identification System (AIS) data, which is recorded and maintained by the Portuguese coastal Vessel Traffic Service (VTS) control centre (CCTMC). Computer programs for decoding, visualization and analysis of the AIS data have been developed. From analysis of the AIS data available, maritime traffic off the coast of Portugal is characterized and a statistical analysis of traffic in the Traffic Separation Schemes is provided. An algorithm has been developed to assess the risk profile and the relative importance of routes associated with ports. A method is proposed to calculate the collision risk from the assessment of the number of collision candidates by estimating future distances between ships based on their previous positions, courses and speeds, and comparing those distances with a defined collision diameter. Values of causation probability suggested in several studies are used to calculate the expected number of collisions in the period of time under investigation based on the number of collision candidates. The results of this study are then compared with the number of collisions that have occurred between 1997–2006, registered and maintained by the Portuguese Maritime Authority.